code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , lowerCamelCase ):
_snake_case = value
_snake_case = None
_snak... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Dict = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2... | 672 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__magic_n... | 672 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 672 | 1 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 672 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
import requests
__magic_name__ : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category cl... | 672 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCon... | 672 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Dict = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2... | 672 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = 2
_snake_case = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(SCREAMING_SNA... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 | 1 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_si... | 672 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( SCREAMING_SNAKE_CASE__ , S... | 672 | 1 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 10_00 ):
'''simple docstring'''
_snake_case = 1
_snake_case = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE__ , digit + 1 ):
_snak... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_availab... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avai... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE__ , 2 ) - a
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''s... | 672 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : Optional[int] = {
"""microsoft/git-base""": """http... | 672 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils imp... | 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinsta... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokeniz... | 672 |
'''simple docstring'''
__magic_name__ : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_s... | 672 | 1 |
'''simple docstring'''
import re
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstri... | 672 |
'''simple docstring'''
from torch import nn
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise Value... | 672 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__magic_name__ : int = logging.get_lo... | 672 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__magic_name__ : Tuple = 0
__magic_name__ : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : int = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if... | 672 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCamelCase( lowerCamelCase ):
rai... | 672 |
'''simple docstring'''
import string
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = ""
for i in sequence:
_snake_case = ord(SCREAMING_SNAKE_CASE__ )
if 65 <= extract <= 90:
output += chr(1_55 - extract )... | 672 | 1 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__magic_name__ : Optional[Any] = logging.getLogger()
@unittest.skip('... | 672 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return vector * sigmoid(1.702 *... | 672 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 672 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 672 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Any = logging.get_logger(__name__)
__magic_name__ : int = {
"""facebook/encodec_24khz""": """https:/... | 672 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
... | 672 | 1 |
'''simple docstring'''
from collections import deque
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = len(SCREAMING_SNAKE_CASE__ )
_snake_case = deque()
_snake_case = [False for _ in range(SCREAMING_SNAKE_CASE__ ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ : Optional[int] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 672 | 1 |
'''simple docstring'''
from math import isqrt, loga
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2... | 672 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_ut... | 672 | 1 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self ):
_snake_case = 0
_snake_case = 0
_snake_case = {}
def UpperCamelCase( self , lowerCamelCase ):
if ve... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 672 | 1 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__magic_name__ : Any = logging.get_logger(__name__)
__magic_name__ : Optional[... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Dict = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2... | 672 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 672 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 672 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__magic_name__ : List[str] = ... | 672 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.... | 672 | 1 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 10_00 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 672 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCon... | 672 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , lowerCamelCase ):
_snake_case = data
_snake_case = ... | 672 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import to... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_model... | 672 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( SCREAMING_SNAKE_CASE__ , S... | 672 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
UpperCAmelCase__ : Optional[Union[str, Path]] = None
UpperCAmel... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_availab... | 672 | 1 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def UpperCamelCase( self , lowerCamelCase ):
raise... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE__ , 2 ) - a
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''s... | 672 | 1 |
'''simple docstring'''
import math
def snake_case_ ( ):
'''simple docstring'''
_snake_case = input("Enter message: " )
_snake_case = int(input(f'''Enter key [2-{len(SCREAMING_SNAKE_CASE__ ) - 1}]: ''' ) )
_snake_case = input("Encryption... | 672 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : Optional[int] = {
"""microsoft/git-base""": """http... | 672 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__magic_name__ : int = logging.get_logger(__name__)
__magic_name__ : int = {""... | 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinsta... | 672 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require... | 672 |
'''simple docstring'''
__magic_name__ : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_s... | 672 | 1 |
'''simple docstring'''
from collections import deque
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
_snake_case = process_name # process name
_snake_case = ... | 672 |
'''simple docstring'''
from torch import nn
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise Value... | 672 | 1 |
'''simple docstring'''
__magic_name__ : Union[str, Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__magic_name__ : Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__magic_name__ : List[str] = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
... | 672 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__magic_name__ : Tuple = 0
__magic_name__ : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 672 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, to... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : int = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if... | 672 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpo... | 672 |
'''simple docstring'''
import string
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = ""
for i in sequence:
_snake_case = ord(SCREAMING_SNAKE_CASE__ )
if 65 <= extract <= 90:
output += chr(1_55 - extract )... | 672 | 1 |
'''simple docstring'''
import pprint
import requests
__magic_name__ : Tuple = """https://zenquotes.io/api"""
def snake_case_ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def snake_case_ ( ):
'''simple docs... | 672 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return vector * sigmoid(1.702 *... | 672 | 1 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
fro... | 672 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 672 | 1 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def snake_case_ ( )... | 672 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
... | 672 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__magic_name__ : List[str] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
'''simple docstring'''
def ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ : Optional[int] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 672 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
... | 672 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_ut... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestC... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 672 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
'''simple docstring'''
def __init__( ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Dict = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2... | 672 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusi... | 672 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 672 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.... | 672 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.... | 672 | 1 |
'''simple docstring'''
