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 Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE ( UpperCamelCase__ ):
"""simple docstring"""
lowerCamelCase : List[Any] =""
lowerC... | 651 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 21 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
A_ : Union[str, Any] =logging.getLogger(__name__)
A_ : Optional[An... | 650 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(_... | 21 | 0 |
import unittest
import numpy as np
import requests
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_available():... | 696 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_... | 21 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Tuple = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[Any] = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/... | 673 |
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.tex... | 21 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( UpperCamelCase__ ):
"""simple docstring"""
__lowerCamelCase = (IPNDMScheduler,)
__lowerCamelCase = (('num_inference_step... | 514 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 21 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCAmelCase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCAmelCase = [ord(letter) for letter in string.ascii_lowerc... | 666 |
import warnings
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
Upp... | 21 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class lowercase :
... | 403 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with... | 21 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ = 1_000 ):
lowerCamelCase_ = 2**power
lowerCamelCase_ = 0
while n:
lowerCamelCase_ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 29 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ... | 21 | 0 |
"""simple docstring"""
import argparse
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 acce... | 553 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
s... | 21 | 0 |
"""simple docstring"""
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ , lowercase__ ):
snake_case_ : int = name
snake_case_ : Optional[int] = val
def __str__(self ):
... | 480 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCA... | 21 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECK... | 108 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
... | 21 | 0 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
... | 651 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise I... | 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : Dict ={
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_... | 650 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ... | 21 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : Optional[Any] ={
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMT... | 696 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:... | 21 | 0 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
lowerCAmelCase_ : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowerCAme... | 673 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self ... | 21 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that th... | 514 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0... | 21 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase__ )
class snake_case__ ( UpperCamelCase__ ):
# `task` is not a ClassVar since we want it to ... | 666 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ... | 21 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : str = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Ve... | 403 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase ... | 21 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges_fi... | 29 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It take... | 21 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__SCREAMING_SNAKE_CASE = 50_00_00
__SCREAMING_SNAKE_CASE = os.path.split(__file__)
__SCREAMING_SNAKE_CASE = os.path.join(RESULTS_BASEPATH, '... | 553 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
... | 21 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 480 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_s... | 21 | 0 |
from __future__ import annotations
from fractions import Fraction
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> int:
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def _SCREAMING_SNAKE... | 108 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmel... | 21 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 651 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 21 | 0 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git w... | 650 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(_... | 21 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCAmelCase : int ="\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={W... | 696 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_... | 21 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [0] * len(lowerCAmelCase )
UpperCAmelCase = []
UpperCAmelCase = [1] * len(lowerCAmelCase )
for values... | 673 |
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.tex... | 21 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 514 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 21 | 0 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] = 2_0_0 ):
snake_case_ : Tuple = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
snake_case_ : Optional[int] = [0] * (pence + 1)
snake_case_ : int = 1 # base case: 1 way to make 0 pence
... | 666 |
import warnings
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
Upp... | 21 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 403 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with... | 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_to... | 29 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ... | 21 | 0 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def UpperCAmelCase ( a__=None , a__=No... | 553 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
s... | 21 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS... | 480 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCA... | 21 | 0 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a:... | 108 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
... | 21 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformer... | 651 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise I... | 21 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ( UpperCamelCase__ ... | 650 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ... | 21 | 0 |
import math
import tensorflow as tf
from packaging import version
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : str = tf.convert_to_tensor(lowercase__ )
__SCREAMING_SNAKE_CASE : List[str] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) ,... | 696 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:... | 21 | 0 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowerCAmelCase_ ... | 673 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self ... | 21 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplin... | 514 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0... | 21 | 0 |
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_encoder ... | 666 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ... | 21 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ):
SCREAMING_SNAKE_CASE = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
SCREAMING_SNAKE_CASE = n - k
# Calculate C(n,k)
... | 403 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase ... | 21 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __lowerCamelCase ( unittest.TestCase ):
def UpperCAmelCase__ ( self ):
debug_launcher(test_script.main )
... | 29 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It take... | 21 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCAmelCase ( a__ = 8 ):
'''simple docstring'''
lowerCAmelCase :Optional[Any] = ascii_letters + digit... | 553 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
... | 21 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusio... | 480 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_s... | 21 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Tuple = logging.get_logger(__name__)
__a: Tuple = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__... | 108 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmel... | 21 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def snake_case_ (__A : int , __A : Optional[Any] ) -> int:
__lowerCAmelCase : List[str] = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
__lowerCA... | 651 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 21 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : Tuple , snake_case : Dict = 0.0 , snake_case : int = 1.0 )-> Union[str, Any]:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __n... | 650 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(_... | 21 | 0 |
def _UpperCamelCase ( lowercase__ , lowercase__ ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_... | 21 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 673 |
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.tex... | 21 | 0 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCAmelCase__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("""""", """|""", """|"""),
