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 torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMas... | 560 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import re... | 111 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 63 |
'''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 = logging.get_logger(__name__)
_UpperCam... | 111 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the roo... | 383 |
'''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_modelin... | 111 | 0 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_snake_case = datasets.logging.get_logger(__name__)
_snake_case = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thi... | 655 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 636 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = ... | 220 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dat... | 409 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 0 |
import re
def A_ ( a ):
"""simple docstring"""
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = split_input(str_ )
return "".join(
[''.join(... | 511 |
'''simple docstring'''
import logging
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,
... | 111 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnal... | 373 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase(a : int , a : Tuple , a : Unio... | 255 |
'''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_seed
... | 111 | 0 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension... | 435 |
'''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 import To... | 111 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from .... | 560 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowerCamelCase__ ( __lowerCamelCase : List[Any] ):
__UpperCAmelCase : Any = SwinConfig(image_s... | 63 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True... | 111 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve... | 383 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_lo... | 655 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 0 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn ... | 636 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 220 |
'''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.me... | 111 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(lowercase ):
for j in range(lowercase ):
if dist[i][j] != float("""inf""" ):
print(int(d... | 409 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWi... | 511 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...... | 111 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/mai... | 373 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
UpperCAmelCase_ : List[Any] ... | 255 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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 ...... | 435 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __A :
'''simple docstring'''
pass
| 560 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import re... | 111 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import... | 63 |
'''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 = logging.get_logger(__name__)
_UpperCam... | 111 | 0 |
from statistics import mean
import numpy as np
def _a ( __lowercase , __lowercase , __lowercase , __lowercase ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = 0
# Number of processes finished
__UpperCamelCase = 0
... | 383 |
'''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_modelin... | 111 | 0 |
from __future__ import annotations
def _A ( __magic_name__ ):
if len(__magic_name__ ) == 0:
return []
lowercase__ = min(__magic_name__ ), max(__magic_name__ )
lowercase__ = int(max_value - min_value ) + 1
lowercase__ = [[] for _ in range(__magic... | 655 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = np.shape(_UpperCamelCase )
if rows != columns:
__lowerCAmelCase = (
"'table' h... | 636 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 0 |
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase = "" , __UpperCAmelCase = False ):
SCREAMING_SNAKE_CASE_ : dict[str, RadixNode] ={}
# A node will be a leaf if the tree contains its word
SCREAM... | 220 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/reso... | 409 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCAmelCase : int = logging.getLogger(__name__)
class _A ( __magic_name__):
SCREAMING_SNAKE_CASE : Optional[int] = '''masked_bert'''
def __init__( self , _SCREAMING_SNAKE_CASE=3_0522 , _SC... | 511 |
'''simple docstring'''
import logging
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,
... | 111 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mc... | 373 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : D... | 255 |
'''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_seed
... | 111 | 0 |
'''simple docstring'''
def __A ( UpperCAmelCase ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : Any = len(UpperCAmelCase )
for i in range(length - 1 ):
_UpperCamelCase : str = i
f... | 435 |
'''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 import To... | 111 | 0 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from .... | 560 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 0 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a : Tuple = abspath(join(dirname(dirname(__file__))... | 63 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True... | 111 | 0 |
import re
from filelock import FileLock
try:
import nltk
_snake_case = True
except (ImportError, ModuleNotFoundError):
_snake_case = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _a ( __lowercase ) -> Opti... | 383 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-... | 655 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 0 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_availa... | 636 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity... | 220 |
'''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.me... | 111 | 0 |
"""simple docstring"""
_UpperCAmelCase = {
"""joule""": 1.0,
"""kilojoule""": 1_0_0_0,
"""megajoule""": 1_0_0_0_0_0_0,
"""gigajoule""": 1_0_0_0_0_0_0_0_0_0,
"""wattsecond""": 1.0,
"""watthour""": 3_6_0_0,
"""kilowatthour""": 3_6_0_0_0_0_0,
"""newtonmeter""": 1.0,
... | 409 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
'processing_... | 511 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...... | 111 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
... | 373 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
UpperCAmelCase_ : int = HUGGINGFACE_HUB_CACHE
UpperCAmelCase_ : Optional[Any] = '''config.json'''
UpperCAmelCase_ : List[str] ... | 255 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 0 |
'''simple docstring'''
lowerCAmelCase_ : List[str] = tuple[float, float, float]
lowerCAmelCase_ : List[Any] = tuple[float, float, float]
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : ... | 435 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 0 |
"""simple docstring"""
import requests
lowerCAmelCase_ = '' # <-- Put your OpenWeatherMap appid here!
