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 |
|---|---|---|---|---|
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase_ = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCAmelCase__ ):
"""si... | 303 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
def __magic_name__ ( __snake_case : Union[str, Any] = 50 ) -> int:
lowercase : str = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_... | 202 | """simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Dict = {"""vocab_... | 99 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 69 | 0 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
A_ : Optional[Any] = get_tests_dir('fixtures/test_sent... | 333 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
from functools import reduce
_snake_case : Tuple = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244... | 123 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are ... | 48 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
import os
def A_ ( _UpperCAmelCase = "matrix.txt" ):
with open(os.path.join(os.path.dirname(_UpperCAmelCase ) , _UpperCAmelCase ) ) as in_file:
SCREAMING_SNAKE_CASE_: Union[str, Any] = in_file.read()
SCREAMING_SNAKE_CASE_: int = [[int(_UpperC... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""facebook/data2vec-... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> list[int]:
if num <= 0:
_a : List[Any] =F"{num}: Invalid input, please enter a positive integer."
raise ... | 276 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 250 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ... | 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 |
def a__ ( snake_case , snake_case ):
"""simple docstring"""
_validate_point(snake_case )
_validate_point(snake_case )
if len(snake_case ) != len(snake_case ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(a - b ... | 303 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is... | 202 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class A__ ( lowerCAmelCase__ ):
"""simple docstring"""
@require_torch
def __lowercase ( self) -> ... | 99 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
A_ : int = logging.get_logger(__name__)
class A_ ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self , *lowercase__ , **lowe... | 333 | """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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | 0 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : Union[str, Any] = OmegaConf.load(__lowerCamelCase )
... | 123 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
SCREAMING_SNAKE_CASE__ : Dict = 50000
SCREAMING_SNAKE_CASE__ : int = 5000
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = os.p... | 48 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optio... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
import os
def lowerCAmelCase__ ( ):
"""simple docstring"""
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + """/p022_names.txt""" ) as file:
__a = str(file.readlines()[0] )
__a = names.replace("""\"""" , """""" ).split(""",""" )
... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can qu... | 276 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,... | 250 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
import math
class A__ :
"""simple docstring"""
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] ) -> int:
"""simple docstring"""
... | 145 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaToken... | 303 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSD... | 202 | """simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
fro... | 99 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[Any] = {
'configuration_bert': ... | 333 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
from __future__ import annotations
class a :
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : List[Any] , lowerCamelCase : Dict ) -> str:
__snake_case , __snake_case : Dict = text, pattern
_... | 123 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
def A ( _SCREAMING_SNAKE_CASE ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
lowerCamelCase : Dict = sorted(string.lower() )
return len(_SCREAMING_SNAKE_... | 48 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
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 SchedulerMixin
@dataclass
# Copied from diffusers.sc... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
__lowerCamelCase : ... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[int] ) -> bool:
return str(_UpperCAmelCase ) == str(_UpperCAmelCase )[::-1]
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ) -> int:
return int(_UpperCAmelCase ) + int(... | 276 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def _A ( ) -> Generator[int, None, None]:
_lowercase : List[Any] = {}
_lowercase : List[str] = 2
while True:
_lowercase : List[str] = factor_ma... | 250 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 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 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/co... | 303 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
def __magic_name__ ( __snake_case : str ) -> int:
lowercase : Any = [[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowercase : str = 1
for n in ra... | 202 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 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
lowercase : Any = logging.get_logger(__name__)
lowercase ... | 99 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : List[Any] = 'src/transformers'
# This is to make sur... | 333 | """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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_snake_case : List[str] = logging.get_logger(__name__)
class a (lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : str , *lowerCamel... | 123 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
import qiskit
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> qiskit.result.counts.Counts:
lowerCamelCase : List[str] = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
lowerCamelCase : ... | 48 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : Tuple):
SCREAMING_SNAKE_CASE_: Dict = data
SCREAMING_S... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
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
@require_torch_gpu
class ... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Optional[Any] ) -> float:
return math.sqrt(sum(pow(a - b ,2 ) for a, b in... | 276 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_torch_available():... | 250 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
import math
def __UpperCAmelCase ( a_: List[str], a_: Any ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(a_ )
else:
if x == 0: # 0 raised to any number is 0
... | 145 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transformer/... | 303 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
_A : str = logging.get_logger(__name__) # pylint: disable=invalid-name
cla... | 202 | """simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
import socket
def A_ ( ) -> List[str]:
a__ : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
a__ : Dict = socket.gethostname()
a__ : List[str] = 1_2312
sock.connect((host, port) )
sock.send(B'Hello server!' )
with o... | 99 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 69 | 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 A_ ( lowerCAmelCase__ ):
'''simple d... | 333 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
de... | 123 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFI... | 48 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] = len(_UpperCAmelCase ), len(grid[0] )
if (
min(_UpperCAmelCase , _UpperCAmelCase ) < 0
or ro... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
# See a... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.t... | 276 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
'''simple docstring'''
import torch
from transformers import AutoModel
class a__ ( torch.nn.Module ):
def __init__( self , _UpperCamelCase="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
super(lowerCAmelCase__ , self ).__init__()
_lowercase : ... | 250 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCAmelCase ( ):
_UpperCAmelCase : int = HfArgumentParser(a_ )