datar... | 514 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 21 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenP... | 666 |
import warnings
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
Upp... | 21 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features ... | 403 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with... | 21 | 0 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowerCamelCase ( UpperCamelCase__ ):
def __init__( self ... | 29 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ... | 21 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCAmelCase ( a__ ):
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint ... | 553 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
s... | 21 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_a... | 480 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCA... | 21 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onn... | 108 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
... | 21 | 0 |
from typing import Any
def snake_case_ (__A : Union[str, Any] , __A : Tuple , __A : Optional[int] , __A : List[str] , __A : Tuple , ) -> List[Any]:
_validation(
__A , __A , __A , __A , __A , )
# Creates data structures and fill initial ste... | 651 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise I... | 21 | 0 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 650 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ... | 21 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowerCAmelCase : Optional[Any] =datasets.utils.logging.get_logger(__name__)
@dataclass
class _lowerc... | 696 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:... | 21 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = sorted(numsa + numsa )
UpperCAmelCase = divmod(len(lowerCAmelCase ... | 673 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A ( tf.keras.layers.Layer ):
def __init__( self ... | 21 | 0 |
lowerCAmelCase__ = range(2, 2_0 + 1)
lowerCAmelCase__ = [1_0**k for k in range(ks[-1] + 1)]
lowerCAmelCase__ = {}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: int , SCRE... | 514 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : str =tf.convert_to_tensor(lowerCamelCase )
__magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0... | 21 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"facebook/xlm-roberta-xl": "h... | 666 |
from collections.abc import Sequence
def lowerCAmelCase_ ( lowerCamelCase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__magic_name__ : str =nums[0]
for i in range(1 , len(lowerCamelCase ) ... | 21 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
... | 403 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase ... | 21 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase ( lowerCAmelCase__="ro" ,lowerCAmelCase__="en" ,lowerCAmelCase__="wmt16" ,lowerCAmelCase__=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''r... | 29 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It take... | 21 | 0 |
"""simple docstring"""
def UpperCAmelCase ( a__ , a__ ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowerCAmelCase :Tuple = str(bin(a__ ) )
binary_number +... | 553 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
... | 21 | 0 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Tuple ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE__ ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"... | 480 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_s... | 21 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class SCREAMING_S... | 108 |
UpperCAmelCase_ : Tuple = 0 # The first color of the flag.
UpperCAmelCase_ : Any = 1 # The second color of the flag.
UpperCAmelCase_ : str = 2 # The third color of the flag.
UpperCAmelCase_ : Tuple = (red, white, blue)
def lowerCAmel... | 21 | 0 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_a... | 651 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 21 | 0 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( snake_case : Tuple , snake_case : List[Any] = None , snake_case : Optional[int] = None )-> int:
if start is None:
_lowerCamelCase = 0
if end is None... | 650 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase_ : Dict = logging.get_logger(_... | 21 | 0 |
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = mf_knapsack(i - 1 , lo... | 696 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_... | 21 | 0 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImage... | 673 |
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.tex... | 21 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class a__ ( UpperCamelCase__... | 514 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 21 | 0 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Any = None ):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
snake_case_ : str = nums[0]
for i in range(1 , len(l... | 666 |
import warnings
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
Upp... | 21 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_... | 403 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with... | 21 | 0 |
"""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 lowercase ( ):
... | 29 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ... | 21 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
... | 553 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase_ ( lowerCamelCase ):
def decorator(lowerCamelCase ):
__magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] )
handle += [key]
s... | 21 | 0 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly... | 480 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : Dict = 2048
UpperCAmelCase_ : int = 4096
UpperCAmelCase_ : Any = 42
UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
UpperCA... | 21 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTok... | 108 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
... | 21 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTest... | 651 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise I... | 21 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def SCREAMING_SNAKE_CASE_ ( snake_case : Tuple )-> Optional[Any]:
def decorator(snake_case : List[str] ):
_lowerCamelCase = getattr(snake_case , 'handle_... | 650 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( lowerCamelCase ... | 21 | 0 |
import argparse
import copy
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Tuple = {}
with open(lowercase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__SCREAMING_SNAKE_CASE : Optional... | 696 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:... | 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[Any] = logging.get_logge... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
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_... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDatase... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ (UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray , UpperCamelCase : float = 1e-12 , UpperCamelCase : int = 100 , ):
'''simple docstring'''
assert np.shape... | 22 |
'''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,
r... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def snake_case_ (UpperCamelCase : Tuple ):
'''simple docstring'''
_a = os.path.join(... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_snake_case : str = ['small', 'medium', 'large']
_snake_case : Any = 'lm_head.decoder.weight'
_snake_case : int = 'lm_head.weight'
def snake_case_... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A ( _a ):
lowercase_ = 'MCTCTFeatureExtractor'
lowercase_ = 'AutoTokenizer'
... | 22 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[list[int]] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : set ):
'''simple docstring'''
_a , _a = len(UpperCamelCase ), len(grid[0] )... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Tuple = logging.get_logger(__name__)
_snake_case : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900... | 22 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_im... | 22 | 1 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 22 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class A :
def __init__( self : Dict ) -> Any:
"""simple docstring"""
_a = []
def __lowerCAmelCase ( ... | 22 |
'''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
... | 22 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Tuple = logging.get_logger(__name__)
_snake_case : str = {
'BAAI/AltCLIP': 'https://huggingface.co/BA... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.ima... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : List[Any] = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig']... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A ( _a ):
@staticmethod
@abstractmethod
def __lowerCAmelCase ( lowerCAmelCase_ : ArgumentParser ) -> Optional[int]:
... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecode... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : List[str] ):
'''simple docstring'''
_a = []
_a = []
_a = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,... | 22 | 1 |
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