code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, c... | 640 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class a ( tf.keras.layers.Layer ):
def __init__( self : Dict , lowercase_ : int , lowercase_ : List[str] , lowercase_ : int , lowercase_ ... | 640 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 1 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 1 |
'''simple docstring'''
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
a : str = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCa... | 640 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 1 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 640 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 1 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a : int = logging.get_logger(__name__)
a : Optional[int... | 640 |
'''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 acceler... | 640 | 1 |
'''simple docstring'''
a : Union[str, Any] = 256
# Modulus to hash a string
a : List[str] = 100_0003
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = len(__UpperC... | 640 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : Optional[int] = ... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError('''String lengths must match!''' )
snake_case_ = ... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 1 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def __magic_name__ ( __UpperCAmelCase="ro", __UpperCAmelCase="en", __UpperCAmelCase="wmt16", __UpperCAmelCase=None ) -> None:
'''simple docstring'''
try:
import datasets
exc... | 640 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise Optio... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 1 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
... | 640 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 1 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __magic_name__ ( __UpperCAmelCase = "", ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''', '''''' ).lower() ).values() ) < 2
def ... | 640 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstri... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
snake_case_ = set()
snake_case_ = 0
snake_case_ = n + 1 # maximum limit
for a in range(2, __UpperCAmelCase ):
... | 640 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
return [ord(__UpperCAmelCase ) - 96 for elem in plain]
def __magic_name__ ( __UpperCAmelCase ) -> st... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCas... | 640 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = " " ) -> list:
'''simple docstring'''
snake_case_ = []
snake_case_ = 0
for index, char in enumerate(__UpperCAmelCase ):
if char == separator:
... | 640 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 1 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
a : str = re.compile(r'([A-Z]+)([A-Z][a-z])')
a : Optional[int] = re.compile(r'([a-z\d])([A-Z])')
a : Any = re.compile(r'(?<!_)_(?!_)')
a : List[Any] =... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 1 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
a : Optional[Any] = HfApi()
a : List[str] = {}
# fmt: off
a : str = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6... | 640 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 1 |
'''simple docstring'''
import math
class a :
def __init__( self : int , lowercase_ : List[str]=0 ): # a graph with Node 0,1,...,N-1
snake_case_ = n
snake_case_ = [
[math.inf for j in range(0 , lowerc... | 640 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list:
'''simple docstring'''
if len(__UpperCAmelCase ) <= 1:
return lst
snake_case_ = 1
while i < len(__UpperCAmelCase ):
if lst[i - 1] <= lst[i]:
... | 640 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, 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,
... | 640 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 1 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a : Dict = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docs... | 640 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 |
'''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 acceler... | 640 | 1 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
a : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class a ... | 640 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( _lowerCamelCase ):
snake_case_ = ["image_processor", "tokenizer"]
snake_case_ = "ChineseCLIPImageProcessor"
... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 200 ) -> int:
'''simple docstring'''
snake_case_ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case_ = [0] * (pence + 1)
snake_case_ = 1 # base case: 1 way to make 0 pen... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def __magic_name__ ( __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ ,snake_case_ = img.shape[0], img.shape[1]
# converting each pixel's color to its neg... | 640 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 1 |
'''simple docstring'''
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
fro... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 1 |
'''simple docstring'''
# 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.... | 640 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __magic_name__ ( __UpperCAmelCase ) -... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Any = {
'andreasm... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unittest.TestCas... | 640 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizer... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 1 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def ... | 640 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...te... | 640 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> list:
'''simple docstring'''
if len(__UpperCAmelCase ) != 2 or len(a[0] ) != 2 or len(__UpperCAmelCase ) != 2 or len(b[0] ... | 640 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 1 |
'''simple docstring'''
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 ... | 640 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 1 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torc... | 640 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 1 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
f... | 640 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 640 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
a : int = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle... | 640 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase,... | 640 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForCondition... | 640 |
'''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 acceler... | 640 | 1 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 ==... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( ) -> int:
'''simple docstring'''
snake_case_ = 0
for i in range(1, 1001 ):
total += i**i
return str(__UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
a : Union[str, Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
