code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def _lowercase ( ) -> list[list[int]]:
return [list(range(1_000 - i ,-1_000 - i ,-1 ) ) for i in range(1_000 )]
__snake_case : Optional[int] = generate_large_matrix()
__snake_case : Union[str, Any] = (
[[4, ... | 269 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Optional[int] = logging.get_logger(__name__)
class... | 269 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__snake_case : Dict = False
__snake_case : str = True
__snake_case : ... | 269 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageP... | 269 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-c... | 269 | 1 |
"""simple docstring"""
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 : int = logging.getLo... | 269 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[Any] = [8, 5, 9, 7]
__snake_case : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
... | 269 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case : int = {
'configuration_efficientformer': [
... | 269 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --npr... | 269 | 1 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 269 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 269 | 1 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowercase ( __snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__snake_case ,__snake_case ):
... | 269 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> float:
if digit_amount > 0:
return round(number - int(__snake_case ) ,__snake_case )
return number - int(__snake_case )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
pr... | 269 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = ... | 269 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'torchsde']
def __init__( self: int , *_SCREAMING_SNAK... | 269 | 1 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_do... | 269 |
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 )... | 269 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__snake_case : Optional[int] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
... | 269 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ... | 269 | 1 |
"""simple docstring"""
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
__snake_case : Optional[Any] ... | 269 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from .... | 269 | 1 |
"""simple docstring"""
__snake_case : List[str] = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip ... | 269 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__snake_case : Optional[int] = 50_000
__snake_case : Dict = 5_000
__snake_case , __snake_case : Union[st... | 269 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
... | 269 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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_modeli... | 269 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch... | 269 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __snake_case = "base_exp.txt" ) -> int:
__lowerCAmelCase : float = 0
__lowerCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 269 | 1 |
"""simple docstring"""
import math
import qiskit
def _lowercase ( __snake_case = 1 ,__snake_case = 1 ,__snake_case = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__snake_case ,__snake_case )
or isinstance(__snake_case ,__snake_case )
... | 269 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 269 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class A__ ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE: Any) -> Optional[Any]:
... | 269 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = ... | 269 | 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
_... | 269 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 | 1 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __snake_case = "base_exp.txt" ) -> int:
__lowerCAmelCase : float = 0
__lowerCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 269 |
"""simple docstring"""
import re
def _lowercase ( __snake_case ) -> str:
if len(re.findall("[ATCG]" ,__snake_case ) ) != len(__snake_case ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" ,"TAGC" ) )
if __name__ == "_... | 269 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from .... | 269 |
"""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 Toke... | 269 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
fro... | 269 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s... | 269 | 1 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWr... | 269 |
"""simple docstring"""
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, TrainingJobAnaly... | 269 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from... | 269 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__snake_case : Optional[int] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
... | 269 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case : List[str] = loggin... | 269 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Optional[int] = logging.get_logger(__name__)
class... | 269 | 1 |
"""simple docstring"""
import string
def _lowercase ( __snake_case ) -> str:
__lowerCAmelCase : Any = ""
for i in sequence:
__lowerCAmelCase : Dict = ord(__snake_case )
if 65 <= extract <= 90:
output +=... | 269 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 | 1 |
"""simple docstring"""
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__snake_case : Union[str, Any] = Mapping[str, np.ndarray]
__snake_case... | 269 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-c... | 269 | 1 |
"""simple docstring"""
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, T... | 269 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[Any] = [8, 5, 9, 7]
__snake_case : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
... | 269 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[Any] = {
'configuration_vision_encoder... | 269 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --npr... | 269 | 1 |
"""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 Toke... | 269 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 269 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowercase ( __snake_case ) -> List[str]:
def wrapper(*__snake_case ,**__snake_case ):
... | 269 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> float:
if digit_amount > 0:
return round(number - int(__snake_case ) ,__snake_case )
return number - int(__snake_case )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
pr... | 269 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ) -> list[int]:
__lowerCAmelCase : Union[str, Any] = [True] * limit
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Any ... | 269 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'torchsde']
def __init__( self: int , *_SCREAMING_SNAK... | 269 | 1 |
"""simple docstring"""
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:RE... | 269 |
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 )... | 269 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE ... | 269 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ... | 269 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
i... | 269 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from .... | 269 | 1 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _lowercase ( __snake_case ) -> List[str]:
