code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import collections
import os
import re
from pathlib import Path
A_ = "src/transformers"
# Matches is_xxx_available()
A_ = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
A_ = re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line ... | 604 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble... | 604 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 604 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_... | 604 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 1 |
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 utils_ner import Spl... | 604 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase = 200 )-> int:
"""simple docstring"""
lowercase = [1, 2, 5, 10, 20, 50, 100, 200]
lowercase = [0] * (pence + 1)
lowercase = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 604 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __UpperCAmelCase ( UpperCAmelCase )-> Union[str, Any]:
"""simple docstring"""
lowercase = FileLock(str(tmpdir / '''foo.lock''' ) ... | 604 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transform... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 604 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
A_ = logging.get_logger(__name__)
A_ = {"vocab_fil... | 604 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowercase ( _A ):
@staticmethod
@abstractmethod
def __a ( __lowerCamelCase : ArgumentParser ) -> Optional[int]:
'''simple docstring'''
rai... | 604 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 1 |
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 TFXL... | 604 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 1 |
from __future__ import annotations
import math
def __UpperCAmelCase ( 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:
... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip... | 604 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enabl... | 604 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import ... | 604 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try:
i... | 604 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
A_ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
A_ = "\\n@inproceedin... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 1 |
import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = "x", UpperCAmelCase = 10**-10, UpperCAmelCase = 1, )-> complex:
"""simple docstring"""
lower... | 604 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.txt"}
A_ = {
"vocab_file": {
... | 604 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 1 |
import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {}
class __lowercase ( _A ):
lowercase = 'llama'
lowercase = ['past_key_values']
def __init__( self ... | 604 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"v... | 604 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 604 | 1 |
import requests
from bsa import BeautifulSoup
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
lowercase = BeautifulSoup(requests.get(UpperCAmelCase, params=UpperCAmelCase ).content, '''html.par... | 604 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 1 |
from scipy.stats import pearsonr
import datasets
A_ = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normal... | 604 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 1 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dim... | 604 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 1 |
import unittest
from transformers import DonutProcessor
A_ = "naver-clova-ix/donut-base"
class __lowercase ( unittest.TestCase ):
def __a ( self : List[str] ) -> Any:
'''simple docstring'''
lowercase = DonutPr... | 604 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 1 |
import unittest
from knapsack import knapsack as k
class __lowercase ( unittest.TestCase ):
def __a ( self : Optional[int] ) -> str:
'''simple docstring'''
lowercase = 0
lowercase = [0]
lowerc... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils impo... | 604 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHori... | 604 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 1 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, ... | 604 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning th... | 604 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 1 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 1 |
import requests
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> None:
"""simple docstring"""
lowercase = {'''Content-Type''': '''application/json'''}
lowercase = requests.post(UpperCAmelCase, json={'''text''': messag... | 604 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, **UpperCAmelCase )-> Tuple:
"""simple docstring"""
lowercase = AutoConfig.from_pretrained(Upper... | 604 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "spiece.model... | 604 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
A_ = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final... | 604 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 1 |
import heapq
import sys
import numpy as np
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict ) -> Optional[int]:
'''simple docstring'''
lowercase = []
lowercase = set()
def _... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
A_ = "src/transformers"
# Matches is_xxx_available()
A_ = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
A_ = re.compile(R"^_import_structure\s+=\s+\{([^\}]+)... | 604 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> Optional[Any]:
"""simple docstring"""
lowercase = [0] * len(UpperCAmelCase )
lowercase = []
lowercase = [1] * len(UpperCAmelCase )
for values in graph.values():
... | 604 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 1 |
from torch import nn
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
... | 604 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 1 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __UpperCAmelCase (... | 604 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __lowercase :
def __init__( self : Optional[Any] , __lowerCamelCase : int ) -> Any:
'''simple docstring'''
lowercase = data
... | 604 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and ... | 604 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 1 |
import datasets
A_ = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and Stoyanov... | 604 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 604 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback... | 604 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> Tuple:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase=0 )-> Opti... | 604 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __lowercase ( _A ):
