code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''Ju... | 79 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_sche... | 329 | 0 |
'''simple docstring'''
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
_SCREAMING_SNAKE_CASE : ... | 361 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class _snake_case ( logging.LoggerAdapter ):
@staticmethod
def lowerCAmelCase__ ( a__ ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = Par... | 92 | 0 |
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 NestedDataStructureLi... | 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 7 | 1 |
import torch
def lowerCamelCase__ ( ) -> Tuple:
if torch.cuda.is_available():
_A: Optional[int] = torch.cuda.device_count()
else:
_A: int = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 301 |
def lowerCamelCase__ ( a = 10**9 ) -> int:
_A: Dict = 1
_A: Union[str, Any] = 2
_A: List[str] = 0
_A: List[Any] = 0
_A: int = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value +... | 301 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ... | 343 |
'''simple docstring'''
def _A ( lowercase__ = 1000000 ):
lowercase__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 164 | 0 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.... | 351 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
Robe... | 4 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Dict ... | 50 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 50 | 1 |
'''simple docstring'''
import random
class __UpperCamelCase :
@staticmethod
def lowercase__ ( lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[ord(lowerCAmelCase ) for i in text]
lowerCamelCase_ =... | 371 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 6 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils imp... | 56 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.... | 92 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __A ( a_ :list[list[float]]) -> list[list[float]]:
__a : List[Any] = Decimal
# Check if the provided matrix has 2 rows and 2 c... | 188 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [''... | 188 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
__lowerCAmelCase = order
# a_{0} ... a_{k}
__lowerCAm... | 301 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCA... | 301 | 1 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCamelCase__ = ... | 182 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu,... | 182 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Bert... | 88 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCAmelCase_ ( __lowercase ):
def __lt__( self : Optional[int] , UpperCAmelCa... | 4 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( a ):
lowercase__ : int = (PNDMScheduler,)
lowercase__ : int = (("""num_inference_steps""", 50),)
... | 206 | def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : str = " " ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
for index, char in enumerate(UpperCAmelCase__ ):
if char == separator:
split_... | 206 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( A_ ,A_):
UpperCamelCase__: str = []
create_all_state(1 ,a__ ,a__ ,[] ,a__)
return result
def lowerCAmelCase_ ( A_ ,A_ ,A_ ,A_ ,A_ ,):
if level == 0:
to... | 149 |
def __lowerCAmelCase ( a__ , a__ ) -> float:
def get_matched_characters(a__ , a__ ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__a = int(max(0 ,... | 6 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__UpperCAmelCase = collections.namedtuple('''_Datasets''', ['''train'''... | 355 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''google... | 42 | 0 |
def UpperCAmelCase__ ( _A : Dict ):
'''simple docstring'''
a__, a__ =[], []
while len(_A ) > 1:
a__, a__ =min(_A ), max(_A )
start.append(_A )
end.append(_A )
collection.remove(_A )
collection.remove(_A )
... | 188 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 188 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
SCREAMING_SNAKE_CASE : Tuple = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.... | 352 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santa... | 252 | 0 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class lowercase__ ( UpperCamelCase_):
Up... | 182 | import itertools
import string
from collections.abc import Generator, Iterable
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(_lowercase )
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = tup... | 182 | 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 : List[Any] = logging.get_logger(__name... | 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 0 |
'''simple docstring'''
lowerCamelCase :Union[str, Any] = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerC... | 206 |
'''simple docstring'''
from math import pow
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal ... | 206 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 370 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Dict:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase , int(b / 2 ) ) * actual_power(UpperCAmelCase , int(b / 2 ) )
else:
return a *... | 265 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetC... | 317 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE__ ( __A = 1_500_000 ) -> int:
_snake_case = defaultdict(__A )
_snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 ... | 42 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Optional[Any] ) -> Any:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__lowerCAmelCase : Any = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Unio... | 232 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_Uppe... | 232 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :int = get_failure_array(lowercase__ )
# 2) Step through t... | 84 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <... | 252 | 0 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ : int ) -> list:
'''simple docstring'''
lowerCAmelCase_ :Any = [True] * n