lowerCAmelCase_ = 'https://api.openweathermap.org/data/2.5/'
def __UpperCAmelCase ( __lowerCamelCase = "Chicago" , __lowerCamelCase = APPID ) -> Optional[Any]:... | 560 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import re... | 111 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : ... | 63 |
'''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 = logging.get_logger(__name__)
_UpperCam... | 111 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.tes... | 383 |
'''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_modelin... | 111 | 0 |
from math import ceil, sqrt
def _A ( __magic_name__ = 100_0000 ):
lowercase__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase__ = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
lower... | 655 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
if edge <= 0 or not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _lo... | 636 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 0 |
__SCREAMING_SNAKE_CASE = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batc... | 220 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =[], []
while len(lowercase ) > 1:
SCREAMING_SNAKE_CASE_: Optional[int] =min(lowercase ), max(lowercase )
start.append(lowercase )
end.append(lowercase )
... | 409 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 0 |
from __future__ import annotations
from typing import Any
def A_ ( a ):
"""simple docstring"""
if not postfix_notation:
return 0
SCREAMING_SNAKE_CASE_ : Tuple = {"+", "-", "*", "/"}
SCREAMING_SNAKE_CASE_ : list[Any] = []
for token i... | 511 |
'''simple docstring'''
import logging
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,
... | 111 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAt... | 373 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE ={}
def UpperCamelCase_ ( self ... | 255 |
'''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_seed
... | 111 | 0 |
'''simple docstring'''
from PIL import Image
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> Any:
'''simple docstring'''
def brightness(UpperCAmelCase ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -2_55.0 <= level <= 2_55... | 435 |
'''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 import To... | 111 | 0 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : List[str] ,_snake_case : int ,... | 560 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 0 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : Tuple ):
if num <= 0:
__UpperCAmelCase : str = f"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(__lowerCamelCase ... | 63 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True... | 111 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=_lowercase ):
"""simple docstring"""
UpperCAmelCase__ = ["transformers", "torch", "note_seq"]
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> ... | 383 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[Any] , *_lowercase :Tuple ... | 655 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 0 |
"""simple docstring"""
import unittest
import numpy as np
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , ):
'''simple docstring'''
__lowerCAmelCase = np.shape(_UpperCamelCase )
... | 636 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory... | 220 |
'''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.me... | 111 | 0 |
"""simple docstring"""
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm mus... | 409 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 511 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...... | 111 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.... | 373 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow... | 255 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import S... | 435 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 0 |
"""simple docstring"""
import os
from distutils.util import strtobool
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
for e in env_keys:
lowercase__ : Tuple = int(os.environ.get(__lowerCamelCase , -1... | 560 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import re... | 111 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
... | 63 |
'''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 = logging.get_logger(__name__)
_UpperCam... | 111 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_fi... | 383 |
'''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_modelin... | 111 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class lower... | 655 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A : Any = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "T... | 636 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 0 |
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
__SCREAMING_SNAKE_CASE = 'http://www.mocksite.com/file... | 220 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 0 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_... | 409 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 0 |
import math
def A_ ( a , a ):
"""simple docstring"""
return math.pow(a , 2 ) - a
def A_ ( a ):
"""simple docstring"""
return 2 * x
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = 2.0
while start ... | 511 |
'''simple docstring'''
import logging
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,
... | 111 | 0 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import C... | 373 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 0 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowerCAmelCase(a : Optional[Any] ) -> int:
_SCREAMING_SNAKE_CASE =img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(a ):
for j in range(a ):
... | 255 |
'''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_seed
... | 111 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available... | 435 |
'''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 import To... | 111 | 0 |
"""simple docstring"""
from itertools import product
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
lowercase__ : Dict = sides_number
lowercase__ : List[str] = max_face_number * dice_number
... | 560 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
... | 63 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True... | 111 | 0 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResamp... | 383 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str ... | 112 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {
"""nielsr/canine-s""": 2_0_4_8,... | 112 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
ex... | 112 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ : List[Any] = logging.getLogger... | 112 |
def _A ( lowerCamelCase ):
a__ : Tuple = []
a__ : str = set({"(", "[", "{"} )
a__ : List[str] = set({")", "]", "}"} )
a__ : int = {"{": "}", "[": "]", "(": ")"}
for i in range(len(lowerCamelCase ) ):
if s[i] in open_brackets:
stac... | 112 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import Pr... | 112 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew ... | 112 | 1 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tuple[float, float, float]
SCREAMING_SNAKE_CASE__ : str = tuple[float, float, float]
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : Optional[int] = end_pointa[0] - end_pointa[0]
a__ : ... | 112 |
def _A ( lowerCamelCase = 200 ):
a__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
a__ : Dict = [0] * (pence + 1)
a__ : int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCamelCase , pence + 1 , 1 ):
num... | 112 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel... | 112 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP... | 112 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""google/pix2struct-textcaps-base""": ... | 112 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : Any = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 112 | 1 |
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,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 112 |
# Function to print upper half of diamond (pyramid)
def _A ( lowerCamelCase ):
for i in range(0 , lowerCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , ... | 112 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ... | 112 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : int = {"""configuration_... | 112 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_UpperCamelCase ):
_UpperCamelCase : Optional[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case , **snake_case ) -> Tuple:
"""simple docst... | 112 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtra... | 112 | 1 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 6_5_5_2_1
def _A ( lowerCamelCase ):
a__ : str = 1
a__ : Union[str, Any] = 0
for plain_chr in plain_text:
a__ : Optional[int] = (a + ord(lowerCamelCase )) % MOD_ADLER
a__ : ... | 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 112 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is... | 112 | 1 |
SCREAMING_SNAKE_CASE__ : Dict = [
(1_0_0_0, """M"""),
(9_0_0, """CM"""),
(5_0_0, """D"""),
(4_0_0, """CD"""),
(1_0_0, """C"""),
(9_0, """XC"""),
(5_0, """L"""),
(4_0, """XL"""),
(1_0, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1... | 112 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 112 | 1 |
import unittest
from transformers import LiltConfig, 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_modeling_common import ModelTesterMixin, ... | 112 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _A ( lowerCamelCase ):
if not is_accelerate_available():
return method
a__ : List[Any] = version.parse(accelerate.__version__ ).base_version
... | 112 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_log... | 112 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 112 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ ... | 112 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __lowerCAmelCase :
_UpperCamelCase : torch.Tensor # [batch_size x 3]
_UpperCamelCase : torch.Tensor # [batch_size x 3]
_UpperCamelCase : torch.Tensor # [batch_size x... | 112 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask... | 112 | 1 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.point(lowerCamel... | 112 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
O... | 112 | 1 |
from __future__ import annotations
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
a__ : Optional[int] = []
a__ , a__ : List[str] = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((... | 112 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""google/pix2struct-textcaps-base""": ... | 112 | 1 |
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