def __init__( self : O... | 604 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCAmelCase ( *UpperCAmelCase )-> str:
"""simple docstring"""
if not isinstance(UpperCAmelCase, UpperCAmelCase ):
... | 604 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 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"],
}
class __lowercase :
def __init__( self : Tuple , __lowerCam... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
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
from flax.jax... | 604 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 1 |
class __lowercase :
def __init__( self : Any ) -> int:
'''simple docstring'''
lowercase = ''''''
lowercase = ''''''
lowercase = []
def __a ( self : str , __lowerCamelCase : in... | 604 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A_ ... | 604 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __UpperCAmelCase ( UpperCAmelCase )-> List[str]:
"""simple docstring"""
lowercase = [
'''encoder.versi... | 604 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
A_ = TypeVar("KT")
A_ = TypeVar("VT")
class __lowercase ( Generic[KT, VT] ):
def __init__( self : Union[str, Any] , __lowerCamelCase : KT | ... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 604 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import... | 604 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 1 |
import math
def __UpperCAmelCase ( UpperCAmelCase = 100 )-> int:
"""simple docstring"""
lowercase = sum(i * i for i in range(1, n + 1 ) )
lowercase = int(math.pow(sum(range(1, n + 1 ) ), 2 ) ... | 604 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> Any: # noqa: E741
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = 0
lowercase = [0] * n
lowercase = [False] * n
lowercase = [False] *... | 604 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
A_ = logging.get_logger(__name__)
class __lowercase ( _A ):
def __init__( self : List[Any] , *__lowerCamelCase : Union[str, Any] , **__low... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase, UpperCAmelCase ):
raise TypeError('''only integers accepted as input''' )
else:
lowercase = str(abs(UpperCAmelCase ... | 604 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase=5 )-> Tuple:
"""simple docstring"""
assert masked_input.count('''<mask>''' ... | 604 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchau... | 604 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 1 |
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 __lowercase ( _A , unittest.TestCase ... | 604 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
... | 604 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_... | 604 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 604 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 604 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.imp... | 604 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/rea... | 604 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx impo... | 604 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
l... | 604 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowercase ( _A ):
def __init__( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : List[Any] ) -> Any:
... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A_ = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_s... | 604 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
lowercase = [1]
for i in range(2, UpperCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bo... | 604 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 1 |
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 .tokenization_barth... | 604 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 1 |
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 .tokenization_rembe... | 604 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_M... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
lowercase = hex_num[0] == '''-'''
if is_neg... | 604 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConfig"]
}
... | 604 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase )
class lowerCamelCase_ ( lowerCamelCase ):
# `task` is not a ClassVar since we want it to be part of the ... | 0 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCamelCase... | 1 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 0 |
from __future__ import annotations
from typing import Any
class lowerCamelCase__ ( _A):
"""simple docstring"""
pass
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : int , __lowerCAmelCase : Any ) -> None:
_A ... | 2 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
... | 3 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class a ( a__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output fo... | 4 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
"""configuration_clip"""... | 5 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
_lowerCamelCase = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class ... | 6 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> int:
'''simple docstring'''
while second != 0:
_A = first & second
first ^= second
_A = c << 1
return first
if __name__ ==... | 7 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.... | 8 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE__ = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the... | 9 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 604 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all T... | 10 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 0 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCAmelCase (__A):
"... | 11 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : List[Any] = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig"... | 12 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 0 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
fr... | 13 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
... | 14 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A : str = logging.get_logger(__name__)
__A : str = 'h... | 16 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCH... | 17 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config... | 18 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
"""simple docstring"""
from collections.abc import Callable
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> float:
"""simple docstring"""
_UpperCamelCase = a
_UpperCamelCase = b
if function(__snak... | 19 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 0 |
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