code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( snake_case__ : Optional[int] , snake_case__ : int ) -> int:
U... | 40 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PAR... | 41 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['flax']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> ... | 42 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 0 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = ''''''
for word_or_phrase in separated:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise Exception('''join() accepts only strings to be join... | 43 |
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,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
_lowerCamelCase : Optional[int] = [0] * (upper_limit + 1)
# Base case: C... | 44 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 0 |
def A ( lowercase__ : List[Any] , lowercase__ : str ) -> List[str]:
UpperCamelCase__ :int = 0
UpperCamelCase__ :Any = len(lowercase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sor... | 45 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbon... | 46 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def UpperCAmelCase__ ( lowerCamelCase_ : float ):
if num <= 0:
raise ValueError('math domain error' )
return quad(lowerCamelCase_ , 0 , lowerCamelCase_ , args=(lowerCamelCase_... | 47 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 0 |
'''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:README.md", "dataset_in... | 48 |
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... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Dict = {'configuration_fnet': ['FNET_PRETRA... | 49 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 0 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPToke... | 50 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 0 |
'''simple docstring'''
from __future__ import annotations
a__ : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
'''simple doc... | 51 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1... | 86 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLP... | 52 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 0 |
import heapq
import sys
import numpy as np
_snake_case : Any = tuple[int, int]
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : str ) -> str:
__lowerCAmelCase = []
__lowerCAmelCase = set()
... | 53 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 0 |
from PIL import Image
def UpperCAmelCase ( a_ , a_ ) -> Image:
"""simple docstring"""
def brightness(a_ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 ... | 55 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_comm... | 56 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 0 |
A_ : Dict = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A_ : Optional[Any] = ['a', 'b', 'c', 'd', 'e']
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
UpperCam... | 57 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int = 1_0_0_0 ):
'''simple docstring'''
snake_case_ : List[str] = 2**power
snake_case_ : List[Any] = 0
while n:
snake_case... | 58 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the docu... | 59 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
defau... | 60 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resol... | 61 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 86 | 0 |
from __future__ import annotations
snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE... | 62 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 0 |
from __future__ import annotations
from typing import TypedDict
class a ( lowercase__ ):
"""simple docstring"""
a : str
a : int
def lowerCamelCase__ ( __lowerCamelCase : str ):
if not isinstance(__lowe... | 63 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface... | 64 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 0 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = ... | 65 |
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,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( __snake_case ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
warnings.war... | 66 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Dict , snake_case__ :int , snake_case__ :List[str] , snake_case__ :int ) -> int: # noqa: E741
while r - l > 1:
_lowercase = (l + r) // 2
if v[m] >= key:
_l... | 67 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import... | 68 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> Tuple:
__snake_case = 1
__snake_case = 2
while i * i <= n:
__snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
... | 69 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 0 |
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : int ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowercase ) , lowercase )
return number - int(lowercase )
if __name__ == "__main__... | 70 |
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... | 86 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : tuple[int, int] , _SCREAMING_SNAKE_CASE : int ) -> list[tuple[int, int]]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] ... | 71 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 0 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-... | 72 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 0 |
import numpy as np
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1e-12 , _UpperCAmelCase = 100 , ):
assert np.shape(_UpperCAmelCase)[0] == np.shape(_UpperCAmelCase)[1]
# Ensure proper dimensionality.
assert np.shape(_UpperC... | 73 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1... | 86 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class __UpperCamelCase ( lowerCAmelCase__ ... | 74 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 0 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
... | 75 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 0 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "x" , __UpperCamelCase = 10**-10 , __UpperCamelCase = 1 , ):
__lower... | 76 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 0 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A = 6378137.0
A = 6356752.314245
A = 6_378_137
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , ... | 77 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_avail... | 78 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = """T5Config... | 79 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 0 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase ) + 1
__lowercase = len(lowerCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string ma... | 80 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 0 |
_snake_case : Optional[int] = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_snake_case : Dict = ["a", "b", "c", "d", "e"]
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case ... | 81 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
rais... | 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def snake_case_ ( A_ : Optional[int] ):
... | 83 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 86 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 84 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 0 |
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_torch_available, is_vision_available
fr... | 85 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 0 |
_lowerCamelCase : Optional[int] = {
"""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""",
... | 87 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class lowercase__ :
__UpperCAmel... | 88 |
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,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 0 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( self) -> Optional[Any]:
"""... | 89 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 0 |
'''simple docstring'''
import heapq
import sys
import numpy as np
__UpperCAmelCase = tuple[int, int]
class a__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
lowerCAmelCase__... | 90 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_token... | 91 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : str ) -> str:
lowercase : Dict =0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase : Any =''''''
lowercase : Optional[int] =''''''
