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"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if n... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
A_ = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : Optional[int] ... | 609 |
"""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 | 1 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
A_ = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
A_... | 609 |
"""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 | 1 |
"""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... | 609 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : Optional[int] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(snake_case__ )[-10:]
if __name__ == "__main__":
print(solution())
| 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
return "".join(sorted(snake_case__ ) )
def UpperCAmelCase__ (snake... | 609 |
"""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 | 1 |
"""simple docstring"""
A_ = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCAmelCase__ (snake_case__ : bytes ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
_snake_case : Any ... | 609 |
"""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 | 1 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
A_ = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Si... | 609 |
"""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 | 1 |
"""simple docstring"""
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_s... | 609 |
"""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 | 1 |
"""simple docstring"""
import math
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Any = 0
_snake_case : Optional[Any] = 0
while num > 0:
_snake_case : Any = num % 8
_snake_... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerM... | 609 |
"""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 | 1 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F40... | 609 |
"""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 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
class lowercase( __a ):
'''simple docstrin... | 609 |
"""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 | 1 |
"""simple docstring"""
from math import ceil
def UpperCAmelCase__ (snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = list(range(0 , snake_case__ ) )
_snak... | 609 |
"""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 | 1 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Op... | 609 |
"""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 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
A_ = ''''''
A_ = ''''''
A_ = ''''''
A_ = 1 # (0 is vertical, 1 is horizontal)
def UpperCAmelCase__ ():
"""simple... | 609 |
"""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 | 1 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.... | 609 |
"""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 | 1 |
"""simple docstring"""
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 ImageProcessingSav... | 609 |
"""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 | 1 |
"""simple docstring"""
import math
import qiskit
def UpperCAmelCase__ (snake_case__ : int = 1 , snake_case__ : int = 1 , snake_case__ : int = 1 ):
"""simple docstring"""
if (
isinstance(snake_case__ , snake_case__ )
or isinst... | 609 |
"""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 | 1 |
"""simple docstring"""
import requests
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[int] = {"""Content-Type""": """application/json"""}
_snake_case : List[str] = ... | 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def UpperCAmelCase__ (snake_case__ : float , snake_case__ : float , snake_case__ : float ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ... | 609 |
"""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 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "ClapFeatureExtractor"
lowercase__ = ("RobertaTokeni... | 609 |
"""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 | 1 |
"""simple docstring"""
import math
from datetime import datetime, timedelta
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : List[str] = year % 19
_snake_case : List[str] = year % 4
_snake_case : ... | 609 |
"""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 | 1 |
"""simple docstring"""
from math import isqrt
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case__ ) + 1 ) )
def UpperCAmelCase__ (snake_case__ : ... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import numpy as np
class lowercase:
'''simple docstring'''
def __init__( self: Any ):
'''simple docstring'''
_snake_case : Tuple = (0, 0)
_snake_case : Any = No... | 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowercase( __a ):
'''simple docstring'''
def UpperCamelCase_ ( self: ... | 609 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(snake_case__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest impor... | 609 |
"""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 | 1 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transfo... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoToken... | 609 |
"""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 | 1 |
"""simple docstring"""
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,... | 609 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
_snake_case : Tuple = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_snake_case : List[str... | 609 |
"""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 | 1 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
A_ = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version())... | 609 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
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 _... | 609 |
"""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 | 1 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecode... | 609 |
"""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 | 1 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def UpperCAmelCase__ (snake_case__ : Callable ):
"""simple docstring"""
@wraps(snake_case__ )
def _inner_fn(*snake_case__ : int , **snake_case__ : Dict ... | 609 |
"""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 | 1 |
"""simple docstring"""
from math import pow
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , ):
"""simple docstring"""
if current_sum == needed_sum:
... | 609 |
"""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 | 1 |
"""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_we... | 609 |
"""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 | 1 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImage... | 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCAmelCase__ (snake_case__ : NDArray[floataa] , snake_case__ : NDArray[floataa] , snake_case__ : list[int] , snak... | 609 |
"""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 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
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_com... | 609 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ():
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def UpperCAmelCase__ (snake_case__ : List[Any] ):
"""simple docstring"""
_snake_case : List[str] = 1
... | 609 |
"""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 | 1 |
"""simple docstring"""
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 impor... | 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A_ = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst o... | 609 |
"""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 | 1 |
"""simple docstring"""
import sys
A_ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''... | 609 |