a : Any = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __magic_name__ ( __UpperCAmelCase ... | 640 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...m... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : List[str] = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import ... | 640 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 1 |
'''simple docstring'''
import argparse
import struct
import unittest
class a :
def __init__( self : Dict , lowercase_ : bytes ):
snake_case_ = data
# Initialize hash values
snake_case_ = [
0X6_A_0_9... | 640 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 1 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
cl... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 1 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None ) -> None:
'''simple docstring'''
if start is None:
snake_case_ = 0
if end is None:
sna... | 640 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
a : List[Any] = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
a : int = BASE_URL + '... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : int = logging.get_logger(__name__)
a : List[str] = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json'... | 640 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
a : Dict = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : Dict , *lowercase_ :... | 640 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvisio... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
def __init__( self : int , lowercase_ : Collection[float] | None = None ):
if components is ... | 640 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
def update_area_of_max_square(__UpperCAmelCase, __UpperCAmelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
... | 640 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
a : Union[str, Any] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : str , *low... | 640 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
snake_case_ = int(__UpperCAmelCase )
snake_case_ ... | 640 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 1 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
a : int = namedtuple(
'_TestCommandArgs',... | 640 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 1 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.model... | 640 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a : List[str] = 1.0_54_57_18_17E-34 # unit of ℏ : J * s
a : Tuple = 3E8 # unit of c : m * s^... | 640 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["flax", "transformers"]
def __init__( self : str , *lowercase_ : List[Any] , **lowercase_ : int ):
... | 640 |
'''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 acceler... | 640 | 1 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class a ( _lowerCamelCase ):
def __init__( self : Any , lowercase_ : Optional[Any]="" , lowercase_ : Any="train" ):
assert ... | 640 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class a ( _lowerCamelCase ):
def A_ ( self : int , lowercase_ : Optional[int]=None , lowercase_ : Union[str, Any]=None , lowercase_ : ... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
return x + 2
class a ( unittest.... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( _lowerCamelCase ):
@staticmethod
@abstractmethod
def A_ ( lowercase_ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def ... | 640 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 1 |
'''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 acceler... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 1 |
'''simple docstring'''
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
if (
not isinstance(__UpperCAmelCase, (int, float) )
or power_factor < -1
or power_factor > 1
):
r... | 640 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 1 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils... | 640 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 1 |
'''simple docstring'''
import math
def __magic_name__ ( __UpperCAmelCase ) -> 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, 0, 1, all... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 1 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 1 |
'''simple docstring'''
import numpy as np
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = int(np.ceil((x_end - xa) / h ) )
snake_case... | 640 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 1 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a ( unittest.TestCase ):
def A_ ( self : Any ):
... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case_ = sorted(stri... | 640 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 1 |
'''simple docstring'''
import math
import os
import sys
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = ''''''
try:
with open(__UpperCAmelCase, '''rb''' ) as binary_file:
snake_case_ ... | 640 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class a :
def __init__( self : List[Any] ):
snake_case_ = []
def A_ ( self : List[Any] , lowercase_ : List[str] ):
return self.node_po... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class a ( _lowerCamelCase ):
snake_case_ = 42
snake_case_ = 42
def __magic_name__ ( __UpperCAmelCase ) -> list[str]:
'''simple docs... | 640 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.j... | 640 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 1 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Lis... | 640 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumen... | 640 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : Any = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class a ( _low... | 640 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : List[str] = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
... | 640 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a : int = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a : Tuple = typing.Union[np.floataa, int, float] # noqa: U... | 640 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
... | 640 |
'''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 acceler... | 640 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from u... | 640 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class a :
def __init__( self : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = str(id_ )
snake_case_ = None
snake_... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a : Optional[int] = 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_copies # no... | 640 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Optional[Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProc... | 640 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class a :
def __init__( self : Any , lowercase_ : int = 6 ):
snake_case_ = None
snake_case_ = None
self.create_linked_list(lowerca... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 1 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 1 |
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