code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi... | 719 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 0 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"""vocab_file""": """vocab.json"""}
lowerCAmelCase__ = {
"""vocab_file""": {
"""mgp-... | 720 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 626 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( snake_case ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCamelCase ( lowercase ) -> Any:
'''simple docstring'''
raise NotImplementedError(... | 721 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 0 |
from math import isclose, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float ) -> tuple[float, float, float]:
'''simple docstring'''
A__ = point_y / 4 / point_x
A__ ... | 700 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 0 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 701 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
with offline(OfflineSimulationMo... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
A__ = set()
A__ = 0
A__ = n + 1 # maximum limit
for a in range(2 , SCREAMING_SNAKE_CASE_ ):
for b in range(2 , SC... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root ... | 704 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 0 |
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,
MobileViTVaForImageClassification,
MobileViTVaForSem... | 705 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,... | 706 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
Reques... | 707 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 0 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: float ) -> float:
'''simple docstring'''
if successes > trials:
raise ValueError("successes must be low... | 708 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 0 |
'''simple docstring'''
from math import pi
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(9_0, 1_... | 709 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] ) -> str:
'''simple docstring'''
A__ = ""
for word_or_phrase in separated:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_... | 710 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig"""],
"""processing_vision_... | 711 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dime... | 712 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 0 |
'''simple docstring'''
lowerCAmelCase__ = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
""... | 713 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 0 |
import gc
import threading
import time
import psutil
import torch
class a__ :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = psutil.Process()
A__ = False
def UpperCamelCase ( ... | 714 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 0 |
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> None:
'''simple docstring'''
A__ = set_counts
A__ = max(lowercase )
A__ = len(lowercase )
A__ = [1] * num_sets... | 715 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 0 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 717 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
lowerCAmelCase__ = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
lowerCAmelCase__ = requests.get(url, heade... | 719 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: int ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbo... | 720 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 626 | 0 |
# Copyright 2023 The HuggingFace 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 applica... | 721 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONA... | 700 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 701 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
lowerCAmelCase__ = """1"""
lowerCAmelCase__ = """0"""
lowerCAmelCase__ = """1"""
lowerCAmelCase__ = ort.SessionOptions()
lowerCAmelCase__ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
lowerCAm... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
from __future__ import annotations
from collections import deque
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> List[str]:
'''simple docstring'''
A__ = []
self.adlist.append(
{"value": "", "next_... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 704 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditi... | 705 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return lst
A__ = 1
while i < len(SCREAMING_SNAKE_CASE_ ):
if lst[i - 1] <= lst[i]:
i += 1
... | 706 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 0 |
import numpy as np
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: np.ndarray , SCREAMING_SNAKE_CASE_: np.ndarray , SCREAMING_SNAKE_CASE_: float = 1e-12 , SCREAMING_SNAKE_CASE_: int = 1_0_0 , ) -> tuple[float, np.ndarray]:
'''simple... | 707 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig,... | 708 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter image url: """).strip()
print(f"""Downloading image from {url} ...""")
lowerCAmelCase__ = BeautifulSoup(requests.get(url).content, """html.par... | 709 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dat... | 710 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 711 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ... | 712 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 0 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 713 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """V... | 714 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
f... | 715 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCAmelCase__ ( SCREAMING_SNAKE_CAS... | 717 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 0 |
from __future__ import annotations
from collections.abc import Callable
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE_: int | float , SCREAMING_SNAKE_CASE_: int | float , SCREAMING_SNAKE_CASE_: int = 1... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a__ ( snake_case ):
"""simple docstri... | 719 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 626 | 0 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import... | 721 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 0 |