# ap... | 92 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from trans... | 93 |
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... | 86 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loade... | 94 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 0 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelera... | 95 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.j... | 96 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1... | 86 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def a ( snake_case__: Optional[int] ):
'''simple docstring'''
lowercase_ = test_file.split(os... | 97 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : Tuple = {
'vocab_file': 'vocab.j... | 98 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 0 |
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
raise ValueError("""max_weight must greater than zero.""" )
... | 99 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 0 |
def __snake_case ( lowerCAmelCase_ ) -> list:
if n_term == "":
return []
SCREAMING_SNAKE_CASE__ = []
for temp in range(int(lowerCAmelCase_ ) ):
series.append(f'''1/{temp + 1}''' if series else '''1''' )
return series
if __name__ == "__main__":
... | 100 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Dict =logging.get_logger(__name__)
class __lowercase (__SCREAMING_SNAKE_CASE ):
... | 101 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 0 |
"""simple docstring"""
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
while second != 0:
UpperCamelCase : List[str] = first & second
first ^= second
UpperCamelCase : Optional[int] = c << 1... | 102 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, s... | 103 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_a... | 104 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 0 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import... | 106 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 86 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
_UpperCAmelCase : Dict = '''src/transformers'''
# Matches is_xxx_available()
_UpperCAmelCase : Optional[int] = re.compile(r'''is\_([a-z_]*)_available()''')
# C... | 107 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case = 2_0_0 ) -> int:
_UpperCAmelCase = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
_UpperCAmelCase = [0] * (pence + 1)
_UpperCAmelCase = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 108 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 0 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers impo... | 109 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'facebook/xmod-base': '... | 110 |
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,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Bli... | 437 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
a_ : Any = logging.get_logger(__name__)
a_ : Tuple = 'T5Config'
class __UpperCamelCase ( snake_case_ ):
... | 194 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See a... | 321 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 0 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_va... | 42 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_i... | 342 |
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... | 86 | 0 |
from __future__ import annotations
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = order
# a_{0} ... a_{k}
UpperCAmelCase__ : Optional[int] = [1.0]... | 79 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase (metaclass=snake_case_ ):
"""simple docstring"""
lowerCamelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : str , *__magic_name__ : int , **__magic_name__ : List[Any... | 140 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE = TypeVar('_T')
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , __A = None ):
__a = list(iter... | 99 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1... | 86 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
... | 69 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
i... | 86 | 0 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 118 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wav... | 86 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 500 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 0 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class A_(snake_case_ ):
"""simple docstring"""
def __init__( self , *A , **A ):
super().__init__(*A , **A )
def _lowerCAmelCase ( self , A ,... | 437 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 86 | 0 |
def __a ( __UpperCAmelCase ):
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def __a ( __UpperCAmelCase ):
a__ = credit_card_number
a__ = 0
a__ = len(__UpperCamelCase ) - 2
for i i... | 194 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExt... | 86 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( snake_case_ ):
"""simple docstring"""
__lowerCAmelCase = (DDPMParallelScheduler,)
def snake_case__ ( self , **lowerCAmelCase_ ):
... | 321 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ) -> Any:
try:
lowerCamelCase_ = float(__UpperCamelCase )
except ValueError:
raise ValueError('Please enter a valid number' )
lowerCamelCase_ = decimal - int(__UpperCamelCase )
if... | 42 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
E... | 86 | 0 |
'''simple docstring'''
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = [0] * len(__UpperCamelCase )
snake_case = []
snake_case = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i]... | 342 |
__a :Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits toget... | 86 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ... | 79 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | 0 |
def a__ ( __UpperCamelCase ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(__UpperCamelCase ).count("1" ... | 140 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 86 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@... | 99 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
... | 86 | 0 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
a : int = {
'tiny.en': 'https://openaipublic... | 69 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PR... | 118 |
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Any , UpperCAmelCase : Dict ):
A_ = None
A_ = None
A_ ... | 86 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_snake_case = _LazyModule(__name__,... | 500 |
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,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 0 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
a_ = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.B... | 437 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : List[str] ):
"""simple docstring"""
A_ = {
"en": "Machine learning is great, isn't i... | 86 | 0 |
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,
)
from transformers.testing_utils import require_to... | 194 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase ... | 86 | 0 |
def __lowercase ( _UpperCAmelCase = 50 ) -> Optional[int]:
'''simple docstring'''
__lowercase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_num... | 321 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = (DDPMParallelScheduler,)
def __A ( self ... | 86 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrate... | 42 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Dict ,__UpperC... | 86 | 0 |
'''simple docstring'''
import os
def __UpperCamelCase ( ) ->List[str]:
with open(os.path.dirname(__UpperCamelCase ) + '''/grid.txt''' ) as f:
snake_case = [] # noqa: E741
for _ in range(20 ):
l.append([int(__UpperCamelCase ) for x in f.readline().sp... | 342 |
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... | 86 | 0 |
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... | 79 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a :Optional[Any] = 'src/transformers'
__a :Tuple = 'docs... | 86 | 0 |
import re
from filelock import FileLock
try:
import nltk
A : List[Any] = True
except (ImportError, ModuleNotFoundError):
A : List[str] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def a__ ( __U... | 140 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import l... | 86 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE = '#'
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self ):
__a = {}
def snake_case_ ( self , __A ):
__a = self._trie
for char... | 99 |
def __snake_case ( __UpperCamelCase : int = 50 ):
"""simple docstring"""
A_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1... | 86 | 0 |
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