






"""simple docstring"""







import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCAmelCase            =	logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_lowerCAmelCase            =	{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCAmelCase            =	{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCAmelCase            =	sorted(arg_to_scheduler.keys())
_lowerCAmelCase            =	'{' + ', '.join(arg_to_scheduler_choices) + '}'


class      UpperCamelCase		(pl.LightningModule     ):






						def __init__(		self  :Tuple			, __magic_name__  :argparse.Namespace			, __magic_name__  :Optional[Any]=None			, __magic_name__  :Tuple="base"			, __magic_name__  :int=None			, __magic_name__  :List[str]=None			, __magic_name__  :Union[str, Any]=None			, **__magic_name__  :str			, )      ->List[Any]:
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(__magic_name__				)
										lowercase    :      Dict       = 0
										lowercase    :      Union[str, Any]       = Path(self.hparams.output_dir				)
										lowercase    :      Optional[int]       = self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
														lowercase    :      Optional[int]       = AutoConfig.from_pretrained(
														    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			, **({"""num_labels""": num_labels} if num_labels is not None else {})			, cache_dir=__magic_name__			, **__magic_name__			, )
										else:
														lowercase    :      PretrainedConfig       = config

										lowercase    :      int       = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
										for p in extra_model_params:
														if getattr(self.hparams			, __magic_name__			, __magic_name__				):
																		assert hasattr(self.config			, __magic_name__				), f"""model config doesn't have a `{p}` attribute"""
																		setattr(self.config			, __magic_name__			, getattr(self.hparams			, __magic_name__				)				)

										if tokenizer is None:
														lowercase    :      Tuple       = AutoTokenizer.from_pretrained(
														    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			, cache_dir=__magic_name__			, )
										else:
														lowercase    :      PreTrainedTokenizer       = tokenizer
										lowercase    :      str       = MODEL_MODES[mode]
										if model is None:
														lowercase    :      List[str]       = self.model_type.from_pretrained(
														    self.hparams.model_name_or_path			, from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path				)			, config=self.config			, cache_dir=__magic_name__			, )
										else:
														lowercase    :      Union[str, Any]       = model






						def      __snake_case      (		self  :Union[str, Any]			, *__magic_name__  :Dict			, **__magic_name__  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Optional[int]       = self.model_type.from_pretrained(*__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Dict				)      ->Tuple:
										lowercase    :      Any       = arg_to_scheduler[self.hparams.lr_scheduler]
										lowercase    :      List[str]       = get_schedule_func(
										    self.opt			, num_warmup_steps=self.hparams.warmup_steps			, num_training_steps=self.total_steps()				)
										lowercase    :      str       = {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
										return scheduler






						def      __snake_case      (		self  :Tuple				)      ->Optional[int]:
										lowercase    :      Optional[int]       = self.model
										lowercase    :      Optional[int]       = ["""bias""", """LayerNorm.weight"""]
										lowercase    :      Any       = [
										    {
										        """params""": [
										            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)
										        ],  # check this named paramters
										        """weight_decay""": self.hparams.weight_decay,
										    },
										    {
										        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)],
										        """weight_decay""": 0.0,
										    },
										]
										if self.hparams.adafactor:
														lowercase    :      Tuple       = Adafactor(
														    __magic_name__			, lr=self.hparams.learning_rate			, scale_parameter=__magic_name__			, relative_step=__magic_name__				)

										else:
														lowercase    :      Dict       = AdamW(
														    __magic_name__			, lr=self.hparams.learning_rate			, eps=self.hparams.adam_epsilon				)
										lowercase    :      Optional[int]       = optimizer

										lowercase    :      Tuple       = self.get_lr_scheduler()

										return [optimizer], [scheduler]






