





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					:				List[Any]  							=					logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowerCamelCase					:				Optional[Any]  							=					{
    """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					:				int  							=					{
    """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					:				str  							=					sorted(arg_to_scheduler.keys())
_lowerCamelCase					:				List[str]  							=					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class 						lowerCamelCase   (pl.LightningModule			):







							"""simple docstring"""




							def __init__(					self	:     int, _UpperCAmelCase	:     argparse.Namespace, _UpperCAmelCase	:     Optional[Any]=None, _UpperCAmelCase	:     List[Any]="base", _UpperCAmelCase	:     Tuple=None, _UpperCAmelCase	:     int=None, _UpperCAmelCase	:     Tuple=None, **_UpperCAmelCase	:     int, )      -> Optional[Any]:





														"""simple docstring"""
														super().__init__()
														# TODO: move to self.save_hyperparameters()
														# self.save_hyperparameters()
														# can also expand arguments into trainer signature for easier reading

														self.save_hyperparameters(snake_case__						)
														SCREAMING_SNAKE_CASE__   :			int      =						0
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						Path(self.hparams.output_dir						)
														SCREAMING_SNAKE_CASE__   :			List[str]      =						self.hparams.cache_dir if self.hparams.cache_dir else None
														if config is None:
																					SCREAMING_SNAKE_CASE__   :			Dict      =						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=snake_case__, **snake_case__, )
														else:
																					SCREAMING_SNAKE_CASE__   :			PretrainedConfig      =						config

														SCREAMING_SNAKE_CASE__   :			Tuple      =						("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
														for p in extra_model_params:
																					if getattr(self.hparams, snake_case__, snake_case__						):
																												assert hasattr(self.config, snake_case__						), F'''model config doesn\'t have a `{p}` attribute'''
																												setattr(self.config, snake_case__, getattr(self.hparams, snake_case__						)						)

														if tokenizer is None:
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						AutoTokenizer.from_pretrained(
																					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path, cache_dir=snake_case__, )
														else:
																					SCREAMING_SNAKE_CASE__   :			PreTrainedTokenizer      =						tokenizer
														SCREAMING_SNAKE_CASE__   :			str      =						MODEL_MODES[mode]
														if model is None:
																					SCREAMING_SNAKE_CASE__   :			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=snake_case__, )
														else:
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						model






							def 					A_	(					self	:     Dict, *_UpperCAmelCase	:     List[Any], **_UpperCAmelCase	:     int						)      -> List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			int      =						self.model_type.from_pretrained(*snake_case__, **snake_case__						)






							def 					A_	(					self	:     Union[str, Any]						)      -> Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Dict      =						arg_to_scheduler[self.hparams.lr_scheduler]
														SCREAMING_SNAKE_CASE__   :			Any      =						get_schedule_func(
														    self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps()						)
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						{"scheduler": scheduler, "interval": "step", "frequency": 1}
														return scheduler






							def 					A_	(					self	:     List[Any]						)      -> Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						self.model
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						["bias", "LayerNorm.weight"]
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						[
														    {
														        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
														        "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:
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						Adafactor(
																					    snake_case__, lr=self.hparams.learning_rate, scale_parameter=snake_case__, relative_step=snake_case__						)

														else:
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						AdamW(
																					    snake_case__, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon						)
														SCREAMING_SNAKE_CASE__   :			List[str]      =						optimizer

														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						self.get_lr_scheduler()

														return [optimizer], [scheduler]






							def 					A_	(					self	:     Any, _UpperCAmelCase	:     Optional[int], _UpperCAmelCase	:     Any						)      -> Tuple:





														"""simple docstring"""
														return self.validation_step(snake_case__, snake_case__						)






							def 					A_	(					self	:     Union[str, Any], _UpperCAmelCase	:     Union[str, Any]						)      -> Optional[Any]:





														"""simple docstring"""
														return self.validation_end(snake_case__						)






							def 					A_	(					self	:     Dict						)      -> List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			str      =						max(1, self.hparams.gpus						)  # TODO: consider num_tpu_cores
														SCREAMING_SNAKE_CASE__   :			Dict      =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
														return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






							def 					A_	(					self	:     List[str], _UpperCAmelCase	:     Tuple						)      -> Union[str, Any]:





														"""simple docstring"""
														if stage == "test":
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						len(self.test_dataloader().dataset						)
														else:
																					SCREAMING_SNAKE_CASE__   :			Optional[int]      =						self.get_dataloader("train", self.hparams.train_batch_size, shuffle=snake_case__						)
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						len(self.train_dataloader().dataset						)






							def 					A_	(					self	:     List[str], _UpperCAmelCase	:     str, _UpperCAmelCase	:     int, _UpperCAmelCase	:     bool = False						)      -> Optional[Any]:





														"""simple docstring"""
														raise NotImplementedError("You must implement this for your task"						)






							def 					A_	(					self	:     Any						)      -> int:





														"""simple docstring"""
														return self.train_loader






							def 					A_	(					self	:     Optional[int]						)      -> str:





														"""simple docstring"""
														return self.get_dataloader("dev", self.hparams.eval_batch_size, shuffle=snake_case__						)






							def 					A_	(					self	:     Optional[Any]						)      -> Union[str, Any]:





														"""simple docstring"""
														return self.get_dataloader("test", self.hparams.eval_batch_size, shuffle=snake_case__						)






							def 					A_	(					self	:     str, _UpperCAmelCase	:     Union[str, Any]						)      -> Optional[Any]:





														"""simple docstring"""
														return os.path.join(
														    self.hparams.data_dir, "cached_{}_{}_{}".format(
														        snake_case__, list(filter(snake_case__, self.hparams.model_name_or_path.split("/"						)						)						).pop(), str(self.hparams.max_seq_length						), ), )






							@pl.utilities.rank_zero_only
							def 					A_	(					self	:     Dict, _UpperCAmelCase	:     Dict[str, Any]						)      -> int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			str      =						self.output_dir.joinpath("best_tfmr"						)
														SCREAMING_SNAKE_CASE__   :			Any      =						self.step_count
														self.model.save_pretrained(snake_case__						)
														self.tokenizer.save_pretrained(snake_case__						)






							@staticmethod
							def 					A_	(					_UpperCAmelCase	:     Union[str, Any], _UpperCAmelCase	:     Union[str, Any]						)      -> Union[str, Any]:





														"""simple docstring"""
														parser.add_argument(
														    "--model_name_or_path", default=snake_case__, type=snake_case__, required=snake_case__, help="Path to pretrained model or model identifier from huggingface.co/models", )
														parser.add_argument(
														    "--config_name", default="", type=snake_case__, help="Pretrained config name or path if not the same as model_name"						)
														parser.add_argument(
														    "--tokenizer_name", default=snake_case__, type=snake_case__, help="Pretrained tokenizer name or path if not the same as model_name", )
														parser.add_argument(
														    "--cache_dir", default="", type=snake_case__, help="Where do you want to store the pre-trained models downloaded from huggingface.co", )
														parser.add_argument(
														    "--encoder_layerdrop", type=snake_case__, help="Encoder layer dropout probability (Optional). Goes into model.config", )
														parser.add_argument(
														    "--decoder_layerdrop", type=snake_case__, help="Decoder layer dropout probability (Optional). Goes into model.config", )
														parser.add_argument(
														    "--dropout", type=snake_case__, help="Dropout probability (Optional). Goes into model.config", )
														parser.add_argument(
														    "--attention_dropout", type=snake_case__, help="Attention dropout probability (Optional). Goes into model.config", )
														parser.add_argument("--learning_rate", default=5E-5, type=snake_case__, help="The initial learning rate for Adam."						)
														parser.add_argument(
														    "--lr_scheduler", default="linear", choices=snake_case__, metavar=snake_case__, type=snake_case__, help="Learning rate scheduler", )
														parser.add_argument("--weight_decay", default=0.0, type=snake_case__, help="Weight decay if we apply some."						)
														parser.add_argument("--adam_epsilon", default=1E-8, type=snake_case__, help="Epsilon for Adam optimizer."						)
														parser.add_argument("--warmup_steps", default=0, type=snake_case__, help="Linear warmup over warmup_steps."						)
														parser.add_argument("--num_workers", default=4, type=snake_case__, help="kwarg passed to DataLoader"						)
														parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=snake_case__						)
														parser.add_argument("--train_batch_size", default=3_2, type=snake_case__						)
														parser.add_argument("--eval_batch_size", default=3_2, type=snake_case__						)
														parser.add_argument("--adafactor", action="store_true"						)


class 						lowerCamelCase   (pl.Callback			):







