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 os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCAmelCase_ : str = ['''small''', '''medium''', '''large''']
UpperCAmelCase_ : Union[str, Any] = '''lm_head.decoder.weight'''
UpperCAmelCase_ : Optional[int] = '... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 24 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def ... | 24 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 24 | 1 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README... | 24 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 24 | 1 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
UpperCAmelCase_ : List[str] = 2_0_4_8
UpperCAmelCase_ : List[Any] = 4_0_9_6
UpperCAmelCase_ : Any = 4_2
UpperCAmelCase_ : List[str] = os.environ.pop('''PROCESS_TRAIN''', ... | 24 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 | 1 |
'''simple docstring'''
import string
from math import logaa
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : str )-> int:
'''simple docstring'''
__snake_case = document.translate(
str.maketrans('''''' , '''''' , stri... | 24 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase (_lowerCamelCase : int )-> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number ... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = abs(_lowerCamelCase )
__snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def ... | 24 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S'''... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Dict:
'''simple docstring'''
__snake_case = []
__snake_case = []
__snake_case = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
... | 24 | 1 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requir... | 24 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Optional[int]:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5:... | 24 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _UpperCamelCase (_lowerCamelCase : Tuple )-> List[str]:
'''simple... | 24 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttentio... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( __lowerCA... | 24 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _UpperCamelCase (_l... | 24 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : float , _lowerCamelCase : float )-> float:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _UpperCamelCase (_lowerCamelC... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> float:
'''simple docstring'''
__snake_case = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# form... | 24 | 1 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTe... | 24 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import ... | 24 | 1 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCamelCase (_lowerCamelCase : float )-> float:
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCame... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = [[0 for _ in range(_lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__snake_case = 1
fo... | 24 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
UpperCAmelCase_ : List[Any] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-va... | 24 |
'''simple docstring'''
import argparse
import os
import re
UpperCAmelCase_ : List[str] = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_ : Tuple = re.... | 24 | 1 |
'''simple docstring'''
class lowerCAmelCase :
def __init__( self , __SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__snake_case = n
__snake_case = [None] * self.n
__snake_case = 0 # index of the ... | 24 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase (*_lowerCamelCase : str , _lowerCamelCase : Optional[Union[Dict, Any]] = None , _lowerCamelCase : List[Any]=True , _low... | 24 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 24 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTe... | 24 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCamelCase (_lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float )-> np.array:
... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 24 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json... | 24 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self ... | 24 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int:
'''simple docstring'''
return number | (1 << position)
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int:
... | 24 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingS... | 24 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCAmelCase ( nn.Module):
def __init__( self , __SCREAMING_SNAKE_CASE = 16 , __SCREAMING_SNAKE_CASE = 88 , __SCREAMING_SNAK... | 24 |
'''simple docstring'''
from collections import deque
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] )-> Optional[int]:
'''simple docstring'''
__snake_case = len(_lowerCamelCase )
__snake_case = deque()
__snake_case ... | 24 | 1 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _UpperCamelCase (_lowerCamelCase : Any )-> Any:
... | 24 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 24 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _UpperCame... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 24 | 1 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import v... | 24 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 24 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wav... | 24 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 24 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 24 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : str = {
'''configuration_efficientformer''': [
'''EFFICIENTFORM... | 24 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 | 1 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : Optional[Any] )-> Union[str, A... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = abs(_lowerCamelCase )
__snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def ... | 24 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class lowerCAmelCase :
def __init__( self , __SCREAMING_SNAKE_CASE = None ) -> Any:
'''simple docstring'''
__snake_case = value
__snake_case ... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Dict:
'''simple docstring'''
__snake_case = []
__snake_case = []
__snake_case = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
... | 24 | 1 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ : Tuple = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained... | 24 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpo... | 24 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _UpperCamelCase (_lowerCamelCase : Tuple )-> List[str]:
'''simple... | 24 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_roberta''': [... | 24 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _UpperCamelCase (_l... | 24 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> float:
'''simple docstring'''