__lowerCAmelCase : int = [
"decode... | 269 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__snake_case : Optional[int] = 50_000
__snake_case : Dict = 5_000
__snake_case , __snake_case : Union[st... | 269 | 1 |
"""simple docstring"""
import socket
def _lowercase ( ) -> Optional[int]:
__lowerCAmelCase : Union[str, Any] = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
__lowerCAmelCase : Optional[int] = socket.gethostname()
... | 269 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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_modeli... | 269 | 1 |
"""simple docstring"""
import random
from typing import Any
def _lowercase ( __snake_case ) -> list[Any]:
for _ in range(len(__snake_case ) ):
__lowerCAmelCase : int = random.randint(0 ,len(__snake_case ) - 1 )
__lowerCAmelCase :... | 269 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __snake_case = "base_exp.txt" ) -> int:
__lowerCAmelCase : float = 0
__lowerCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 269 | 1 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ... | 269 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 269 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Any = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resol... | 269 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = ... | 269 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Optional[Any] = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/conf... | 269 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__snake_case : Union[str, Any] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Con... | 269 |
"""simple docstring"""
import re
def _lowercase ( __snake_case ) -> str:
if len(re.findall("[ATCG]" ,__snake_case ) ) != len(__snake_case ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" ,"TAGC" ) )
if __name__ == "_... | 269 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsC... | 269 |
"""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 Toke... | 269 | 1 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> int:
def update_area_of_max_square(__snake_case ,__snake_case ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__lowerCAmelCase : ... | 269 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s... | 269 | 1 |
"""simple docstring"""
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 tra... | 269 |
"""simple docstring"""
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, TrainingJobAnaly... | 269 | 1 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> np.ndarray:
__lowerCAmelCase : Optional[int] = np.array(__snake_case )
if arr.shape[0] != arr.shape[1]:
ra... | 269 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__snake_case : Optional[int] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
... | 269 | 1 |
"""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... | 269 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Optional[int] = logging.get_logger(__name__)
class... | 269 | 1 |
"""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 _lowercase ( __snake_case ) -> L... | 269 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 | 1 |
"""simple docstring"""
__snake_case : List[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface... | 269 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-c... | 269 | 1 |
"""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
__snake_case : int = logging.get_log... | 269 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[Any] = [8, 5, 9, 7]
__snake_case : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
... | 269 | 1 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
... | 269 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --npr... | 269 | 1 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionCon... | 269 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 269 | 1 |
"""simple docstring"""
def _lowercase ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 ,999 )
for b in range(__snake_case ,999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{so... | 269 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> float:
if digit_amount > 0:
return round(number - int(__snake_case ) ,__snake_case )
return number - int(__snake_case )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
pr... | 269 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'torchsde']
def __init__( self: int , *_SCREAMING_SNAK... | 269 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'torchsde']
def __init__( self: int , *_SCREAMING_SNAK... | 269 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'configuration_r... | 269 |
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 )... | 269 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 269 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ... | 269 | 1 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from .... | 269 | 1 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__snake_case : Opt... | 269 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__snake_case : Optional[int] = 50_000
__snake_case : Dict = 5_000
__snake_case , __snake_case : Union[st... | 269 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ) -> list[list[int]]:
__lowerCAmelCase : list[list[int]] = []
__lowerCAmelCase : list[int] = []
__lowerCAmelCase ... | 269 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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_modeli... | 269 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import Sche... | 269 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __snake_case = "base_exp.txt" ) -> int:
__lowerCAmelCase : float = 0
__lowerCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 269 | 1 |
"""simple docstring"""
import datasets
__snake_case : List[str] = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samu... | 269 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 269 | 1 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s... | 269 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = ... | 269 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Config... | 269 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[Any] = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/... | 269 |
"""simple docstring"""
import re
def _lowercase ( __snake_case ) -> str:
if len(re.findall("[ATCG]" ,__snake_case ) ) != len(__snake_case ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" ,"TAGC" ) )
if __name__ == "_... | 269 | 1 |
"""simple docstring"""
import sys
__snake_case : Any = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522744... | 269 |
"""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 Toke... | 269 | 1 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Tuple ... | 269 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s... | 269 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
Musi... | 269 |
"""simple docstring"""
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, TrainingJobAnaly... | 269 | 1 |
"""simple docstring"""
from __future__ import annotations
__snake_case : int = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class A__ :
'''... | 269 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__snake_case : Optional[int] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
... | 269 | 1 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@registe... | 269 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Optional[int] = logging.get_logger(__name__)