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :List[str]			, __magic_name__  :int				)      ->Union[str, Any]:
										return self.validation_step(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Optional[Any]				)      ->Optional[int]:
										return self.validation_end(__magic_name__				)






						def      __snake_case      (		self  :List[Any]				)      ->int:
										lowercase    :      str       = max(1			, self.hparams.gpus				)  # TODO: consider num_tpu_cores
										lowercase    :      List[str]       = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :Union[str, Any]				)      ->Tuple:
										if stage == "test":
														lowercase    :      Union[str, Any]       = len(self.test_dataloader().dataset				)
										else:
														lowercase    :      Optional[int]       = self.get_dataloader("""train"""			, self.hparams.train_batch_size			, shuffle=__magic_name__				)
														lowercase    :      Any       = len(self.train_dataloader().dataset				)






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :str			, __magic_name__  :int			, __magic_name__  :bool = False				)      ->Optional[Any]:
										raise NotImplementedError("""You must implement this for your task"""				)






						def      __snake_case      (		self  :int				)      ->List[str]:
										return self.train_loader






						def      __snake_case      (		self  :str				)      ->List[str]:
										return self.get_dataloader("""dev"""			, self.hparams.eval_batch_size			, shuffle=__magic_name__				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->List[Any]:
										return self.get_dataloader("""test"""			, self.hparams.eval_batch_size			, shuffle=__magic_name__				)






						def      __snake_case      (		self  :Dict			, __magic_name__  :Union[str, Any]				)      ->Any:
										return os.path.join(
										    self.hparams.data_dir			, """cached_{}_{}_{}""".format(
										        __magic_name__			, list(filter(__magic_name__			, self.hparams.model_name_or_path.split("""/"""				)				)				).pop()			, str(self.hparams.max_seq_length				)			, )			, )






						@pl.utilities.rank_zero_only
						def      __snake_case      (		self  :Dict			, __magic_name__  :Dict[str, Any]				)      ->None:
										lowercase    :      List[Any]       = self.output_dir.joinpath("""best_tfmr"""				)
										lowercase    :      Union[str, Any]       = self.step_count
										self.model.save_pretrained(__magic_name__				)
										self.tokenizer.save_pretrained(__magic_name__				)






						@staticmethod
						def      __snake_case      (		__magic_name__  :List[Any]			, __magic_name__  :Any				)      ->Optional[int]:
										parser.add_argument(
										    """--model_name_or_path"""			, default=__magic_name__			, type=__magic_name__			, required=__magic_name__			, help="""Path to pretrained model or model identifier from huggingface.co/models"""			, )
										parser.add_argument(
										    """--config_name"""			, default=""""""			, type=__magic_name__			, help="""Pretrained config name or path if not the same as model_name"""				)
										parser.add_argument(
										    """--tokenizer_name"""			, default=__magic_name__			, type=__magic_name__			, help="""Pretrained tokenizer name or path if not the same as model_name"""			, )
										parser.add_argument(
										    """--cache_dir"""			, default=str(Path(__magic_name__				).parent / """test_run""" / """cache"""				)			, type=__magic_name__			, help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""			, )
										parser.add_argument(
										    """--encoder_layerdrop"""			, type=__magic_name__			, help="""Encoder layer dropout probability (Optional). Goes into model.config"""			, )
										parser.add_argument(
										    """--decoder_layerdrop"""			, type=__magic_name__			, help="""Decoder layer dropout probability (Optional). Goes into model.config"""			, )
										parser.add_argument(
										    """--dropout"""			, type=__magic_name__			, help="""Dropout probability (Optional). Goes into model.config"""			, )
										parser.add_argument(
										    """--attention_dropout"""			, type=__magic_name__			, help="""Attention dropout probability (Optional). Goes into model.config"""			, )
										parser.add_argument("""--learning_rate"""			, default=5E-5			, type=__magic_name__			, help="""The initial learning rate for Adam."""				)
										parser.add_argument(
										    """--lr_scheduler"""			, default="""linear"""			, choices=__magic_name__			, metavar=__magic_name__			, type=__magic_name__			, help="""Learning rate scheduler"""			, )
										parser.add_argument("""--weight_decay"""			, default=0.0			, type=__magic_name__			, help="""Weight decay if we apply some."""				)
										parser.add_argument("""--adam_epsilon"""			, default=1E-8			, type=__magic_name__			, help="""Epsilon for Adam optimizer."""				)
										parser.add_argument("""--warmup_steps"""			, default=0			, type=__magic_name__			, help="""Linear warmup over warmup_steps."""				)
										parser.add_argument("""--num_workers"""			, default=4			, type=__magic_name__			, help="""kwarg passed to DataLoader"""				)
										parser.add_argument("""--num_train_epochs"""			, dest="""max_epochs"""			, default=3			, type=__magic_name__				)
										parser.add_argument("""--train_batch_size"""			, default=32			, type=__magic_name__				)
										parser.add_argument("""--eval_batch_size"""			, default=32			, type=__magic_name__				)
										parser.add_argument("""--adafactor"""			, action="""store_true"""				)