__snake_case = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# form... | 24 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgum... | 24 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import ... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Dict = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = [[0 for _ in range(_lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__snake_case = 1
fo... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'... | 24 |
'''simple docstring'''
import argparse
import os
import re
UpperCAmelCase_ : List[str] = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_ : Tuple = re.... | 24 | 1 |
'''simple docstring'''
import os
UpperCAmelCase_ : Tuple = {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
def _UpperCamelCase (_lowerCamelCase : str )-> int:
'''simple docstring'''
__snake_case ... | 24 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase (*_lowerCamelCase : str , _lowerCamelCase : Optional[Union[Dict, Any]] = None , _lowerCamelCase : List[Any]=True , _low... | 24 | 1 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate... | 24 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTe... | 24 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 24 | 1 |
'''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 import Paddin... | 24 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self ... | 24 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
... | 24 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingS... | 24 | 1 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = R'''
Ar... | 24 |
'''simple docstring'''
from collections import deque
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] )-> Optional[int]:
'''simple docstring'''
__snake_case = len(_lowerCamelCase )
__snake_case = deque()
__snake_case ... | 24 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax impor... | 24 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 24 | 1 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _UpperCamelCase (_lowerCamelCase : int = 3 )-> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 24 | 1 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_ : Optional[Any] = '''src/transformers'''
# This is to mak... | 24 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 24 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 24 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 24 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAm... | 24 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf,... | 24 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase_ : int = '''docs/source/en/_toctree.yml'''
def _UpperCamelCase (_lowerCamelCase : Tuple )-> List[str]:
'''simple docstring'''
__snake_case = ... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = abs(_lowerCamelCase )
__snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def ... | 24 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int:
'''simple docstring'''
while b:
__snake_case , __snake_case = b, a % b
return a
def _UpperCamelCase (_lowerCamelCase ... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Dict:
'''simple docstring'''
__snake_case = []
__snake_case = []
__snake_case = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
... | 24 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCAmelCase_ : Union[str, Any] = '''\
'''
UpperCAmelCase_ : int = '''
Perplexity ... | 24 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ : Dict = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
UpperCAmelCase_ : Union[st... | 24 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _UpperCamelCase (_lowerCamelCase : Tuple )-> List[str]:
'''simple... | 24 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : dict )-> str:
'''simple docstring'''
__snake_case = BeautifulSoup(requests.get(_lowerCamelCase , params=_lowerCa... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
UpperCAmelCase... | 24 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _UpperCamelCase (_l... | 24 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : list[int] , _lowerCamelCase : int )-> bool:
'''simple docstring'''
__snake_case = len(_lowerCamelCase )
__snake_case = [[False] * (required_sum + 1) for _ in range(arr_len +... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> float:
'''simple docstring'''
__snake_case = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# form... | 24 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self , *__SCREAMING_SNAKE_CAS... | 24 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import ... | 24 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = [[0 for _ in range(_lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__snake_case = 1
fo... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'''configuration_whisper''': [... | 24 |
'''simple docstring'''
import argparse
import os
import re
UpperCAmelCase_ : List[str] = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_ : Tuple = re.... | 24 | 1 |
'''simple docstring'''
UpperCAmelCase_ : List[Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
1_0: '''a''',
1_1: '''b''',
1_2: '''c''',
1_3: '''d''',
1_4: '''e... | 24 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase (*_lowerCamelCase : str , _lowerCamelCase : Optional[Union[Dict, Any]] = None , _lowerCamelCase : List[Any]=True , _low... | 24 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils... | 24 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTe... | 24 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase)
class lowerCAmelCase ( __lowerCAmelCase):
# `task` is not a ClassVar since we ... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 24 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.ka... | 24 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self ... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Tuple = {
'''configuration_llama''': ['''LLAMA_PRETRAINE... | 24 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingS... | 24 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : list[int] )-> list[list[int]]:
'''simple docstring'''
__snake_case = []
if len(_lowerCamelCase ) == 1:
return [nums.copy()]
for _ in range(len(_lowerCamelCase ) ):
... | 24 |
'''simple docstring'''
from collections import deque
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] )-> Optional[int]:
'''simple docstring'''
__snake_case = len(_lowerCamelCase )
__snake_case = deque()
__snake_case ... | 24 | 1 |
'''simple docstring'''
from collections import deque
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] )-> Optional[int]:
'''simple docstring'''
__snake_case = len(_lowerCamelCase )
__snake_case = deque()
__snake_case ... | 24 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 24 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''microsoft/wavlm-base''': '''https://huggingface.co... | 24 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 24 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _UpperCamelCase (_lowerCamelCase : list[list[float]] )-> list[list[float]]:
'''simple docstring'''
