


import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


SCREAMING_SNAKE_CASE			: Dict				   =					logging.getLogger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      str       					=     """summarization"""
       lowercase_					:      List[str]       					=     ["""loss"""]
       lowercase_					:      Union[str, Any]       					=     ROUGE_KEYS
       lowercase_					:      int       					=     """rouge2"""
       def __init__(   self, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           if hparams.sortish_sampler and hparams.gpus > 1:
               _lowercase			:				Optional[int] 						=						False
           elif hparams.max_tokens_per_batch is not None:
               if hparams.gpus > 1:
                   raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training')
               if hparams.sortish_sampler:
                   raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously')

           super().__init__(lowerCamelCase, num_labels=lowerCamelCase, mode=self.mode, **lowerCamelCase)
           use_task_specific_params(self.model, 'summarization')
           save_git_info(self.hparams.output_dir)
           _lowercase			:				Any 						=						Path(self.output_dir) / 'metrics.json'
           _lowercase			:				Optional[Any] 						=						Path(self.output_dir) / 'hparams.pkl'
           pickle_save(self.hparams, self.hparams_save_path)
           _lowercase			:				Tuple 						=						0
           _lowercase			:				Tuple 						=						defaultdict(lowerCamelCase)
           _lowercase			:				Any 						=						self.config.model_type
           _lowercase			:				Dict 						=						self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

           _lowercase			:				dict 						=						{
               "data_dir": self.hparams.data_dir,
               "max_source_length": self.hparams.max_source_length,
               "prefix": self.model.config.prefix or "",
           }
           _lowercase			:				Optional[int] 						=						{
               'train': self.hparams.n_train,
               'val': self.hparams.n_val,
               'test': self.hparams.n_test,
           }
           _lowercase			:				Optional[int] 						=						{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

           _lowercase			:				List[str] 						=						{
               'train': self.hparams.max_target_length,
               'val': self.hparams.val_max_target_length,
               'test': self.hparams.test_max_target_length,
           }
           assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
           assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
           if self.hparams.freeze_embeds:
               freeze_embeds(self.model)
           if self.hparams.freeze_encoder:
               freeze_params(self.model.get_encoder())
               assert_all_frozen(self.model.get_encoder())

           _lowercase			:				int 						=						get_git_info()['repo_sha']
           _lowercase			:				str 						=						hparams.num_workers
           _lowercase			:				str 						=						None  # default to config
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, lowerCamelCase):
               _lowercase			:				Dict 						=						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
               _lowercase			:				List[str] 						=						self.decoder_start_token_id
           _lowercase			:				str 						=						(
               SeqaSeqDataset if hasattr(self.tokenizer, 'prepare_seq2seq_batch') else LegacySeqaSeqDataset
           )
           _lowercase			:				Tuple 						=						False
           _lowercase			:				List[str] 						=						self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
           if self.hparams.eval_max_gen_length is not None:
               _lowercase			:				Optional[Any] 						=						self.hparams.eval_max_gen_length
           else:
               _lowercase			:				List[Any] 						=						self.model.config.max_length
           _lowercase			:				Any 						=						self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict[str, List[str]]:




           """simple docstring"""

           _lowercase			:				str 						=						{
               k: self.tokenizer.batch_decode(v.tolist()) if 'mask' not in k else v.shape for k, v in batch.items()
           }
           save_json(lowerCamelCase, Path(self.output_dir) / 'text_batch.json')
           save_json({k: v.tolist() for k, v in batch.items()}, Path(self.output_dir) / 'tok_batch.json')

           _lowercase			:				Optional[Any] 						=						True
           return readable_batch
       def      UpperCamelCase	(   self, lowerCamelCase, **lowerCamelCase)			->  Dict:




           """simple docstring"""

           return self.model(lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.tokenizer.batch_decode(
               lowerCamelCase, skip_special_tokens=lowerCamelCase, clean_up_tokenization_spaces=lowerCamelCase)
           return lmap(str.strip, lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.tokenizer.pad_token_id
           _lowercase						,							_lowercase			:				int 						=						batch['input_ids'], batch['attention_mask']
           _lowercase			:				Dict 						=						batch['labels']
           if isinstance(self.model, lowerCamelCase):
               _lowercase			:				int 						=						self.model._shift_right(lowerCamelCase)
           else:
               _lowercase			:				Optional[int] 						=						shift_tokens_right(lowerCamelCase, lowerCamelCase)
           if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
               _lowercase			:				int 						=						decoder_input_ids
               self.save_readable_batch(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						self(lowerCamelCase, attention_mask=lowerCamelCase, decoder_input_ids=lowerCamelCase, use_cache=lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						outputs['logits']
           if self.hparams.label_smoothing == 0:
               # Same behavior as modeling_bart.py, besides ignoring pad_token_id
               _lowercase			:				List[Any] 						=						nn.CrossEntropyLoss(ignore_index=lowerCamelCase)

               assert lm_logits.shape[-1] == self.vocab_size
               _lowercase			:				List[Any] 						=						ce_loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), tgt_ids.view(-1))
           else:
               _lowercase			:				Union[str, Any] 						=						nn.functional.log_softmax(lowerCamelCase, dim=-1)
               _lowercase						,							_lowercase			:				Dict 						=						label_smoothed_nll_loss(
                   lowerCamelCase, lowerCamelCase, self.hparams.label_smoothing, ignore_index=lowerCamelCase)
           return (loss,)
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return self.tokenizer.pad_token_id
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self._step(lowerCamelCase)