lowerCAmelCase_ :Any = False
lowerCAmelCase_ :Dict = False
low... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCAmelCase_ : Union[str, Any] = """<<<<<<< This should probably be modified because it men... | 91 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise Optional... | 350 |
"""simple docstring"""
import sys
from collections import defaultdict
class a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
"""simple docstring"""
A__ = []
def UpperCamelCase ( sel... | 69 | 0 |
import re
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if len(re.findall('''[ATCG]''' , lowerCAmelCase__ ) ) != len(lowerCAmelCase__ ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''ATCG''' , '''TAGC''' ) )
if... | 101 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils imp... | 265 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_A = '''__DUMMY_TRANSFORMERS_USER__'''
_A = '''Dummy User'''
_A = '''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'''
_A = '''https://h... | 261 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 261 | 1 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils i... | 368 |
# Lint as: python3
import itertools
import os
import re
_UpperCAmelCase : str = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase : Dict = re.compile(R"""([a-z\d])([A-Z])""")
_UpperCAmelCase : Dict = re.compile(R"""(?<!_)_(?!_)""")
_UpperCAmelCase : Tupl... | 200 | 0 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowercase:
'''simple docstring'''
def __init__( self: Union[str, Any], a_: Optional[Any], a_: int, a_: int ):
'''sim... | 64 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
Compu... | 367 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers... | 139 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'vocab_f... | 145 | """simple docstring"""
from math import factorial
def UpperCAmelCase ( UpperCAmelCase = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fa... | 69 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/mic... | 366 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__A = logging.get_logger(__name__)
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *_UpperCAme... | 2 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_uti... | 261 | """simple docstring"""
import random
def _lowerCamelCase( a , a , a ):
__a = a[left_index]
__a = left_index + 1
for j in range(left_index + 1 , a ):
if a[j] < pivot:
__a , __a = a[i], a[j]
... | 261 | 1 |
"""simple docstring"""
def __lowerCamelCase ( __a :float , __a :float ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
print(F'''{price... | 367 |
def __lowerCamelCase ( __a :int = 3 , __a :int = 7 , __a :int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
A__ = 0
A__ = 1
for current_denominator in range(1 , limit + 1 ):
A__ = cu... | 276 | 0 |
'''simple docstring'''
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_torc... | 80 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple ... | 200 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : List[str] = [randint(-1000, 1000 ) for i in range(10 )]
_SCREAMING_SNAKE_CASE : Union[str... | 359 |
from timeit import timeit
def lowerCamelCase__ (__lowerCamelCase ):
if number < 0:
raise ValueError("the value of input must not be negative" )
_SCREAMING_SNAKE_CASE : str = 0
while number:
number &= number - 1
result += 1
return... | 325 | 0 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE ( self : Optional[in... | 14 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"t5-small": "https://huggingface.co/t5-small/resolve/ma... | 139 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase_ : Dict = [int(__lowerCAmelCase ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__lowerCAmelCase ) == 4 and all(0 <= int... | 350 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __snake_case ( unittest.TestCase):
"""simple docstring"""
def __lowercase ( self : Tuple ) -> Dict:
... | 89 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
A =logging.get_logger(__name__)
class _a ( lowercase_ ):
def __init__( self : str , *lowercase : List[str] , **lowercase : Union[str,... | 34 |
'''simple docstring'''
class __lowerCAmelCase : # Public class to implement a graph
'''simple docstring'''
def __init__(self : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[bool]] ):
'''simple ... | 2 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : List[Any] , *UpperCAmelCase_ : str , **U... | 169 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( _UpperCAmelCase ):
A__ : Any =(CMStochasticIterativeScheduler,)
A__ : Optional[int] =1_0
def A_ ( self : Dict , ... | 169 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase : List[str] = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
... | 124 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: List[str] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_M... | 276 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = 9, 14 # noqa: F841
SCREAMING_SNAKE_CASE : Dict = [
[0, 1, 4]... | 258 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.con... | 258 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def __UpperCAmelCase ( lowercase="ro" ,lowercase="en" ,lowercase="wmt16" ,lowercase=None ):
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
rai... | 289 |
from math import isqrt, loga
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> list[int]:
__lowercase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , ... | 325 | 0 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm... | 365 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmel... | 340 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def SCREAMING_SNAKE_CASE__ ( __A ) -> Dict: # picklab... | 42 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase = logging.... | 89 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE : int = TypeVar("T")
class _lowerCamelCase( Generic[T] ):