__snake_case = Decimal
# Check if the... | 24 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 24 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ : List[str] = logging.getLogger(__name__)
def _UpperCamelCase ()-> Any:
'''simple docstring'''
__snake_... | 24 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 24 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Dict = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax... | 24 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 | 1 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
UpperCAmelCase_ : Any = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformab... | 24 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 | 1 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
U... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = abs(_lowerCamelCase )
__snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def ... | 24 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Dict:
'''simple docstring'''
__snake_case = []
__snake_case = []
__snake_case = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
... | 24 | 1 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 24 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 | 1 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise... | 24 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _UpperCamelCase (_lowerCamelCase : Tuple )-> List[str]:
'''simple... | 24 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase_ : Union[str, Any] = [True] * 1_0_0_0_0_0_1
UpperCAmelCase_ : Optional[Any] = 2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
UpperCAmelCase_ ... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.... | 24 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _UpperCamelCase (_l... | 24 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self , *__SCREAMING_... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> float:
'''simple docstring'''
__snake_case = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# form... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : List[Any] = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', ... | 24 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import ... | 24 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_M... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = [[0 for _ in range(_lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__snake_case = 1
fo... | 24 | 1 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__... | 0 |
'''simple docstring'''
import argparse
import os
import re
UpperCAmelCase_ : List[str] = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_ : Tuple = re.... | 24 | 0 |
def _A ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
__snake_case = generate_large_matrix()
__snake_case = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, ... | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase (*_lowerCamelCase : str , _lowerCamelCase : Optional[Union[Dict, Any]] = None , _lowerCamelCase : List[Any]=True , _low... | 24 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(_snake_case :float , _snake_case... | 2 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTe... | 24 | 0 |
'''simple docstring'''
def A_( A : int):
if not isinstance(A , A):
UpperCamelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(A)
if number < 0:
return False
UpperCamelCase = number... | 3 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 24 | 0 |
"""simple docstring"""
import math
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowerCAmelCase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCAmelCase )
if number < 1:
lowerCAmelC... | 4 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self ... | 24 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTes... | 5 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingS... | 24 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 6 |
'''simple docstring'''
from collections import deque
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] )-> Optional[int]:
'''simple docstring'''
__snake_case = len(_lowerCamelCase )
__snake_case = deque()
__snake_case ... | 24 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : list ) -> list:
'''simple docstring'''
_A = False
while is_sorted is False: # Until all the indices are traversed keep looping
_A = True
for i in range(0 , len(_snake_ca... | 7 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 24 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configu... | 8 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 24 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE__ = ['''small''', '''medium''', '''large''']
SCREAMING_SNAKE_CASE__ = '''lm_head.decoder.weight'''
SCREAMING_SNAKE_CASE__ = '''lm_head.weight'''
def A ( __UpperCamelCase , ... | 9 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transform... | 24 | 0 |
from __future__ import annotations
_lowerCAmelCase = []
def _snake_case ( __snake_case , __snake_case , __snake_case ):
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
... | 10 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 24 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def lowerCAmelCase (... | 11 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 24 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ : str = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
... | 12 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simp... | 13 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
__snake_case = abs(_lowerCamelCase )
__snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def ... | 24 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils impor... | 14 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : Optional[Any] )-> Dict:
'''simple docstring'''
__snake_case = []
__snake_case = []
__snake_case = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
... | 24 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path im... | 15 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A : int = logging.get_logger(__name__)
__A : str = [
['attention', 'attn'],
['encoder_a... | 16 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _UpperCamelCase (_lowerCamelCase : Tuple )-> List[str]:
'''simple... | 24 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> str:
def wrapper(*a__ : List[Any] ,**a__ : str ):
__A : List[Any] = ... | 17 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __a(SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : NDArray[floataa] , SCREAMING_SNAKE_CASE_ : list[int] , ... | 18 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _UpperCamelCase (_l... | 24 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorForma... | 19 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> float:
'''simple docstring'''
__snake_case = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# form... | 24 | 0 |
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