class      UpperCamelCase		(pl.Callback     ):






						def      __snake_case      (		self  :int			, __magic_name__  :Tuple			, __magic_name__  :Union[str, Any]				)      ->Tuple:
										if (
										    trainer.is_global_zero and trainer.global_rank == 0
										):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
														pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class      UpperCamelCase		(pl.Callback     ):






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Union[str, Any]			, __magic_name__  :Union[str, Any]				)      ->Tuple:
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
														if param.grad is None:
																		print(__magic_name__				)


class      UpperCamelCase		(pl.Callback     ):






						def      __snake_case      (		self  :List[str]			, __magic_name__  :Dict			, __magic_name__  :str				)      ->List[Any]:
										lowercase    :      str       = trainer.lr_schedulers[0]["""scheduler"""]
										lowercase    :      List[str]       = {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
										pl_module.logger.log_metrics(__magic_name__				)






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :pl.Trainer			, __magic_name__  :pl.LightningModule				)      ->List[str]:
										rank_zero_info("""***** Validation results *****"""				)
										lowercase    :      Optional[Any]       = trainer.callback_metrics
										# Log results
										for key in sorted(__magic_name__				):
														if key not in ["log", "progress_bar"]:
																		rank_zero_info("""{} = {}\n""".format(__magic_name__			, str(metrics[key]				)				)				)






						def      __snake_case      (		self  :List[str]			, __magic_name__  :pl.Trainer			, __magic_name__  :pl.LightningModule				)      ->Any:
										rank_zero_info("""***** Test results *****"""				)
										lowercase    :      List[Any]       = trainer.callback_metrics
										# Log and save results to file
										lowercase    :      Tuple       = os.path.join(pl_module.hparams.output_dir			, """test_results.txt"""				)
										with open(__magic_name__			, """w"""				) as writer:
														for key in sorted(__magic_name__				):
																		if key not in ["log", "progress_bar"]:
																						rank_zero_info("""{} = {}\n""".format(__magic_name__			, str(metrics[key]				)				)				)
																						writer.write("""{} = {}\n""".format(__magic_name__			, str(metrics[key]				)				)				)


def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    """--output_dir"""  ,				default=str(Path(_A ).parent / """test_run""" / """model_checkpoints""" )  ,				type=_A  ,				help="""The output directory where the model predictions and checkpoints will be written."""  ,				)
				parser.add_argument(
				    """--fp16"""  ,				action="""store_true"""  ,				help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""  ,				)

