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 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowerc... | 218 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 0 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _snake_case ( __snake_case : int ):
"""simple docstring"""
def is_in_circle(__snake_case : float ... | 88 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_py... | 330 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 0 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase ( a , a , a ) -> str:
'''simple docstring'''
__magic_name__ = AutoConfig.from_pretrained(snake_case__ ... | 432 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-1... | 357 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 0 |
import math
from datetime import datetime, timedelta
def lowercase( UpperCamelCase_ ) -> str:
'''simple docstring'''
UpperCamelCase = year % 19
UpperCamelCase = year % 4
UpperCamelCase = year % 7
UpperCamelCase = math.floor(year / 100 )
UpperCamelCas... | 537 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''google/bigbird-roberta-base''': '''https://huggingface.co... | 157 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 0 |
from __future__ import annotations
def UpperCamelCase ( _A ):
"""simple docstring"""
__magic_name__ : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789""" )
def UpperCamelCase ( ):
... | 324 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
snake_case_ : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst ... | 212 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = [True] * limit
_snake_case : Optional[Any] = False
_snake_case : List[str] ... | 609 | 0 |
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :List[str], snake_case :Tuple, snake_case :Dict, snake_case :str):
"""simple docstring"""
_lowercase =None
_lowercase =None
_lowerc... | 181 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokeniza... | 609 | 0 |
import os
__lowerCAmelCase = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
def _lowercase ( a__ : str ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = 0
while index < len... | 147 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlat... | 609 | 0 |
"""simple docstring"""
def lowercase__() ->List[str]:
"""simple docstring"""
lowercase__ : Optional[int]= 0
for i in range(1 , 1_001 ):
total += i**i
return str(snake_case__ )[-10:]
if __name__ == "__main__... | 218 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase = 10
def _snake_case ( __snake_case : list[int] ):
"""simple docstring"""
_lowerCamelCase : Any = 1
_lowerCamelCase : Dict = max(snake_case__ )
whil... | 88 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789... | 609 | 0 |
'''simple docstring'''
lowerCamelCase_ = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __lowercase ( __lowercase ) -> str:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
_A = F'''a bytes-lik... | 330 |
"""simple docstring"""
import argparse
from collections import defaultdict
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str ):
"""simple doc... | 609 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImagePro... | 432 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase( nn.Module ):
'''simple docstring'''
... | 609 | 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.transfo_xl.tokenization_... | 357 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 | 0 |
def lowercase( UpperCamelCase_ ) -> Any:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
... | 537 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_lowercase = _LazyModule(__name__,... | 157 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 324 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')... | 609 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_input... | 212 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transform... | 181 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Dict = max(snake_case__ )
while plac... | 609 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require... | 147 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = ... | 609 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def lowercase__(A , A , A ) ->Optional[Any]:
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise... | 218 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_rembert""... | 88 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 0 |
'''simple docstring'''
import random
def __lowercase ( __lowercase , __lowercase ) -> Optional[Any]:
'''simple docstring'''
_A = [], [], []
for element in data:
if element < pivot:
less.append(snake_case__ )
elif ele... | 330 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 0 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
# TODO Update this
_lowerCAmelCase = {
"facebook/esm-1b": "ht... | 432 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
... | 357 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 537 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',... | 157 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 0 |
def UpperCamelCase ( _A ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__, (list, tuple) ) or not all(
isinstance(snake_case__, snake_case__ ) for number in numbers ):
raise Val... | 324 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 0 |
'''simple docstring'''
def __snake_case ( ):
for n in range(1, 100_0000):
yield n * (n + 1) // 2
def __snake_case ( _UpperCAmelCase : List[Any]):
UpperCamelCase = 1
UpperCamelCase = 2
while i * i <= n:
UpperCamelCase = 0
while n % ... | 212 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = [True] * limit
_snake_case : Optional[Any] = False
_snake_case : List[str] ... | 609 | 0 |
def _snake_case (_snake_case : Tuple) -> List[Any]:
_lowercase =1
_lowercase =2
while i * i <= n:
_lowercase =0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n... | 181 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokeniza... | 609 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 147 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlat... | 609 | 0 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def lowercase__(A ) ->int:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(snake_case__... | 218 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
UpperCAmelCase = None
def _snake_case ( ):
