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 |
|---|---|---|---|---|
import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCAmelCase : Any = TypeVar("T")
__lowerCAmelCase : Dict = Union[List[T], Tuple[T, ...]]
__lowerCAmelCase : Optional[Any] = Union[T, List[T], Dict[str, T]]
__lowerCAmelCase ... | 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI... | 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
__A : Tuple = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def __lowerCAmelCase( _SCREAMING_... | 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_A = datasets.logging.get_logger(__name__)
_A = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poes... | 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)... | 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int ) -> int:
_lowerCamelCase = abs(__a )
_lowerCamelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowerCAmelCase_( lowercase_ : int ) -> int:
... | 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
from ...processing_utils import ProcessorMixin
class __A ( __lowercase ):
UpperCamelCase :str = "SpeechT5FeatureExtractor"
UpperCamelCase :Tuple = "SpeechT5Tokenizer"
def __init__(self , __magic_name__ , __magic_name__ ):
super().__init__(... | 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils im... | 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__lowercase ):
"""simple docstring"""
A__ : List[Any] = ["flax", "transformers"]
def __init__( self : Optional[Any] , *_snake_c... | 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, r... | 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
_A = os.path.join(args.tf... | 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase_ : List[Any] = TypeVar('''KEY''')
UpperCAmelCase_ : str = TypeVar('''VAL''')
@dataclass(frozen=__lowercase , slots=__... | 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
_A = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCamelCase__ ( __lowerCAmelCase : bytes ):
"""simple docstring"""
if not isinstance(__a , __a ):
lowerCAmelCase_ = F"""a bytes-like object is required, not '{da... | 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
'''simple docstring'''
import string
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase_ : int = ''''''
for symbol in message:
if symbol in string.... | 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 14 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
__A : Tuple = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and ... | 602 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None ):
_lowerCamelCase = data
_lowerCamelCase = previous
_lowerCamel... | 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _A (UpperCamelCase : Union[str, Any] , UpperCamelCase : List[Any] , UpperCamelCase ... | 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( __lowercase ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''si... | 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : list[list[float]] ):
"""simple docstring"""
__A= Decimal
# Check if the provided matrix has 2 rows and 2 columns
... | 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1_024 , __lowerCAmelCase=1_024... | 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
from __future__ import annotations
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(__a ) == 0:
return []
__A = min(__a ), max(__a )
__A = int(max_value - min_value ) + 1
__A = [[] for _ in range(__a )]
... | 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCamelCase( ... | 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffuser... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_A = logging.get_logger(__name__)
# TODO: upload to AWS
_A = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
),
}
class _lowerCAmel... | 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple... | 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Union[str, Any] = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}... | 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE : Tuple = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE : List[str] = numpy.array([0.5, 0.866_0254])
__SCREAMING... | 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __A ( unittest.TestCase ):
def _snake_case (self ):
lowerCame... | 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
from __future__ import annotations
import math
def a__ ( _UpperCamelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# Se... | 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
__lowercase : List[str] = int(__a )
if decimal in (0, 1): # Exit cases for the recursion
return str(__a )
__lowercase : List[Any] = divmod(__a , 2 )
re... | 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelera... | 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
import heapq
import sys
import numpy as np
__A : Tuple = tuple[int, int]
class lowerCamelCase:
'''simple docstring'''
def __init__( self ):
_A = []
_A = set()
def lowerCAmelCase... | 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
'''simple docstring'''
UpperCAmelCase_ : str = '''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase_ : str = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _UpperCamelCase (_lowerCamelCase : str )-> bool:
'''simple docstring'''
if not isinsta... | 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_logger(__name__)
_A = [
["attention", "attn"],
["encoder_attention", "encoder_attn"],
["q_lin", "q_proj"],
... | 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.... | 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 14 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase)
class _a ( __lowercase):
"""simple docstring"""
UpperCamelCase__ = ... | 602 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SC... | 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( _UpperCamelC... | 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
__A= 1, 1
__A= []
for i in range(1,n + 1 ):
__A= prev_numerator + 2 * prev_denominator
__A= prev_numerator + prev_denominator
if len(str(__a ) ) > len(st... | 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ft... | 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from di... | 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase (_lowerCamelCase : int | str )-> bool:
'''simple docstring'''
__snake_case = str(__a )
return n == n[::-1]
def _UpperCamelCase (_lowerCamelCase : int =... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDep... | 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
if not isinstance(__a , __a ):
raise TypeError("Undefined for non-integers" )
eli... | 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def lowercase ( ):
'''simple docstring'''
print('''Making key files...''' ... | 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : list[int] , lowercase_ : list[int] ) -> None:
_lowerCamelCase = len(__a )
print('''The following activities are selected:''' )
# The first activity is always selected
_lowerCamelCase = 0
... | 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def _A (UpperCamelCase : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives,... | 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transformer/small-base""": """https... | 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See ... | 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = {
'YituTech/conv... | 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__A : int = "Usage of script: script_name <size_of_canvas:int>"
__A : List[str] = [0] * 100 + [1] * 10
random.shuffle(choice)
... | 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_t... | 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from .... | 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_availab... | 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 14 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transf... | 602 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__SCREAMING_SNAKE_CASE : Tuple = '''src/diffusers'''
# Matches is_xxx_available()
__SCREAMING_SNAKE_CAS... | 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_lowercase = logging.get_logger(__name__)
class __A ( __lowercase ):
UpperCamelCase :List[Any] = CLIPConfig
... | 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
UpperCAmelCase__ = {
'''n_samples''': 6_4,
'''horizon''': 3_2,
'''num_inference_steps''': 2_0,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not... | 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> list:
__lowercase : Tuple = len(__a )
__lowercase : str = []
for i in range(len(__a ) - pat_len + 1 ):
__lowercase : Any = ... | 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_... | 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
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_channel_dim... | 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data i... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/res... | 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple=7 ) -> Any:
"""simple docstring"""
U... | 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
def lowercase ... | 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_perceiver''': ['''PERCEI... | 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise Op... | 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( _UpperCamelCase : Tuple ,_Up... | 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
__A= 2**power
__A= 0
while n:
__A= r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : str = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main... | 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
def UpperCAmelCase ( a_ , a_ , a_ ) -> Optional[Any]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__a , n - 1 , __a ) * a) % mod
else:
__A = binary_exponentiation(_... | 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCamelCase( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self )... | 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _UpperCamelCase (_lowerCamelCase : bytes , _lowerCamelCase : int )-> np.array:
'''simple docstring'''
__snake_case ... | 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
lowerCAmelCase_ = BeautifulSoup(requests.get(__a ).text , "html.parser" )
... | 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch... | 71 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 14 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available()... | 602 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import versi... | 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _A (UpperCamelCase : Namespace ) ->Dict:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_du... | 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Dict=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] ... | 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ = lo... | 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowercase : Optional[Any] = sorted(string.lower() )
return len(__a ... | 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
__A = [int(__a ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(__a ) == 4 and all(0 <= int(__a ) <= 2_5_4 for octet in octets )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE ... | 55 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A : str = logging.get_l... | 27 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase_ : str = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
t... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"processing_cl... | 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__lowercase)
class _snake_case (__lowercase):
__A : str =field(default="image-c... | 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
... | 602 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_m... | 661 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from... | 157 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSe... | 186 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> str:
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function''' )... | 509 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
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