							"""simple docstring"""




							def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     str, _UpperCAmelCase	:     Optional[Any]						)      -> Optional[int]:





														"""simple docstring"""
														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 						lowerCamelCase   (pl.Callback			):







							"""simple docstring"""




							def 					A_	(					self	:     List[Any], _UpperCAmelCase	:     Union[str, Any], _UpperCAmelCase	:     Tuple						)      -> List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						trainer.lr_schedulers[0]["scheduler"]
														SCREAMING_SNAKE_CASE__   :			Dict      =						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
														pl_module.logger.log_metrics(snake_case__						)






							def 					A_	(					self	:     Dict, _UpperCAmelCase	:     pl.Trainer, _UpperCAmelCase	:     pl.LightningModule						)      -> List[Any]:





														"""simple docstring"""
														rank_zero_info("***** Validation results *****"						)
														SCREAMING_SNAKE_CASE__   :			str      =						trainer.callback_metrics
														# Log results
														for key in sorted(snake_case__						):
																					if key not in ["log", "progress_bar"]:
																												rank_zero_info("{} = {}\n".format(snake_case__, str(metrics[key]						)						)						)






							def 					A_	(					self	:     int, _UpperCAmelCase	:     pl.Trainer, _UpperCAmelCase	:     pl.LightningModule						)      -> Union[str, Any]:





														"""simple docstring"""
														rank_zero_info("***** Test results *****"						)
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						trainer.callback_metrics
														# Log and save results to file
														SCREAMING_SNAKE_CASE__   :			Tuple      =						os.path.join(pl_module.hparams.output_dir, "test_results.txt"						)
														with open(snake_case__, "w"						) as writer:
																					for key in sorted(snake_case__						):
																												if key not in ["log", "progress_bar"]:
																																			rank_zero_info("{} = {}\n".format(snake_case__, str(metrics[key]						)						)						)
																																			writer.write("{} = {}\n".format(snake_case__, str(metrics[key]						)						)						)




def _a					(  SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  Union[str, Any]   )       ->				None:

							'''simple docstring'''






							parser.add_argument(
							    "--output_dir" ,      default=SCREAMING_SNAKE_CASE__ ,      type=SCREAMING_SNAKE_CASE__ ,      required=SCREAMING_SNAKE_CASE__ ,      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=SCREAMING_SNAKE_CASE__ ,      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=SCREAMING_SNAKE_CASE__   )
							parser.add_argument("--max_grad_norm" ,      dest="gradient_clip_val" ,      default=1.0 ,      type=SCREAMING_SNAKE_CASE__ ,      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=SCREAMING_SNAKE_CASE__ ,      default=1 ,      help="Number of updates steps to accumulate before performing a backward/update pass." ,      )
							parser.add_argument("--seed" ,      type=SCREAMING_SNAKE_CASE__ ,      default=42 ,      help="random seed for initialization"   )
							parser.add_argument(
							    "--data_dir" ,      default=SCREAMING_SNAKE_CASE__ ,      type=SCREAMING_SNAKE_CASE__ ,      required=SCREAMING_SNAKE_CASE__ ,      help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,      )







def _a					(  SCREAMING_SNAKE_CASE__			:  BaseTransformer ,      SCREAMING_SNAKE_CASE__			:  argparse.Namespace ,      SCREAMING_SNAKE_CASE__			:  Any=None ,      SCREAMING_SNAKE_CASE__			:  Tuple=True ,      SCREAMING_SNAKE_CASE__			:  Optional[Any]=None ,      SCREAMING_SNAKE_CASE__			:  Optional[int]=[] ,      SCREAMING_SNAKE_CASE__			:  Any=None ,      SCREAMING_SNAKE_CASE__			:  str=None ,      **SCREAMING_SNAKE_CASE__			:  Optional[Any] ,      )       ->				str:

							'''simple docstring'''






							pl.seed_everything(args.seed   )

							# init model
							SCREAMING_SNAKE_CASE__   :			List[Any]      =						Path(model.hparams.output_dir   )
							odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__   )