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,
resize,
to_channel_dimension_format,
... | 700 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
A__ = olid.strip().strip("/" ) # Remove leadin... | 701 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple d... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 704 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 0 |
from math import factorial
lowerCAmelCase__ = {str(digit): factorial(digit) for digit in range(1_0)}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
... | 705 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 0 |
from math import sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> bool:
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
A__ ... | 706 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,... | 707 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormer... | 708 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 0 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 709 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: List[str] ) -> str:
'''simple docstring'''
A__ = ""
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase__ ( SCREA... | 710 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCAmelCase__ = """src/transformers"""
lowerCAmelCase__ =... | 711 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list[int]:
'''simple docstring'''
if num <= 0:
A__ = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(SCREAMING_SNA... | 712 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCAmelCase__ ( SCREAM... | 713 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 0 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 714 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> str:
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the functio... | 715 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 0 |
class a__ ( snake_case ):
pass
class a__ ( snake_case ):
pass
class a__ :
def __init__( self ) -> Optional[Any]:
'''simple docstring'''
A__ = [
[],
[],
[],
]
def UpperCa... | 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list , SCREAMING_SNAKE_CASE_: int | None = None , SCREAMING_SNAKE_CASE_: int | None = None ) -> None:
'''simple docstring'''
if start is None:
A__ = ... | 717 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: ndarray ) -> float:
'''simple docstring'''
return np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def lowerCAmelCase__ ( ) -> List[str]:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_... | 719 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 720 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 626 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # ... | 721 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __lowercase... | 627 |
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 transform... | 627 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->Dict:
UpperCAmelCase = int(lowerCAmelCase_ )
assert noofclusters < len(lowerCAmelCase_ )
# Find out the dimensionality
... | 627 |
from math import sqrt
def _UpperCamelCase ( lowerCAmelCase_ = 1_0_0_0_0_0_0 ) ->int:
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , ... | 627 | 1 |
import os
def _UpperCamelCase ( lowerCAmelCase_ = "input.txt" ) ->int:
with open(os.path.join(os.path.dirname(lowerCAmelCase_ ) , lowerCAmelCase_ ) ) as input_file:
UpperCAmelCase = [
[int(lowerCAmelCase_ ) for element in line.split(""",""" )]
... | 627 |
from __future__ import annotations
def _UpperCamelCase ( lowerCAmelCase_ ) ->None:
create_state_space_tree(lowerCAmelCase_ , [] , 0 , [0 for i in range(len(lowerCAmelCase_ ) )] )
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAm... | 627 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowercase ( __snake_case , __snake_case ):
@register_to_config
def... | 627 |
import numpy
class __lowercase :
def __init__( self : Union[str, Any] , __lowerCamelCase : numpy.ndarray , __lowerCamelCase : numpy.ndarray ) -> None:
"""simple docstring"""
... | 627 | 1 |
from itertools import count
def _UpperCamelCase ( lowerCAmelCase_ = 5_0 ) ->int:
UpperCAmelCase = [1] * min_block_length
for n in count(lowerCAmelCase_ ):
fill_count_functions.append(1 )
for block_length in range(lowerCAmelCase_ , n + 1 ):
... | 627 |
import argparse
__a = """docs/source/_static/js/custom.js"""
def _UpperCamelCase ( lowerCAmelCase_ ) ->Optional[Any]:
with open(lowerCAmelCase_ , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCAmelCase = f.readlines()
UpperCAmelCase ... | 627 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"""facebook/levit-128S""": """ht... | 627 |
import math
class __lowercase :
def _lowercase ( self : Union[str, Any] , __lowerCamelCase : list[list[float]] , __lowerCamelCase : list[int] ) -> int:
"""simple docstring"""
... | 627 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCamelCase ( lowerCAmelCase_ ) ->float:
if num <= 0:
raise ValueError("""math domain error""" )
return quad(lowerCAmelCase_ , 0 , lowerCAmelCase_ , args=(lowerCAmelCase_) )[0]
... | 627 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
UpperCAmelCase = {}
UpperCAmelCase = tok... | 627 | 1 |
from numpy import exp, pi, sqrt
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 1.0 ) ->int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 627 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__a = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksach... | 627 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=1_0_0_0 ) ->Dict:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCAmelCase = n - 1
UpperCAmelCase... | 627 |
import math
import qiskit
def _UpperCamelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 ) ->qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ ... | 627 | 1 |
import warnings
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 __lowercase ( __snake_case ... | 627 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class __lowercase ( __snake_case ):