class... | 269 | 1 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, l... | 269 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class A__ ( unittest.Te... | 269 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-c... | 269 | 1 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__snake_case : Dict = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and ... | 269 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[Any] = [8, 5, 9, 7]
__snake_case : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
... | 269 | 1 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
de... | 269 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --npr... | 269 | 1 |
"""simple docstring"""
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... | 269 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 269 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler... | 269 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> float:
if digit_amount > 0:
return round(number - int(__snake_case ) ,__snake_case )
return number - int(__snake_case )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
pr... | 269 | 1 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration... | 269 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'torchsde']
def __init__( self: int , *_SCREAMING_SNAK... | 269 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case : Any = logging.get_logger(__name__)
__snake_case : Optional[Any] = {
'SenseTime/defor... | 269 |
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 )... | 269 | 1 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ... | 269 | 1 |
"""simple docstring"""
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 A__ ( __SCREAMING_SN... | 269 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from .... | 269 | 1 |
"""simple docstring"""
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A__ :
'''simple docstring'''
... | 269 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__snake_case : Optional[int] = 50_000
__snake_case : Dict = 5_000
__snake_case , __snake_case : Union[st... | 269 | 1 |
"""simple docstring"""
__snake_case : List[str] = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-build... | 269 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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_modeli... | 269 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def _a ( a :Union[str, Any] ) -> Optional[int]:
a = ... | 0 |
"""simple docstring"""
import os
from math import logaa
def _lowercase ( __snake_case = "base_exp.txt" ) -> int:
__lowerCAmelCase : float = 0
__lowerCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 269 | 0 |
'''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 __A ( UpperCamelCase__ , unitt... | 1 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 269 | 0 |
'''simple docstring'''
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 ... | 2 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = ... | 269 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Dict = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/con... | 3 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 4 |
"""simple docstring"""
import re
def _lowercase ( __snake_case ) -> str:
if len(re.findall("[ATCG]" ,__snake_case ) ) != len(__snake_case ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" ,"TAGC" ) )
if __name__ == "_... | 269 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
_lowercase =iter(__snake_case )
while True:
_lowercase ... | 5 |
"""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 Toke... | 269 | 0 |
from itertools import product
def __lowerCAmelCase ( a__ , a__ ) -> list[int]:
__a = sides_number
__a = max_face_number * dice_number
__a = [0] * (max_total + 1)
__a = 1
__a = range(a__ , max_face_number ... | 6 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s... | 269 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, Bonnie and\n... | 7 |
"""simple docstring"""
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, TrainingJobAnaly... | 269 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
... | 8 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__snake_case : Optional[int] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
... | 269 | 0 |
from math import ceil
def _UpperCamelCase ( lowercase__ = 1001 ):
__SCREAMING_SNAKE_CASE : Dict = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__SCREAMING_SNAKE_CASE : Optional[Any] = 2 * i + 1
__SCREAMI... | 9 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Optional[int] = logging.get_logger(__name__)
class... | 269 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter... | 10 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class A__ :
'''simple docstring'''
def __init__( self: str) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] ... | 269 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = '▁'
lowerCAmelCas... | 11 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-c... | 269 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accel... | 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[Any] = [8, 5, 9, 7]
__snake_case : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
... | 269 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availa... | 13 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --npr... | 269 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class UpperCamelCas... | 14 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluati... | 269 | 0 |
def UpperCAmelCase ( a_ , a_ ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__A = str(bin(a_ ) )[2:] # remove the leading "0b"
__A = str(bin(a_ ) )[2:] # remove t... | 15 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> float:
if digit_amount > 0:
return round(number - int(__snake_case ) ,__snake_case )
return number - int(__snake_case )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
pr... | 269 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A ( metaclass=A_ ):
'''simple docstring'''
lowerCAmelCase : Tuple = ["sentencepiece"]
def __init__( self : str ,*_snake_case :... | 16 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'torchsde']
def __init__( self: int , *_SCREAMING_SNAK... | 269 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _... | 17 |
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 )... | 269 | 0 |
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_roberta import RobertaTokenizer
__low... | 18 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] ... | 269 | 0 |
import random
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ):
lowerCamelCase_ = {i: [] for i in range(lowerCamelCase__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete... | 19 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from .... | 269 | 0 |
lowercase : Union[str, Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def _snake_case( ) -> None:
lowercase : Tuple = input("""Enter message: """ )
lowercase : Dict = input("""Enter key [alphanumeric]: """ )
lowercase ... | 20 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__snake_case : Optional[int] = 50_000
__snake_case : Dict = 5_000
__snake_case , __snake_case : Union[st... | 269 | 0 |
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