__magic_name__ : List[str] = tuple[float, float, float]
__magic_name__ : Union[str, Any] = tuple[float, float, float]
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case ... | 672 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCon... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ... | 672 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.trans... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 672 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( SCREAMING_SNAKE_CASE__ , S... | 672 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Im... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_availab... | 672 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
__magic_name__ : Tuple = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.js... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE__ , 2 ) - a
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''s... | 672 | 1 |
'''simple docstring'''
from itertools import product
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_to... | 672 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : Optional[int] = {
"""microsoft/git-base""": """http... | 672 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = math.inf , SCREAMING_SNAKE_CASE__ = -math.inf ... | 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinsta... | 672 | 1 |
'''simple docstring'''
import cmath
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = math.radians(SCREAMING_SNAKE_CASE__... | 672 |
'''simple docstring'''
__magic_name__ : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_s... | 672 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_conf... | 672 |
'''simple docstring'''
from torch import nn
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise Value... | 672 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 672 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__magic_name__ : Tuple = 0
__magic_name__ : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 672 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPM... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : int = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if... | 672 | 1 |
'''simple docstring'''
from math import pow
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ):
'''simple docstring'''
if current_sum ==... | 672 |
'''simple docstring'''
import string
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = ""
for i in sequence:
_snake_case = ord(SCREAMING_SNAKE_CASE__ )
if 65 <= extract <= 90:
output += chr(1_55 - extract )... | 672 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
'''simple docstring'''
def UpperCamelCase( self , lowerCamelCase ):
... | 672 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return vector * sigmoid(1.702 *... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since th... | 672 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 672 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__magic_name__ : int = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Sy... | 672 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
... | 672 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def snake_case_ ( ):
'''simple docstring'''
raise RuntimeError("CUDA out of memory."... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ : Optional[int] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 672 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Union[str, Any] = ... | 672 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_ut... | 672 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 672 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__magic_name__ : Dict = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value re... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Dict = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2... | 672 | 1 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 672 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : int = get_tests... | 672 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.... | 672 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return vector * sigmoid(1.702 *... | 672 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCon... | 672 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead."""
)
| 672 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 | 1 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__magic_name__ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--dump_... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 | 1 |
'''simple docstring'''
import qiskit
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
_snake_case ... | 672 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( SCREAMING_SNAKE_CASE__ , S... | 672 | 1 |
'''simple docstring'''
__magic_name__ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_availab... | 672 | 1 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__magic_name__ : List[Any] = logging.get_logger(__name... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE__ , 2 ) - a
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''s... | 672 | 1 |
'''simple docstring'''
import string
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = ""
for i in sequence:
_snake_case = ord(SCREAMING_SNAKE_CASE__ )
if 65 <= extract <= 90:
output += chr(1_55 - extract )... | 672 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : Optional[int] = {
"""microsoft/git-base""": """http... | 672 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.... | 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinsta... | 672 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 a... | 672 |
'''simple docstring'''
__magic_name__ : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_s... | 672 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 672 |
'''simple docstring'''
from torch import nn
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise Value... | 672 | 1 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case ... | 672 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__magic_name__ : Tuple = 0
__magic_name__ : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 672 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common imp... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : int = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if... | 672 | 1 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Any = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optiona... | 672 |
'''simple docstring'''
import string
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = ""
for i in sequence:
_snake_case = ord(SCREAMING_SNAKE_CASE__ )
if 65 <= extract <= 90:
output += chr(1_55 - extract )... | 672 | 1 |
'''simple docstring'''
__magic_name__ : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_s... | 672 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return vector * sigmoid(1.702 *... | 672 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The c... | 672 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = list(SCREAMING_SNAKE_CASE__ ... | 672 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase( ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ : Optional[int] = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_model... | 672 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_ut... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 672 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Dict = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2... | 672 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 672 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 672 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
loggin... | 672 |
'''simple docstring'''
import baseaa
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return baseaa.... | 672 | 1 |
'''simple docstring'''
from collections import defaultdict
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = first_str.lower().strip()
_snake_case = second_str.lower().strip()
# Remove whitespa... | 672 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationCon... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 672 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
'''simple d... | 672 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 | 1 |
'''simple docstring'''
import qiskit
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
_snake_case ... | 672 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( SCREAMING_SNAKE_CASE__ , S... | 672 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = x
_snake_case = y
for step in ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_availab... | 672 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_t... | 672 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE__ , 2 ) - a
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''s... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 672 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : Optional[int] = {
"""microsoft/git-base""": """http... | 672 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__magic_name__ : int = 500_000
__magic_name__ , __magic_name__ : int = os.path.split(__file__)
__magic_name__ : Optional[in... | 672 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if isinsta... | 672 | 1 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return base * power(SCREAMING_SNAKE_CASE__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the po... | 672 |
'''simple docstring'''
__magic_name__ : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_s... | 672 | 1 |
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