UpperCamelCase = '''ct... | 627 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import loggi... | 627 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 627 | 1 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
UpperCAmelCase = sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ ) # Calculate the average
return sum(abs(x - average )... | 627 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDepen... | 627 | 1 |
import numpy
class __lowercase :
def __init__( self : Union[str, Any] , __lowerCamelCase : numpy.ndarray , __lowerCamelCase : numpy.ndarray ) -> None:
"""simple docstring"""
... | 627 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__a = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 627 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( __snake_case ):
UpperCamelCase = ['''image_processor''', '''tokenizer''']
UpperCamelCase = '''CLIPImageProcessor''... | 627 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__a = logging.get_logger(__name__)
def _UpperCamelCase ( lowerCAmelCase_ , ... | 627 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"""roberta-base""": """https://huggingface.co/roberta-base/resolv... | 627 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->str | Literal[False]:
UpperCAmelCase = list(lowerCAmelCase_ )
UpperCAmelCase = list(lowerCAmelCas... | 627 | 1 |
from __future__ import annotations
import math
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->float:
UpperCAmelCase = u
for i in range(1 , lowerCAmelCase_ ):
UpperCAmelCase = temp * (u - i)
return temp
def _UpperCamelCase ... | 627 |
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
from trans... | 627 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __lowercase :
def __init__( self : Dict ) -> str:
"""simple docstring"""
UpperCAmelCase = {}
... | 627 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subproc... | 627 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) ->Dict:
Upp... | 627 |
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 __lowercase ( unittest.TestCase ):
def _lowercase ( self : ... | 627 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowercase ( __snake_case ):
def __init__( self : ... | 627 |
from math import isqrt
def _UpperCamelCase ( lowerCAmelCase_ ) ->bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCAmelCase_ ) + 1 ) )
def _UpperCamelCase ( lowerCAmelCase_ = 1_0**6 ) ->int:
UpperCAmelCase = 0
UpperCAm... | 627 | 1 |
from __future__ import annotations
__a = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
... | 627 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class __lowercase ( __snake_case ):... | 627 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfi... | 627 |
__a = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def _UpperCamelCase ( ... | 627 | 1 |
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->int:
UpperCAmelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase = n - k
# Calculate C(n,k)
for i in range(lowerCAmelCase_ ):
... | 627 |
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->int:
return int((input_a, input_a).count(0 ) == 0 )
def _UpperCamelCase ( ) ->None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
... | 627 | 1 |
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->int:
return number | (1 << position)
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->int:
return number & ~(1 << position)
def _UpperCamelCase ( lowerCAmelCase_ , lowerCA... | 627 |
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 transform... | 627 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _UpperCamelCase ( lowerCAmelCase_ ) ->Optional[int]:
UpperCAmelCase = [
"""decoder.version""",
"""decoder.output_project... | 627 |
from math import sqrt
def _UpperCamelCase ( lowerCAmelCase_ = 1_0_0_0_0_0_0 ) ->int:
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , ... | 627 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_com... | 627 |
from __future__ import annotations
def _UpperCamelCase ( lowerCAmelCase_ ) ->None:
create_state_space_tree(lowerCAmelCase_ , [] , 0 , [0 for i in range(len(lowerCAmelCase_ ) )] )
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAm... | 627 | 1 |
import math
def _UpperCamelCase ( lowerCAmelCase_ ) ->bool:
return math.sqrt(lowerCAmelCase_ ) * math.sqrt(lowerCAmelCase_ ) == num
def _UpperCamelCase ( lowerCAmelCase_ ) ->bool:
UpperCAmelCase = 0
UpperCAmelCase = n
while left <= right:... | 627 |
import numpy
class __lowercase :
def __init__( self : Union[str, Any] , __lowerCamelCase : numpy.ndarray , __lowerCamelCase : numpy.ndarray ) -> None:
"""simple docstring"""
... | 627 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__a = {
"""gwf-440k""": {
... | 627 |
import argparse
__a = """docs/source/_static/js/custom.js"""
def _UpperCamelCase ( lowerCAmelCase_ ) ->Optional[Any]:
with open(lowerCAmelCase_ , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCAmelCase = f.readlines()
UpperCAmelCase ... | 627 | 1 |
from __future__ import annotations
import numpy as np
def _UpperCamelCase ( lowerCAmelCase_ ) ->tuple[np.ndarray, np.ndarray]:
UpperCAmelCase , UpperCAmelCase = np.shape(lowerCAmelCase_ )
if rows != columns:
UpperCAmelCase = (
"""'table' has... | 627 |
import math
class __lowercase :
def _lowercase ( self : Union[str, Any] , __lowerCamelCase : list[list[float]] , __lowerCamelCase : list[int] ) -> int:
"""simple docstring"""
... | 627 | 1 |
# Copyright 2021 The HuggingFace 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 require... | 627 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
UpperCAmelCase = {}
UpperCAmelCase = tok... | 627 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 627 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__a = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksach... | 627 | 1 |
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 ... | 627 |
import math
import qiskit
def _UpperCamelCase ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 ) ->qiskit.result.counts.Counts:
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ ... | 627 | 1 |
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