"""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 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.nu... | 609 |
"""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 | 1 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, Sched... | 609 |
"""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 | 1 |
"""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_QUESTIO... | 609 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDua... | 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowercase:
'''simple docstring'''
def __init__( self: Tuple, a_: Tuple=None ):
'''simple docstring'''
_snake_case : Optional[int] = data... | 609 |
"""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 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
[... | 609 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
'''configuration_rembert... | 609 |
"""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 | 1 |
"""simple docstring"""
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_uti... | 609 |
"""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 | 1 |
"""simple docstring"""
import functools
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Union[str, Any] = len(snake_case__ )
_snake_case : List[str] = len(snake_case_... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
_snake_case : str = [int(snake_case__ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(snake_case__ ) == 4 and all(0 <= int(snake_cas... | 609 |
"""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 | 1 |
"""simple docstring"""
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_c... | 609 |
"""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 | 1 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def UpperCAmelCase__ (snake_case__ : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(snake_case__ , 0 ,... | 609 |
"""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 | 1 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 609 |
"""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 | 1 |
"""simple docstring"""
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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDe... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 4_00_00_00 ):
"""simple docstring"""
_snake_case : List[str] = [0, 1]
_snake_case : Tuple = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + ... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_... | 609 |
"""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 | 1 |
"""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
... | 609 |
"""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 | 1 |
"""simple docstring"""
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 ... | 609 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A_ = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
A_ = _LazyModule(__n... | 609 |
"""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 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.ima... | 609 |
"""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 | 1 |
"""simple docstring"""
from __future__ import annotations
A_ = []
def UpperCAmelCase__ (snake_case__ : list[list[int]] , snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
for i in range(len(snake_case__ ) ):
... | 609 |
"""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 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class l... | 609 |
"""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 | 1 |
"""simple docstring"""
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_availab... | 609 |
"""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 | 1 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutpu... | 609 |
"""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 | 1 |
"""simple docstring"""
import re
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
if len(re.findall("""[ATCG]""" , snake_case__ ) ) != len(snake_case__ ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dn... | 609 |
"""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 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.tab... | 609 |
"""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 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
A_ = '''h... | 609 |
"""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 | 1 |
"""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_tokenization_comm... | 609 |
"""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 | 1 |
"""simple docstring"""
import qiskit
def UpperCAmelCase__ (snake_case__ : int = 2 ):
"""simple docstring"""
_snake_case : Tuple = qubits
# Using Aer's simulator
_snake_case : Tuple = qiskit.Aer.get_backend("""aer_simulator""" ... | 609 |
"""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 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Bat... | 609 |
"""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 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIV... | 609 |
"""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 | 1 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400... | 609 |
"""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 | 1 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def UpperCAmelCase__ (snake_case__ : str = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
... | 609 |
"""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 | 1 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, 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
from ...test_mod... | 609 |
"""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 | 1 |
"""simple docstring"""
class lowercase:
'''simple docstring'''
def __init__( self: List[str], a_: Tuple, a_: Dict, a_: str ):
'''simple docstring'''
_snake_case : Optional[int] = None
_snake_case ... | 609 |
"""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 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ():
"""simple docstring"""
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
A_ = generate_large_matrix()
A_ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1,... | 609 |
"""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 | 1 |
"""simple docstring"""
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 lowercase( __a , unittest.TestCase ):
'''simple docst... | 609 |
"""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 | 1 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_bas... | 609 |
"""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 | 1 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
def is_in_circle(snake_case__ : float , snake_case__ ... | 609 |
"""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 | 1 |
"""simple docstring"""
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_DO... | 609 |
"""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 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.jso... | 609 |
"""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 | 1 |
"""simple docstring"""
A_ = {}
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any o... | 609 |
"""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 | 1 |
"""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 t... | 609 |
"""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 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
# TODO Update this
A_ = {
'''facebook/esm-1b''': '... | 609 |
"""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 | 1 |
"""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 |
"""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 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Tra... | 609 |
"""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 | 1 |
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