"""simple docs... | 88 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789... | 609 | 0 |
'''simple docstring'''
from math import pow
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> str:
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then... | 330 |
"""simple docstring"""
import argparse
from collections import defaultdict
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str ):
"""simple doc... | 609 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> List[Any]:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
__magic_name__ = str(bin(snake_case__ ) )
bin... | 432 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase( nn.Module ):
'''simple docstring'''
... | 609 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
res... | 357 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 | 0 |
import argparse
from collections import defaultdict
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
UpperCamelCase = f"""{file}_{class_name}_{test_name}"... | 537 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-robert... | 157 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 0 |
from ...configuration_utils import PretrainedConfig
__magic_name__: Optional[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/goo... | 324 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')... | 609 | 0 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : int = 50):
UpperCamelCase = [[0] * 3 for _ in range(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):
... | 212 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 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,
defa... | 181 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Dict = max(snake_case__ )
while plac... | 609 | 0 |
def _lowercase ( a__ : int , a__ : int ) -> Dict:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCamelCase = str(bin(snake_case__ ) )[2:] # remove the leading "0b"
_UpperCamelCase =... | 147 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = ... | 609 | 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=in... | 218 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils impor... | 88 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normal... | 330 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/de... | 432 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet imp... | 357 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 0 |
from __future__ import annotations
from typing import Any
def lowercase( UpperCamelCase_ ) -> str:
'''simple docstring'''
if not postfix_notation:
return 0
UpperCamelCase = {"""+""", """-""", """*""", """/"""}
UpperCamelCase = []
for token in postfix_notation:
if tok... | 537 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 0 |
from __future__ import annotations
class __A :
def __init__(self , __magic_name__=None ):
lowerCamelCase__ : Optional[int] = data
lowerCamelCase__ : int = None
def __repr__(self ):
lowerCamelCase__ : Union[str, Any] ... | 157 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__: Optional[int] = get_tests... | 324 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 0 |
'''simple docstring'''
snake_case_ : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'To use `datasets`, Python>=3.7 is required, and the current version of Pytho... | 212 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = [True] * limit
_snake_case : Optional[Any] = False
_snake_case : List[str] ... | 609 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] ='''ClapFeatureExtractor'''
__lowerCAmelCase : Any =('''Rober... | 181 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokeniza... | 609 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase ... | 147 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlat... | 609 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a : Optional[Any] = {
"""configuration_resnet""": ["... | 218 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowercase__ ( unittest.T... | 88 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789... | 609 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.u... | 330 |
"""simple docstring"""
import argparse
from collections import defaultdict
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str ):
"""simple doc... | 609 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a , a ) -> Tuple:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' ... | 432 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase( nn.Module ):
'''simple docstring'''
... | 609 | 0 |
"""simple docstring"""
def A_ ( __lowercase ):
if any(not isinstance(snake_case__ , snake_case__ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(snake_case__ ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(snake_case_... | 357 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 | 0 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils imp... | 537 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 157 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available(... | 324 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')... | 609 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import... | 212 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 0 |
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import ConfigTester
from ...test... | 181 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Dict = max(snake_case__ )
while plac... | 609 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models... | 147 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = ... | 609 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class __UpperCAmelCase( __a ):
"""simple docstring"""
... | 218 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 0 |
"""simple docstring"""
import math
def _snake_case ( __snake_case : int ):
"""simple docstring"""
_lowerCamelCase : Any = 0
_lowerCamelCase : Optional[Any] = 0
while num > 0:
_lowerCamelCase : Any = num % 8
_lower... | 88 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 0 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> Dict:
'''simple docstring'''