lowercase_ : deque[T] # Cache store of keys
lowercase_ : set[T] # R... | 84 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE : str = logging.getLogger()
def ... | 84 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : Optional[Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json"... | 169 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCa... | 169 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 355 |
_UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :dict[int, list[int]] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CAS... | 232 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __UpperCAm... | 258 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class _lowerCAmelCase ( tf.keras.layers.Layer ):
d... | 355 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__snake_case = logging.getLogger()
@unittest.skip('''Temporarily... | 112 | 0 |
from manim import *
class __lowerCAmelCase ( UpperCamelCase__):
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
a__ : List[Any] =Rectangle(height=0.5 , width=0.5 )
... | 95 |
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 ):
a_ ... | 340 | 0 |
import numpy as np
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
return np.where(vector > 0 , __lowerCamelCase , (alpha * (np.exp(__lowerCamelCase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 197 | from math import sqrt
def lowerCAmelCase( __lowerCamelCase ):
__a = 0
for i in range(1 , int(sqrt(__lowerCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__lowerCamelCase ):
total += i + n // i
elif i == sqrt(__lowerCam... | 197 | 1 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {name: getattr(tra... | 84 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_C... | 84 | 1 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
a_ = '.'
if __name__ == "__main__":
a_ = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
a_ ... | 222 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlip... | 222 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 258 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCAmelCase_ : int = TypeVar('''_T''')
class _SCREAMING_SNAKE_CASE ( Generic[_T] ):
def __init__( self : Dict , __lowerCamelCase : Iterable[_T] | None = None ):
UpperCamelCase :list[_T... | 62 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not... | 62 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : List[Any] = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if ... | 324 |
'''simple docstring'''
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_fast import BertTokenizerFast
from .tokenization_dpr import DP... | 112 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 137 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=A_ ):
UpperCAmelCase__ : Union[str, Any] = ["onnx"]
def __init__(self : Tuple , *_A : Optional[int] , **_A : Any ) -> Dict:
... | 137 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 197 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _A :
def A__ ( self , __lowerCAmelCase ):
"""simple docstri... | 197 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ = 1_00_00_00 ) -> int:
__lowerCamelCase = 1
__lowerCamelCase = 1
__lowerCamelCase = {1: 1}
for inputa in range(2 , UpperCamelCase__ ):
__lowerCamelCase = 0
... | 237 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> float:
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' ... | 237 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase ... | 222 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAIN... | 222 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __SCREAMING_SNAKE_CASE ( unitte... | 361 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__a = 50_00_00
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get... | 173 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_A = logging.get_logger(__name__)
_A = {'vocab_fi... | 62 |
from __future__ import annotations
import math
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ ) -> None:
__UpperCamelCase =size
# approximate the overall size of segment tree with given value
__UpperCame... | 62 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : List[str] = logging.get_logger(__name__)
lowerCamelCase_ : str = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/conf... | 223 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCamelCa... | 223 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
... | 137 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _snake_case ( A__ ):
_lowercase : int = '''Speech2TextFeatureExtractor'''
_lowercase : List[Any] = '''Speech2TextTokenizer'''
def __init__( ... | 137 | 1 |
"""simple docstring"""
class UpperCamelCase : # Public class to implement a graph
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Any = row
_lowercase : Union[str, Any] ... | 369 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@r... | 237 |
'''simple docstring'''
import functools
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : str ):
A__ = len(_lowerCamelCase )
A__ = len(_lowerCamelCase )
@functools.cache
def min_distance(_lowerCamelCase :... | 237 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ( __magic_name__ ,... | 364 |
"""simple docstring"""
import unittest
import numpy as np
import requests
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_inp... | 298 | 0 |
import numpy
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : numpy.ndarray , lowerCAmelCase_ : numpy.ndarray ):
"""simple docstring"""
_A: Dict = input_array
# Random i... | 121 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_UpperCAmelCase = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMN... | 173 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 350 |
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
if n_term == "":
return []
a :list = []
for temp in range(int(UpperCAmelCase_ ) ):