_UpperCAmelCase : Optional[int] = parser.parse_args_into_dataclasses()[0]
... | 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 |
def a__ ( snake_case ):
"""simple docstring"""
return "".join([hex(snake_case )[2:].zfill(2 ).upper() for byte in list(snake_case )] )
def a__ ( snake_case ):
"""simple docstring"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
i... | 303 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is... | 202 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modelin... | 99 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 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
A_ : Optional[int] = logging.get_logger(__name__)
A_ : str = {
'f... | 333 | """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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_snake_case : Tuple = datasets.utils.logging.get_logger(__name__)
class a (folder_based_builder.FolderBasedBuilderConfig ):
"""simple d... | 123 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE__ : Optional[int] = ['small', 'medium', 'large']
SCREAMING_SNAKE_CASE__ : Any = 'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE__ : List[str] = 'lm_he... | 48 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase : Dict = [fil... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
import functools
from typing import Any
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or len(_SCREAMING_SNAKE_CASE ) == 0:
ra... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
'''simple docstring'''
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A__ ( lowerCAmelCase__ ):
__UpperCamelCase : Optional[int] = "facebook/bart-large-mnli"
... | 276 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
from math import factorial
def _A ( snake_case = 20 ) -> int:
_lowercase : Any = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_lowercase : Dict = n // 2
return int(factorial(snake_case ... | 250 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
Upp... | 145 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContext... | 303 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def __magic_name__ ( __snake_case : Optional[int] ) -> Callable:
@wraps(__snake_case )
def _inner_fn(*__snake_case : List[str] , **__snake_case : ... | 202 | """simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
from __future__ import annotations
lowercase : Any = list[list[int]]
# assigning initial values to the grid
lowercase : List[Any] = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9... | 99 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 69 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ... | 333 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
import sys
from collections import defaultdict
class a :
"""simple docstring"""
def __init__( self : Optional[int] ) -> Optional[int]:
__snake_case : List[str] = []
def __snake_case ( self : Union[str, Any] , lower... | 123 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def A ( _SCREAMING_SNAKE_CASE="ro" ,_SCREAMING_SNAKE_CASE="en" ,_SCREAMING_SNAKE_CASE="wmt16" ,_SCREAMING_SNAKE_CASE=None ) -> None:
try:
import datasets
except (ModuleNotFoundError, ImportError)... | 48 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
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 ...... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowerCamelCase__ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25,
"""L""": ... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Union[str, Any] = logging.get_logger(__name__)
A__: Union[str, Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcod... | 276 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a__ :
pass
| 250 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: Union[str, Any], a_: Dict ):
if not (isinstance(a_, a_ ) and isinstance(a_, a_ )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
_UpperCAmelCase : List[str] ... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""... | 303 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_A : Dict = ... | 202 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( lowerCAmelCase__ ):
... | 69 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def A_ ( A__ , A__=1 ) -> Any:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return... | 99 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 69 | 0 |
A_ : List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : List[Any] = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def ... | 333 | """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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import s... | 123 | """simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ) -> int:
snake_case_ = HfArgumentParser(UpperCAmelCase )
snake_case_ = parser.parse_args_into_dataclasses()[0]
... | 69 | 0 |
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
while a != 0:
lowerCamelCase , lowerCamelCase : Dict = b % a, a
return b
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
if gcd(_SCRE... | 48 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ) -> None:
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def UpperCAmelCase ( UpperCAmel... | 69 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 69 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wav... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Re... | 69 | 0 |
'''simple docstring'''
import argparse
A__: Any = '''docs/source/_static/js/custom.js'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> int:
with open(_UpperCAmelCase ,encoding="""utf-8""" ,newline="""\n""" ) as f:
_a : O... | 276 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 0 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _A ( snake_case , snake_case , snake_case ) -> List[str]:
_lowercase : Dict = AutoConfig.from_pretrained(snake_case )
_lowe... | 250 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import re... | 145 | """simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Reformer... | 303 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCamelCase :
def __init__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = data
snake_case_ ... | 69 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A : Optional[int] = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfi... | 202 | """simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This ... | 69 | 0 |
lowercase : Optional[Any] = range(2, 2_0 + 1)
lowercase : List[str] = [1_0**k for k in range(ks[-1] + 1)]
lowercase : Union[str, Any] = {}
def A_ ( A__ , A__ , A__ , A__ ) -> Optional[int]:
a__ : Optional[Any] = ... | 99 | """simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 69 | 0 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import tes... | 333 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : Tuple = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
... | 123 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuratio... | 69 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Optional[int] = TypeVar('T')
class UpperCamelCase__ (Generic[T] ):
'''simple docstring'''
lowerCamelCase_ : Tuple... | 48 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaP... | 69 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.tes... | 13 | """simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> list:
if len(UpperCAmelCase ) <= 1:
return [tuple(UpperCAmelCase )]
snake_case_ = []
def generate(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = ... | 69 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
def __init__( self : Optional[int] , __lowercase : Any , __lowercase ... | 302 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.token... | 69 | 0 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
... | 276 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 250 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 | 0 |
'''simple docstring'''
import sys
from pathlib import Path
__a = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import d... | 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 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowercase_ = abspath(join(dirname(dirname(dirname(__file__))), """src... | 303 | """simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 | 0 |
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