				parser.add_argument(
				    """--fp16_opt_level"""  ,				type=_A  ,				default="""O2"""  ,				help=(
				        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
				        """See details at https://nvidia.github.io/apex/amp.html"""
				    )  ,				)
				parser.add_argument("""--n_tpu_cores"""  ,				dest="""tpu_cores"""  ,				type=_A )
				parser.add_argument("""--max_grad_norm"""  ,				dest="""gradient_clip_val"""  ,				default=1.0  ,				type=_A  ,				help="""Max gradient norm""" )
				parser.add_argument("""--do_train"""  ,				action="""store_true"""  ,				help="""Whether to run training.""" )
				parser.add_argument("""--do_predict"""  ,				action="""store_true"""  ,				help="""Whether to run predictions on the test set.""" )
				parser.add_argument(
				    """--gradient_accumulation_steps"""  ,				dest="""accumulate_grad_batches"""  ,				type=_A  ,				default=1  ,				help="""Number of updates steps to accumulate before performing a backward/update pass."""  ,				)
				parser.add_argument("""--seed"""  ,				type=_A  ,				default=42  ,				help="""random seed for initialization""" )
				parser.add_argument(
				    """--data_dir"""  ,				default=str(Path(_A ).parent / """test_run""" / """dummy-train-data""" )  ,				type=_A  ,				help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""  ,				)


def      UpperCamelCase       ( _A  ,				_A  ,				_A=None  ,				_A=True  ,				_A=[]  ,				_A=None  ,				_A=None  ,				**_A  ,				)     ->       Optional[int]:
				pl.seed_everything(args.seed )

				# init model
				lowercase    :      int       = Path(model.hparams.output_dir )
				odir.mkdir(exist_ok=_A )

				# add custom checkpoints
				if checkpoint_callback is None:
								lowercase    :      List[Any]       = pl.callbacks.ModelCheckpoint(
								    filepath=args.output_dir  ,				prefix="""checkpoint"""  ,				monitor="""val_loss"""  ,				mode="""min"""  ,				save_top_k=1 )
				if early_stopping_callback:
								extra_callbacks.append(_A )
				if logging_callback is None:
								lowercase    :      Optional[Any]       = LoggingCallback()

				lowercase    :      List[str]       = {}

				if args.fpaa:
								lowercase    :      str       = 16

				if args.gpus > 1:
								lowercase    :      Tuple       = """auto"""
								lowercase    :      int       = """ddp"""

				lowercase    :      str       = args.accumulate_grad_batches
				lowercase    :      Union[str, Any]       = None
				lowercase    :      Dict       = """auto"""

				lowercase    :      Union[str, Any]       = pl.Trainer.from_argparse_args(
				    _A  ,				weights_summary=_A  ,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]  ,				logger=_A  ,				val_check_interval=1  ,				num_sanity_val_steps=2  ,				**_A  ,				)

				if args.do_train:
								trainer.fit(_A )

				else:
								print("""RAG modeling tests with new set functions successfuly executed!""" )
				return trainer








"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A )     ->       List[List[ImageInput]]:
				if isinstance(_A  ,				(list, tuple) ) and isinstance(videos[0]  ,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(_A  ,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(_A ):
								return [[videos]]

				raise ValueError(F"""Could not make batched video from {videos}""" )


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		["""pixel_values"""]






						def __init__(		self  :str			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[int, float] = 1 / 255			, __magic_name__  :bool = True			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, **__magic_name__  :List[str]			, )      ->None:
										super().__init__(**__magic_name__				)
										lowercase    :      Union[str, Any]       = size if size is not None else {"""shortest_edge""": 256}
										lowercase    :      Tuple       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      List[Any]       = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										lowercase    :      Tuple       = do_resize
										lowercase    :      Optional[Any]       = size
										lowercase    :      Union[str, Any]       = do_center_crop
										lowercase    :      List[Any]       = crop_size
										lowercase    :      Union[str, Any]       = resample
										lowercase    :      Optional[Any]       = do_rescale
										lowercase    :      int       = rescale_factor
										lowercase    :      Dict       = offset
										lowercase    :      Optional[int]       = do_normalize
										lowercase    :      Optional[Any]       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase    :      Tuple       = image_std if image_std is not None else IMAGENET_STANDARD_STD






