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 io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a ( snak... | 97 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/re... | 97 | 1 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepie... | 97 |
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(snake_case__ ) )
lowercase_ = os.path.join(sna... | 97 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase__( metaclass=UpperCAmelCase ):
"""simple docstring"""
a :Tuple = ['onnx']
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : ... | 97 |
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 a ( ... | 97 | 1 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 97 |
import unittest
from knapsack import knapsack as k
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[int] ) -> str:
lowercase_ = 0
lowercase_ = [0]
lowercase_ = [0]
lowercase_ ... | 97 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class l... | 97 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def a ( snake_case__: Optional[int] , snake_case__: Optional[Any] , snake_case__: int , snake_case__: Dict=5 ):
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/b... | 97 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :List[str] = 'encoder-decoder'
a :Any = Tr... | 97 | 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 Model... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils im... | 97 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a ( snake_case__: bytes ):
'''simple docstring'''
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = F'''... | 97 | 1 |
def a ( snake_case__: dict ):
'''simple docstring'''
lowercase_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase_ = set()
return any(
node not in visited and depth_first_search(snake_case__ , snake_cas... | 97 |
from __future__ import annotations
def a ( snake_case__: list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 97 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def a ( snake_case__: int , snake_case__: int , snake_case__: int , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
#... | 97 |
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':... | 97 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Dict , *SCREAMING_SNAKE_CAS... | 97 |
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 al... | 97 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGEN... | 97 |
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 a ( snake_case__: Any ... | 97 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def a ( ):
'''simple docstring'''
lowercase_ = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
... | 97 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__a = logging.getLogger(__name__)
_... | 97 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 97 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a = 'src/transformers'
__a = 'docs/source/en/tasks'
... | 97 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a = 'src/transformers'
__a = 'docs/source/en/tasks'
... | 97 |
from __future__ import annotations
__a = 8.988E9 # units = N * m^s * C^-2
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = abs(chargea * chargea )
if (forc... | 97 | 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 OptionalDependencyNotAvailab... | 97 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 97 | 1 |
from __future__ import annotations
def a ( snake_case__: list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 97 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_... | 97 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import to... | 97 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for di... | 97 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = (UnCLIPScheduler,)
def _lowercase ( self : Union[str, Any] , **... | 97 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 97 | 1 |
def a ( snake_case__: int = 200 ):
'''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:
for i in range(snake_case__ , pence + 1... | 97 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a ( snake_case__: List[Any] ):
'''simple docstring'''
if "cls_token" in name:
lowercase_ = name.replace(... | 97 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 97 |
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_bar... | 97 | 1 |
import os
def a ( ):
'''simple docstring'''
with open(os.path.dirname(snake_case__ ) + '''/grid.txt''' ) as f:
lowercase_ = [] # noqa: E741
for _ in range(20 ):
l.append([int(snake_case__ ) for x in f.readline().split()] )
lo... | 97 |
from __future__ import annotations
from math import pi, sqrt
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
rais... | 97 | 1 |
def a ( ):
'''simple docstring'''
return 1
def a ( snake_case__: int ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def a ( snake_case__: int ):
'''simple docstring'''
return 0 if x... | 97 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/re... | 97 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__a = 5_0_0_0_0
__a = 5_0_0_0
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '... | 97 |
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(snake_case__ ) )
lowercase_ = os.path.join(sna... | 97 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ft... | 97 |
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 a ( ... | 97 | 1 |
from __future__ import annotations
import math
def a ( snake_case__: int , snake_case__: int , snake_case__: bool , snake_case__: list[int] , snake_case__: float ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0'... | 97 |
import unittest
from knapsack import knapsack as k
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[int] ) -> str:
lowercase_ = 0
lowercase_ = [0]
lowercase_ = [0]
lowercase_ ... | 97 | 1 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a ( snake_case__: Dict ):
'''simple docstring'''
return 1 / (1 + np.exp(-z ))
... | 97 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__a = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is... | 97 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :List[str] = 'encoder-decoder'
a :Any = Tr... | 97 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_au... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
from timeit import timeit
def a ( snake_case__: int ):
'''simple docstring'''
if number < 0:
raise ValueError('''the value of input must not be negative''' )
lowercase_ = 0
while number:
number &= number - 1
result += 1
return res... | 97 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a ( snake_case__: bytes ):
'''simple docstring'''
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = F'''... | 97 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_m... | 97 |
from __future__ import annotations
def a ( snake_case__: list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 97 | 1 |
from __future__ import annotations
import time
import numpy as np
__a = [8, 5, 9, 7]
__a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
... | 97 |
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':... | 97 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetect... | 97 |
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 al... | 97 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__a = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__a = requests.get(url, headers={'UserAg... | 97 |
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 a ( snake_case__: Any ... | 97 | 1 |
__a = [
(1_0_0_0, 'M'),
(9_0_0, 'CM'),
(5_0_0, 'D'),
(4_0_0, 'CD'),
(1_0_0, 'C'),
(9_0, 'XC'),
(5_0, 'L'),
(4_0, 'XL'),
(1_0, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def a ( snake_case__: str ):
'''s... | 97 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__a = logging.getLogger(__name__)
_... | 97 | 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,... | 97 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a = 'src/transformers'