							# add custom checkpoints
							if checkpoint_callback is None:
														SCREAMING_SNAKE_CASE__   :			Tuple      =						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(SCREAMING_SNAKE_CASE__   )
							if logging_callback is None:
														SCREAMING_SNAKE_CASE__   :			List[str]      =						LoggingCallback()

							SCREAMING_SNAKE_CASE__   :			int      =						{}

							# TODO: remove with PyTorch 1.6 since pl uses native amp
							if args.fpaa:
														SCREAMING_SNAKE_CASE__   :			Tuple      =						16
														# train_params["amp_level"] = args.fp16_opt_level

							if args.gpus > 1:
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						"auto"  # "ddp"
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						"ddp"

							SCREAMING_SNAKE_CASE__   :			Dict      =						args.accumulate_grad_batches
							SCREAMING_SNAKE_CASE__   :			List[Any]      =						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
							SCREAMING_SNAKE_CASE__   :			List[str]      =						"auto"

							SCREAMING_SNAKE_CASE__   :			Tuple      =						pl.Trainer.from_argparse_args(
							    SCREAMING_SNAKE_CASE__ ,      weights_summary=SCREAMING_SNAKE_CASE__ ,      callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()] ,      logger=SCREAMING_SNAKE_CASE__ ,      **SCREAMING_SNAKE_CASE__ ,      )

							if args.do_train:
														trainer.fit(SCREAMING_SNAKE_CASE__   )

							return trainer














"""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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase     :     Any            =	logging.get_logger(__name__)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =['''pixel_values''']
   def __init__(						self	:     str				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     bool = True				,       snake_case__	:     Union[int, float] = 1 / 2_5_5				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Dict				=				size if size is not None else {"height": 2_2_4, "width": 2_2_4}
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				do_resize
        UpperCAmelCase__		:  Any				=				do_rescale
        UpperCAmelCase__		:  Optional[int]				=				do_normalize
        UpperCAmelCase__		:  Dict				=				size
        UpperCAmelCase__		:  Optional[Any]				=				resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor
        UpperCAmelCase__		:  str				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase__		:  Dict				=				image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				get_size_dict(snake_case__				)
        if "height" not in size or "width" not in size:
             raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'				)
        return resize(
            snake_case__				,       size=(size["height"], size["width"])				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Any				,       snake_case__	:     np.ndarray				,       snake_case__	:     float				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Any				):


        '''simple docstring'''
        return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     str				,       snake_case__	:     np.ndarray				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        return normalize(snake_case__				,       mean=snake_case__				,       std=snake_case__				,       data_format=snake_case__				,       **snake_case__				)




   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     ImageInput				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Dict[str, int] = None				,       snake_case__	:     PILImageResampling = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[float] = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[str, TensorType]] = None				,       snake_case__	:     Union[str, ChannelDimension] = ChannelDimension.FIRST				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__		:  Optional[Any]				=				do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__		:  str				=				do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__		:  Dict				=				resample if resample is not None else self.resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__		:  Optional[int]				=				image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__		:  Optional[int]				=				image_std if image_std is not None else self.image_std

        UpperCAmelCase__		:  Any				=				size if size is not None else self.size
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)

        UpperCAmelCase__		:  List[Any]				=				make_list_of_images(snake_case__				)

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

        if do_resize and size is None:
             raise ValueError("Size must be specified if do_resize is True."				)

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

        # All transformations expect numpy arrays.
        UpperCAmelCase__		:  Optional[int]				=				[to_numpy_array(snake_case__				) for image in images]

        if do_resize:
             UpperCAmelCase__		:  List[Any]				=				[self.resize(image=snake_case__				,       size=snake_case__				,       resample=snake_case__				) for image in images]

        if do_rescale:
             UpperCAmelCase__		:  Tuple				=				[self.rescale(image=snake_case__				,       scale=snake_case__				) for image in images]

        if do_normalize:
             UpperCAmelCase__		:  List[Any]				=				[self.normalize(image=snake_case__				,       mean=snake_case__				,       std=snake_case__				) for image in images]

        UpperCAmelCase__		:  Any				=				[to_channel_dimension_format(snake_case__				,       snake_case__				) for image in images]

        UpperCAmelCase__		:  str				=				{"pixel_values": images}
        return BatchFeature(data=snake_case__				,       tensor_type=snake_case__				)