						def      __snake_case      (		self  :Any			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->np.ndarray:
										lowercase    :      Optional[int]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										if "shortest_edge" in size:
														lowercase    :      int       = get_resize_output_image_size(__magic_name__			, size["""shortest_edge"""]			, default_to_square=__magic_name__				)
										elif "height" in size and "width" in size:
														lowercase    :      Tuple       = (size["""height"""], size["""width"""])
										else:
														raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
										return resize(__magic_name__			, size=__magic_name__			, resample=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :str			, )      ->np.ndarray:
										lowercase    :      Any       = get_size_dict(__magic_name__				)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
										return center_crop(__magic_name__			, size=(size["""height"""], size["""width"""])			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :np.ndarray			, __magic_name__  :Union[int, float]			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->Any:
										lowercase    :      Optional[int]       = image.astype(np.floataa				)
										if offset:
														lowercase    :      Optional[int]       = image - (scale / 2)
										return rescale(__magic_name__			, scale=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :int			, __magic_name__  :np.ndarray			, __magic_name__  :Union[float, List[float]]			, __magic_name__  :Union[float, List[float]]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Union[str, Any]			, )      ->np.ndarray:
										return normalize(__magic_name__			, mean=__magic_name__			, std=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[ChannelDimension] = ChannelDimension.FIRST			, )      ->np.ndarray:
										if do_resize and size is None or resample is None:
														raise ValueError("""Size and resample must be specified if do_resize is True."""				)

										if do_center_crop and crop_size is None:
														raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

										if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

										if offset and not do_rescale:
														raise ValueError("""For offset, do_rescale must also be set to True."""				)

										# All transformations expect numpy arrays.
										lowercase    :      Optional[Any]       = to_numpy_array(__magic_name__				)

										if do_resize:
														lowercase    :      Any       = self.resize(image=__magic_name__			, size=__magic_name__			, resample=__magic_name__				)

										if do_center_crop:
														lowercase    :      Any       = self.center_crop(__magic_name__			, size=__magic_name__				)

										if do_rescale:
														lowercase    :      Optional[int]       = self.rescale(image=__magic_name__			, scale=__magic_name__			, offset=__magic_name__				)

										if do_normalize:
														lowercase    :      Union[str, Any]       = self.normalize(image=__magic_name__			, mean=__magic_name__			, std=__magic_name__				)

										lowercase    :      Optional[Any]       = to_channel_dimension_format(__magic_name__			, __magic_name__				)
										return image






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :ChannelDimension = ChannelDimension.FIRST			, **__magic_name__  :List[str]			, )      ->PIL.Image.Image:
										lowercase    :      List[Any]       = do_resize if do_resize is not None else self.do_resize
										lowercase    :      Dict       = resample if resample is not None else self.resample
										lowercase    :      Optional[int]       = do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase    :      List[Any]       = do_rescale if do_rescale is not None else self.do_rescale
										lowercase    :      Tuple       = rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase    :      Optional[Any]       = offset if offset is not None else self.offset
										lowercase    :      str       = do_normalize if do_normalize is not None else self.do_normalize
										lowercase    :      List[str]       = image_mean if image_mean is not None else self.image_mean
										lowercase    :      Union[str, Any]       = image_std if image_std is not None else self.image_std

										lowercase    :      Optional[int]       = size if size is not None else self.size
										lowercase    :      Optional[Any]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      Tuple       = crop_size if crop_size is not None else self.crop_size
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										if not valid_images(__magic_name__				):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

										lowercase    :      Tuple       = make_batched(__magic_name__				)

										lowercase    :      List[str]       = [
										    [
										        self._preprocess_image(
										            image=__magic_name__			, do_resize=__magic_name__			, size=__magic_name__			, resample=__magic_name__			, do_center_crop=__magic_name__			, crop_size=__magic_name__			, do_rescale=__magic_name__			, rescale_factor=__magic_name__			, offset=__magic_name__			, do_normalize=__magic_name__			, image_mean=__magic_name__			, image_std=__magic_name__			, data_format=__magic_name__			, )
										        for img in video
										    ]
										    for video in videos
										]

										lowercase    :      Dict       = {"""pixel_values""": videos}
										return BatchFeature(data=__magic_name__			, tensor_type=__magic_name__				)