series.append(F'''1/{temp + 1}''' if series else '''1''' ... | 281 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .toke... | 223 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int = 10_00 ):
lowercase_ , lowercase_ :str = 1, 1
lowercase_ :Any = 2
while True:
lowercase_ :str = 0
lowercase_ :Tuple = fa + fa
lowercase_ ,... | 223 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require... | 42 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip... | 42 | 1 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 77 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
UpperCAmelCase : str = [randint(-1_000 , 1_000 ) for i in range(10 )]
UpperCAmelCase : Any = randint(-5_... | 336 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Optional[int] = ['image_processor', 'tokenizer']
_SCREAMING_SNAKE_CASE : Tupl... | 199 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class a__ ... | 199 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case : Dict = logging.getLogger(__name__)
if __name__ == "__ma... | 248 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_c... | 298 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
... | 351 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1_024 , __lowerCAmelCase=1_024... | 322 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {}
class A ( __UpperCAmelCase ):
__snake_case = 'llama'
__snake_case = ['past_key_values']
def __init__( self, UpperCamelCase__=3_2000, Up... | 278 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice m... | 281 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( A : list[int | str] ):
'''simple docstring'''
create_state_space_tree(A , [] , 0 , [0 for i in range(len(A ) )] )
def lowerCamelCase__ ( A ... | 354 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase__:
__magic_name__ : int
__magic_name__ : TreeNode | None = None
__magic_name__ : TreeNode | None = None
... | 91 | 0 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/... | 42 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 | 1 |
import torch
from torch import nn
class A__ ( nn.Module ):
def __init__( self : Optional[int] , a : Union[str, Any] , a : str , a : str , a : List[Any] , a : List[Any]=1 , a : Tuple=False ):
... | 307 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_... | 307 | 1 |
def a_ ( SCREAMING_SNAKE_CASE__ : list ):
'''simple docstring'''
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ ... | 199 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCam... | 199 | 1 |
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "philschmid/bart-large-cnn-samsum"
lowercase__ = (
"This is a to... | 358 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 132 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolv... | 299 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_p... | 322 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = ... | 357 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAM... | 65 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ( ) -> tuple[list[int], int]:
__snake_case = [randint(-1000 , 1000 ) for i in range(10 ... | 24 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = (PNDMScheduler,)
__UpperCamelCase = ... | 91 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model ... | 356 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documenta... | 75 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_commo... | 307 |
def a_ ( _A , _A ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) =... | 307 | 1 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSchedu... | 168 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> np.ndarray:
# prepare kernel
# the kernel siz... | 168 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def UpperCamelCase ( _lowerCAmelCase : Any, _lowerCAmelCase : Optional[Any] ) -> Generator[tuple[str, ...], None, None]:
_UpperCAmelCase : List[Any] = iter(__l... | 246 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Generator[tuple[str, ...], None, None]:
SCREAMING_SNAKE_CASE__ : List[Any] = iter(__lowerCAmelCase... | 132 | 0 |
from math import factorial
def __UpperCAmelCase ( __a : int ,__a : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(__a ... | 15 |
from __future__ import annotations
def __UpperCAmelCase ( __a : list ) -> float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(__a ) / len(__a )
if __name__ == "__main__":
import do... | 15 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Optional[Any] = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise Optio... | 27 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface ... | 65 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> int:
A: Any = len(__lowercase ), len(grid[0] )
if (
min(__lowercase , __lowercase ) < 0
... | 371 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import Tokeniz... | 334 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def A_ ( _lowercase, _lowercase, _lowercase = 10**-10 ):
'''simple docstring'''
snake_case_ :List[str] = a
while True:
... | 66 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 75 | 0 |
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_... | 221 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase = '__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase = 'Dummy User'
UpperCamelCase = 'hf_hZEmnoOEY... | 221 | 1 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
a_ : int = loggi... | 168 |
'''simple docstring'''
import os
import re
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_ : Optional[Any] = log... | 168 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A(__a: Optional[Any] , __a: int , __a: List[str] , __a: int=5 ):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask>" ) =... | 22 |
def A(__a: Tuple ):
lowerCAmelCase_ = len(__a )
while cur > 1:
# Find the maximum number in arr
lowerCAmelCase_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCAmelCase_ = arr[mi::-1] + arr[mi + 1 : len(__a )]
# Reve... | 22 | 1 |
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