__a = 'docs/source/en/tasks'
... | 97 | 1 |
from __future__ import annotations
from math import pow, sqrt
def a ( snake_case__: float , snake_case__: float , snake_case__: float ):
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argumen... | 97 |
from __future__ import annotations
__a = 8.988E9 # units = N * m^s * C^-2
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = abs(chargea * chargea )
if (forc... | 97 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ...tes... | 97 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 97 | 1 |
from __future__ import annotations
from math import pi, sqrt
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
rais... | 97 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_... | 97 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : str ) -> Optional[Any]:
# test for the above condition
self.test()
def _lowercase ( self : T... | 97 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for di... | 97 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__a = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
'featu... | 97 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 97 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 97 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a ( snake_case__: List[Any] ):
'''simple docstring'''
if "cls_token" in name:
lowercase_ = name.replace(... | 97 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_m... | 97 |
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_bar... | 97 | 1 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__a = 'src/transformers'
# This is to make sure the transformers module imported is the one i... | 97 |
from __future__ import annotations
from math import pi, sqrt
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
rais... | 97 | 1 |
import colorsys
from PIL import Image # type: ignore
def a ( snake_case__: float , snake_case__: float , snake_case__: int ):
'''simple docstring'''
lowercase_ = x
lowercase_ = y
for step in range(snake_case__ ): # noqa: B007
lowercas... | 97 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/re... | 97 | 1 |
import os
def a ( ):
'''simple docstring'''
with open(os.path.dirname(snake_case__ ) + '''/p022_names.txt''' ) as file:
lowercase_ = str(file.readlines()[0] )
lowercase_ = names.replace('''"''' , '''''' ).split(''',''' )
names.sort()
... | 97 |
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(snake_case__ ) )
lowercase_ = os.path.join(sna... | 97 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def a ( snake_case__: Dict , snake_case__: Any , snake_case__: Dict ):
'''simple docstring'''
lowercase_ = AutoConfig.from_pretrained(snake_case__ )
... | 97 |
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 a ( ... | 97 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series i... | 97 |
import unittest
from knapsack import knapsack as k
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[int] ) -> str:
lowercase_ = 0
lowercase_ = [0]
lowercase_ = [0]
lowercase_ ... | 97 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
Au... | 97 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 | 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 a ( snake_case__: Any ... | 97 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :List[str] = 'encoder-decoder'
a :Any = Tr... | 97 | 1 |
from sklearn.metrics import fa_score
import datasets
__a = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__a = '\nArgs:\n predictions (`list` of `int`): Predicted labe... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDepend... | 97 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a ( snake_case__: bytes ):
'''simple docstring'''
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = F'''... | 97 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__a = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase__( UpperCAmelCase ):... | 97 |
from __future__ import annotations
def a ( snake_case__: list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 97 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = ... | 97 |
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':... | 97 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
... | 97 |
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 al... | 97 | 1 |
from jiwer import compute_measures
import datasets
__a = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for conne... | 97 |
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 a ( snake_case__: Any ... | 97 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def a ( snake_case__: Tuple ):
'''simple docstring'''
lowercase_ = SwinConfig(image_size=192 )
if "base" in... | 97 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__a = logging.getLogger(__name__)
_... | 97 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_util... | 97 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a = 'src/transformers'
__a = 'docs/source/en/tasks'
... | 97 | 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
... | 97 |
from __future__ import annotations
__a = 8.988E9 # units = N * m^s * C^-2
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = abs(chargea * chargea )
if (forc... | 97 | 1 |
from __future__ import annotations
import math
def a ( snake_case__: list , snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) != 2 or len(a[0] ) != 2 or len(snake_case__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2'''... | 97 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 97 | 1 |
import math
def a ( ):
'''simple docstring'''
lowercase_ = input('''Enter message: ''' )
lowercase_ = int(input(F'''Enter key [2-{len(snake_case__ ) - 1}]: ''' ) )
lowercase_ = input('''Encryption/Decryption [e/d]: ''' )
if mode.lower().st... | 97 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_... | 97 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowercase__:
"""simple docstring"""
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : Any ) -> str:
lowercase_ = data
lowercase_ = No... | 97 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for di... | 97 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
... | 97 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 97 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_x... | 97 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a ( snake_case__: List[Any] ):
'''simple docstring'''
if "cls_token" in name:
lowercase_ = name.replace(... | 97 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[Any] = 'ClapFeatureExtractor'
a :List[str] = ('RobertaTokenizer', 'RobertaToke... | 97 |
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_bar... | 97 | 1 |
from PIL import Image
def a ( snake_case__: Image , snake_case__: int ):
'''simple docstring'''
lowercase_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__: int ) -> int:
return int(128 + factor * (c - 128) )
return ... | 97 |
from __future__ import annotations
from math import pi, sqrt
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
rais... | 97 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow... | 97 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/re... | 97 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_in... | 97 |
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(snake_case__ ) )
lowercase_ = os.path.join(sna... | 97 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config']... | 97 |
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 a ( ... | 97 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.... | 97 |
import unittest
from knapsack import knapsack as k
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[int] ) -> str:
lowercase_ = 0
lowercase_ = [0]
lowercase_ = [0]
lowercase_ ... | 97 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__a = input('Enter image url: ').strip()
print(f"Downloading image from {url} ...")
