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 unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.mo... | 30 |
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 30 | 1 |
from ..utils import DummyObject, requires_backends
class __a( metaclass=_a ):
"""simple docstring"""
lowerCAmelCase = ['''torch''', '''torchsde''']
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
requires_bac... | 30 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 30 | 1 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is require... | 30 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = (IPNDMScheduler,)
lowerCAmelCase = (('''num_inference_steps''', 50),)
def a__ ( se... | 30 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> None:
... | 30 | 1 |
import operator as op
__a = 'scaler.pt'
__a = 'pytorch_model'
__a = 'random_states'
__a = 'optimizer'
__a = 'scheduler'
__a = 'pytorch_model.bin'
__a = 'pytorch_model.bin.index.json'
__a = 'mod... | 30 |
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 t... | 30 | 1 |
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 __a( _a ):
"""... | 30 |
import unittest
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = None , ):
'''simple docstring'''
UpperCAmelCase_ : Dict = np.shape(_lowercase )
UpperCAmelCase_ : Optional[Any] = np.shape(_lowerc... | 30 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
impo... | 30 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is require... | 30 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 30 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 30 | 1 |
from __future__ import annotations
__a = tuple[int, int, int]
__a = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selection ----------------------... | 30 |
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856... | 30 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 30 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise Value... | 30 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def lowerCamelCase_... | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(... | 30 | 1 |
def lowerCamelCase__ ( _lowercase = 10 , _lowercase = 22 ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = range(1 , _lowercase )
UpperCAmelCase_ : Optional[int] = range(1 , _lowercase )
return sum(
1 for power in powers for base in base... | 30 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarni... | 30 | 1 |
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCamelCase__ ( _lower... | 30 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
while a != 0:
UpperCAmelCase_, UpperCAmelCase_ : Optional[int] = b % a, a
return b
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
... | 30 | 1 |
from heapq import heappop, heappush
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_ : Dict = grid.shape
UpperCAmelCase_ : str = [... | 30 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 30 | 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 lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAme... | 30 |
import numpy as np
import datasets
__a = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. M... | 30 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a = logging.get_logger(__name__)
__a = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/... | 30 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__name__)
__a ... | 30 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __a:
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=0.2 ,_SCREAMING_SNAKE_... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['Encode... | 30 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 30 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See ... | 30 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __a( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) -> Optional[int]:
debug_launcher(test_script.main )
... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailab... | 30 | 1 |
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856... | 30 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 30 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
if not (isinstance(_lowercase , _lowercase ) and isinstance(_lowercase , _lowercase )):
raise ValueError('''longest_common_substring() takes two strings for inputs''' )
UpperCAmelCase_ : ... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__a = logging.get... | 30 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase__ ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 ... | 30 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__a = [
'word_embeddings_layernorm.weight',
'wo... | 30 | 1 |
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCamelCase__ ( _low... | 30 |
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 30 | 1 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=T... | 30 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 30 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impor... | 30 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 1 |
import enum
import shutil
import sys
__a ,__a = shutil.get_terminal_size()
__a = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class __a( enum.Enum ):
"""simple docstring"""
lowerCAmelCase = 0
lowerCAmelCase = 1
def lowe... | 30 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> None:
... | 30 | 1 |
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__ ( _lowercase ):
... | 30 |
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 t... | 30 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase = " " ):
'''simple docstring'''
UpperCAmelCase_ : str = []
UpperCAmelCase_ : Any = 0
for index, char in enumerate(_lowercase ):
if char == separator:
split_words.append(string[last_inde... | 30 |
import unittest
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = None , ):
'''simple docstring'''
UpperCAmelCase_ : Dict = np.shape(_lowercase )
UpperCAmelCase_ : Optional[Any] = np.shape(_lowerc... | 30 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-l... | 30 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is require... | 30 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__a = logging.get_logger(__name__)
@datacla... | 30 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 30 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def l... | 30 |