_A = ("""dense.weight""", ""... | 330 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 432 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 0 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a__ ( ... | 357 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 537 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _A (UpperCamelCase : NDArray[floataa] , UpperCamelCase : NDArray[floataa] , UpperCamelCase : list[int] , UpperCamelCase : int , ) ->Optional[Any]:... | 157 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 0 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case__ :
lowercase__ : Dict = field(
metadata={'''help''': '''The output ... | 324 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 0 |
'''simple docstring'''
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class lowercase__ :
'''simple docstring'''
def __init__( self , lower... | 212 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = [True] * limit
_snake_case : Optional[Any] = False
_snake_case : List[str] ... | 609 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import Fl... | 181 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokeniza... | 609 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
ge... | 147 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlat... | 609 | 0 |
"""simple docstring"""
def lowercase__(A , A , A=False ) ->Optional[int]:
"""simple docstring"""
if isinstance(snake_case__ , snake_case__ ) and isinstance(snake_case__ , snake_case__ ):
lowercase__ : str=... | 218 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffuser... | 88 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : int = str(snake_case__ )
return len(snake_case__ ) == 9 and set(snake_case__ ) == set("""123456789... | 609 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MA... | 330 |
"""simple docstring"""
import argparse
from collections import defaultdict
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str ):
"""simple doc... | 609 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCamelCase ( a ) -> Tuple:
'''simple docstring'''
if "cls_token" in name:
__magic_na... | 432 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase( nn.Module ):
'''simple docstring'''
... | 609 | 0 |
"""simple docstring"""
from math import ceil
def A_ ( __lowercase , __lowercase ):
UpperCamelCase_ : Union[str, Any] =list(range(0 , snake_case__ ) )
UpperCamelCase_ : List[str] =[item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate ch... | 357 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in num... | 609 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_SCREAMING_... | 537 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 609 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
class __A ( __a ):
UpperCamelCase :List[Any] = '''encoder-decoder'''
UpperCamelCase :Union[str, Any] = True
def __ini... | 157 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__magic_name__: Tuple = (720, 1_280) # Height, Width
__magic_name__: Optional[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
__magic_name__:... | 324 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')... | 609 | 0 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : str, _UpperCAmelCase : str):
if not (isinstance(snake_case__, snake_case__) and isinstance(snake_case__, snake_case__)):
raise ValueError('''longest_common_substring() takes two strings for inputs''')
... | 212 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 0 |
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils... | 181 |
"""simple docstring"""
from __future__ import annotations
A_ = 10
def UpperCAmelCase__ (snake_case__ : list[int] ):
"""simple docstring"""
_snake_case : Any = 1
_snake_case : Dict = max(snake_case__ )
while plac... | 609 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCAmelCase = """"""
__lowerCAmelCase = """"""
__lowerCAmelCase = """"""
__lowerCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def _lowercase ( ) -> ... | 147 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = ... | 609 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tok... | 218 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, ru... | 609 | 0 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( __snake_case : str ):
"""simple docstring"""
return "".join(sorted(snake_case__ ) )
def _snake_case (... | 88 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 0 |
'''simple docstring'''
import qiskit
def __lowercase ( __lowercase = 2 ) -> List[str]:
'''simple docstring'''
_A = qubits
# Using Aer's simulator
_A = qiskit.Aer.get_backend("aer_simulator" )
# Creating a Quantum Circuit acting on the q r... | 330 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
... | 609 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 432 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 0 |
"""simple docstring"""
import math
def A_ ( __lowercase ):
if not isinstance(snake_case__ , snake_case__ ):
UpperCamelCase_ : Dict =F'''Input value of [number={number}] must be an integer'''
raise TypeError(snake_case__ )
if number < 1:
UpperCamelCase_ : str =F'... | 357 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
Distil... | 609 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_f... | 537 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_stru... | 157 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
r... | 609 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class snake_case__ ( __a , unittest.TestCase ):
lowercase__ : Optional[int] = DownBlockaD # noqa F405
lowe... | 324 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_avail... | 212 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = [True] * limit
_snake_case : Optional[Any] = False
_snake_case : List[str] ... | 609 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
... | 181 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokeniza... | 609 | 0 |
import functools
def _lowercase ( a__ : str , a__ : str ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = len(snake_case__ )
_UpperCamelCase = len(snake_case__ )
@functools.cache
def min_distance(a__ : int , a__ : ... | 147 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
A_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlat... | 609 | 0 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenize... | 218 |
"""simple docstring"""
A_ = 2_56
# Modulus to hash a string
A_ = 1_00_00_03
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Any = len(snake_case__ )
_snak... | 609 | 0 |
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