__a = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image UR... | 97 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not acce... | 97 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :List[str] = 'encoder-decoder'
a :Any = Tr... | 97 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a ( snake_case__: str , snake_case__: str ):
'''simple docstring'''
lowercase_ = list(snake_case__ )
lowercase_ = list(snake_case__ )
lowercase... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
import heapq
import sys
import numpy as np
__a = tuple[int, int]
class lowercase__:
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> Any:
lowercase_ = []
lowercase_ = set()
def _lowercase ( self : Dict ) ... | 97 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a ( snake_case__: bytes ):
'''simple docstring'''
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = F'''... | 97 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.conve... | 97 |
from __future__ import annotations
def a ( snake_case__: list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 97 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__a = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
(... | 97 |
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':... | 97 | 1 |
import string
def a ( snake_case__: str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
lowercase_ = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
lowercase_ = string.a... | 97 |
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 al... | 97 | 1 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 |
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 a ( snake_case__: Any ... | 97 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a ( snake_case__: Optional[int] ):
'''si... | 97 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__a = logging.getLogger(__name__)
_... | 97 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
... | 97 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a = 'src/transformers'
__a = 'docs/source/en/tasks'
... | 97 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowercase__:
"""simple docstring"""
a :float
a :TreeNode | None = None
a :TreeNode | None = None
def a ( snake_case__: TreeNode | None ):
... | 97 |
from __future__ import annotations
__a = 8.988E9 # units = N * m^s * C^-2
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = abs(chargea * chargea )
if (forc... | 97 | 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_alb... | 97 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 97 | 1 |
import string
def a ( snake_case__: str ):
'''simple docstring'''
lowercase_ = ''''''
for i in sequence:
lowercase_ = ord(snake_case__ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
... | 97 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_... | 97 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_available():
... | 97 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for di... | 97 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 97 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
'_TestCommandArgs',
[
'dataset',
... | 97 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__a = 'scheduler_config.json'
class lowercase__( UpperCAmelCase ... | 97 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a ( snake_case__: List[Any] ):
'''simple docstring'''
if "cls_token" in name:
lowercase_ = name.replace(... | 97 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase__( UpperCAmelCase , unittest.TestCase ):
... | 97 |
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_bar... | 97 | 1 |
def a ( snake_case__: list ):
'''simple docstring'''
lowercase_ = len(snake_case__ )
for _ in range(snake_case__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
lowercase_ , lowercase_ = arr[i ... | 97 |
from __future__ import annotations
from math import pi, sqrt
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
rais... | 97 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase__( UpperCAmelCase ):
... | 97 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/re... | 97 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike... | 97 |
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(snake_case__ ) )
lowercase_ = os.path.join(sna... | 97 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',... | 97 |
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 a ( ... | 97 | 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'
' f... | 97 |
import unittest
from knapsack import knapsack as k
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Optional[int] ) -> str:
lowercase_ = 0
lowercase_ = [0]
lowercase_ = [0]
lowercase_ ... | 97 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',... | 97 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 97 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :List[str] = 'encoder-decoder'
a :Any = Tr... | 97 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequ... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
from __future__ import annotations
def a ( snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
lowercase_ = []
lowercase_ , lowercase_ = input_list[low:mid], input_list[mid : high + 1]
... | 97 |
__a = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a ( snake_case__: bytes ):
'''simple docstring'''
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case__ , snake_case__ ):
lowercase_ = F'''... | 97 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .... | 97 |
from __future__ import annotations
def a ( snake_case__: list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 97 | 1 |
from __future__ import annotations
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitio... | 97 |
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':... | 97 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__a = (7_2_0, 1_2_8_0) # Height, Width
__a = (0.4, 0.6) # if height or width lower than this scale, drop it.
__a = 1 / 1_0_0
__a = ''
__a ... | 97 |
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 al... | 97 | 1 |
from heapq import heappop, heappush
import numpy as np
def a ( snake_case__: np.ndarray , snake_case__: tuple[int, int] , snake_case__: tuple[int, int] , snake_case__: bool , ):
'''simple docstring'''
lowercase_ , lowercase_ = grid.shape
lowercas... | 97 |
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 a ( snake_case__: Any ... | 97 | 1 |
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