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856... | 30 | 1 |
import argparse
import os
import re
__a = 'src/transformers'
# Pattern that looks at the indentation in a line.
__a = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_impor... | 30 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise Value... | 30 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase = None ):
'''simple docstring'''
if version.parse(hfh.__version__ ).release < version.parse('''0.11.... | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(... | 30 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name... | 30 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarni... | 30 | 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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM... | 30 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
while a != 0:
UpperCAmelCase_, UpperCAmelCase_ : Optional[int] = b % a, a
return b
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
... | 30 | 1 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__a = [
'word_embeddings_layernorm.weight',
'wo... | 30 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
try:
if not is_torch_a... | 30 |
import numpy as np
import datasets
__a = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. M... | 30 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
Vilt... | 30 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__name__)
__a ... | 30 | 1 |
from typing import Any
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not input_list:
return []
UpperCAmelCase_ : Any = [input_list.count(_lowercase ) for value in input_list]
UpperCAmelCase_ : Dict = max(_lowercase ) # Gets the ... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['Encode... | 30 | 1 |
import os
import sys
import unittest
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_in... | 30 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See ... | 30 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipeline... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailab... | 30 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
),
# See all Vivit ... | 30 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 30 | 1 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : str = input('''Enter message: ''' )
UpperCAmelCase_ : Optional[int] = input('''Enter key [alphanumeric]: ''' )
UpperCAmelCase_ : Tup... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__a = logging.get... | 30 | 1 |
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__ ( _lowercase ):
... | 30 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__a = [
'word_embeddings_layernorm.weight',
'wo... | 30 | 1 |
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 transformers.utils... | 30 |
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 30 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression... | 30 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 30 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : Tuple = len(_lowercase ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 30 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 1 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__a = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n ... | 30 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> None:
... | 30 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 30 |
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 t... | 30 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# See all AltCLIP mode... | 30 |
import unittest
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = None , ):
'''simple docstring'''
UpperCAmelCase_ : Dict = np.shape(_lowercase )
UpperCAmelCase_ : Optional[Any] = np.shape(_lowerc... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__a = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 30 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is require... | 30 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tr... | 30 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 30 | 1 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mix... | 30 |
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856... | 30 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __a( unittest.TestCase ):
... | 30 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise Value... | 30 | 1 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(... | 30 | 1 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __a( _a ):
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE="" ,_SCREAMING_SNAKE_CASE="train" ) -> Tuple:
assert os.path.isdir(_SCREAMING_SNAKE_CASE )
... | 30 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarni... | 30 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _lowercase , _lowe... | 30 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
while a != 0:
UpperCAmelCase_, UpperCAmelCase_ : Optional[int] = b % a, a
return b
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
... | 30 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = '▁'
__a = ... | 30 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 30 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __a( _a ):
"""simple docstring"""
lowerCAmelC... | 30 |
import numpy as np
import datasets
__a = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. M... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processin... | 30 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__name__)
__a ... | 30 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils imp... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['Encode... | 30 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __a( unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase = JukeboxTokenizer
lowerCAmelCase = {
'''artist''': '''Zac Brown Band''',
... | 30 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See ... | 30 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a = logging.get_logger(__name__)
__a = {
'google/bit-50': 'https://huggingface.co/google/bit-50... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailab... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
... | 30 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 30 | 1 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
lowerCAmelCase ... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__a = logging.get... | 30 | 1 |
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 SequenceFeatureExtracti... | 30 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__a = [
'word_embeddings_layernorm.weight',
'wo... | 30 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Any = {}
UpperCAmelC... | 30 |
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 30 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__a = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', he... | 30 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 30 | 1 |
from __future__ import annotations
from fractions import Fraction
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCamelCase__ ( _lower... | 30 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logg... | 30 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> None:
... | 30 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
... | 30 |
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 t... | 30 | 1 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 |
import unittest
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = None , ):
'''simple docstring'''
UpperCAmelCase_ : Dict = np.shape(_lowercase )
UpperCAmelCase_ : Optional[Any] = np.shape(_lowerc... | 30 | 1 |
def lowerCamelCase__ ( _lowercase = 1000 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 30 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is require... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'processing... | 30 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 30 | 1 |
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if edge <= 0 or not isinstance(_lowercase , _lowercase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def lowerCamelCase__ ( _lowercas... | 30 |
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_available():
... | 30 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise Value... | 30 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_... | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available()... | 30 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarni... | 30 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from da... | 30 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
while a != 0:
UpperCAmelCase_, UpperCAmelCase_ : Optional[int] = b % a, a
return b
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['Encode... | 30 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 30 | 1 |
import os
from math import logaa
def lowerCamelCase__ ( _lowercase = "base_exp.txt" ):
'''simple docstring'''
UpperCAmelCase_ : float = 0
UpperCAmelCase_ : Union[str, Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_lowercase ) , _... | 30 |
import numpy as np
import datasets
__a = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. M... | 30 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that... | 30 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__name__)
__a ... | 30 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['Encode... | 30 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 30 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See ... | 30 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __a( unittest.TestCase ):
"""simple docstring"""
def a__ ( self ) ->... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailab... | 30 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impo... | 30 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 30 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__a = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Tr... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__a = logging.get... | 30 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common ... | 30 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__a = [
'word_embeddings_layernorm.weight',
'wo... | 30 | 1 |
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
__a = int(input('Enter n... | 30 |
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 30 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Any = len(_lowercase )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCAmelCase_ : int = 0
print(_lowerca... | 30 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 30 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __a( nn.Module ):
"""si... | 30 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', '... | 30 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> None:
... | 30 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase=1024 ):
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_ : List[... | 30 |
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 t... | 30 | 1 |
__a = 8.314462 # Unit - J mol-1 K-1
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin ... | 30 |
import unittest
import numpy as np
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = None , ):
'''simple docstring'''
UpperCAmelCase_ : Dict = np.shape(_lowercase )
UpperCAmelCase_ : Optional[Any] = np.shape(_lowerc... | 30 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = (CMStochasticIterativeScheduler,)
lowerCAmelCase = 10
def a__ ( self ,**_SCREAMING... | 30 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = f'''a bytes-like object is require... | 30 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggingface.co/models?fil... | 30 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 30 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__a = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schw... | 30 |
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856... | 30 | 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_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_M... | 30 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise Value... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'Perce... | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(... | 30 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
... | 30 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarni... | 30 | 1 |
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