           _lowercase			:				Tuple 						=						dict(zip(self.loss_names, lowerCamelCase))
           # tokens per batch
           _lowercase			:				List[str] 						=						batch['input_ids'].ne(self.pad).sum() + batch['labels'].ne(self.pad).sum()
           _lowercase			:				Dict 						=						batch['input_ids'].shape[0]
           _lowercase			:				Any 						=						batch['input_ids'].eq(self.pad).sum()
           _lowercase			:				str 						=						batch['input_ids'].eq(self.pad).float().mean()
           # TODO(SS): make a wandb summary metric for this
           return {"loss": loss_tensors[0], "log": logs}
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Dict:




           """simple docstring"""

           return self._generative_step(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase="val")			->  Dict:




           """simple docstring"""

           self.step_count += 1
           _lowercase			:				Union[str, Any] 						=						{k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
           _lowercase			:				Optional[Any] 						=						losses['loss']
           _lowercase			:				Any 						=						{
               k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['gen_time', 'gen_len']
           }
           _lowercase			:				Tuple 						=						(
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           _lowercase			:				torch.FloatTensor 						=						torch.tensor(lowerCamelCase).type_as(lowerCamelCase)
           generative_metrics.update({k: v.item() for k, v in losses.items()})
           losses.update(lowerCamelCase)
           _lowercase			:				Dict 						=						{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
           _lowercase			:				Dict 						=						self.step_count
           self.metrics[prefix].append(lowerCamelCase)  # callback writes this to self.metrics_save_path
           _lowercase			:				Optional[Any] 						=						flatten_list([x['preds'] for x in outputs])
           return {
               "log": all_metrics,
               "preds": preds,
               F'''{prefix}_loss''': loss,
               F'''{prefix}_{self.val_metric}''': metric_tensor,
           }
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Dict:




           """simple docstring"""

           return calculate_rouge(lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  dict:




           """simple docstring"""

           _lowercase			:				List[str] 						=						time.time()

           # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
           _lowercase			:				int 						=						self.model.generate(
               batch['input_ids'], attention_mask=batch['attention_mask'], use_cache=lowerCamelCase, decoder_start_token_id=self.decoder_start_token_id, num_beams=self.eval_beams, max_length=self.eval_max_length, )
           _lowercase			:				Optional[Any] 						=						(time.time() - ta) / batch['input_ids'].shape[0]
           _lowercase			:				List[str] 						=						self.ids_to_clean_text(lowerCamelCase)
           _lowercase			:				List[str] 						=						self.ids_to_clean_text(batch['labels'])
           _lowercase			:				List[str] 						=						self._step(lowerCamelCase)
           _lowercase			:				Optional[int] 						=						dict(zip(self.loss_names, lowerCamelCase))
           _lowercase			:				Dict 						=						self.calc_generative_metrics(lowerCamelCase, lowerCamelCase)
           _lowercase			:				Optional[int] 						=						np.mean(lmap(lowerCamelCase, lowerCamelCase))
           base_metrics.update(gen_time=lowerCamelCase, gen_len=lowerCamelCase, preds=lowerCamelCase, target=lowerCamelCase, **lowerCamelCase)
           return base_metrics
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           return self._generative_step(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           return self.validation_epoch_end(lowerCamelCase, prefix='test')
       def      UpperCamelCase	(   self, lowerCamelCase)			->  SeqaSeqDataset:




           """simple docstring"""

           _lowercase			:				int 						=						self.n_obs[type_path]
           _lowercase			:				Union[str, Any] 						=						self.target_lens[type_path]
           _lowercase			:				Dict 						=						self.dataset_class(
               self.tokenizer, type_path=lowerCamelCase, n_obs=lowerCamelCase, max_target_length=lowerCamelCase, **self.dataset_kwargs, )
           return dataset
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = False)			->  DataLoader:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.get_dataset(lowerCamelCase)

           if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
               _lowercase			:				Dict 						=						dataset.make_sortish_sampler(lowerCamelCase, distributed=self.hparams.gpus > 1)
               return DataLoader(
                   lowerCamelCase, batch_size=lowerCamelCase, collate_fn=dataset.collate_fn, shuffle=lowerCamelCase, num_workers=self.num_workers, sampler=lowerCamelCase, )

           elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
               _lowercase			:				Dict 						=						dataset.make_dynamic_sampler(
                   self.hparams.max_tokens_per_batch, distributed=self.hparams.gpus > 1)
               return DataLoader(
                   lowerCamelCase, batch_sampler=lowerCamelCase, collate_fn=dataset.collate_fn, num_workers=self.num_workers, )
           else:
               return DataLoader(
                   lowerCamelCase, batch_size=lowerCamelCase, collate_fn=dataset.collate_fn, shuffle=lowerCamelCase, num_workers=self.num_workers, sampler=lowerCamelCase, )
       def      UpperCamelCase	(   self)			->  DataLoader:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.get_dataloader('train', batch_size=self.hparams.train_batch_size, shuffle=lowerCamelCase)
           return dataloader
       def      UpperCamelCase	(   self)			->  DataLoader:




           """simple docstring"""

           return self.get_dataloader('val', batch_size=self.hparams.eval_batch_size)
       def      UpperCamelCase	(   self)			->  DataLoader:




           """simple docstring"""

           return self.get_dataloader('test', batch_size=self.hparams.eval_batch_size)




       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           BaseTransformer.add_model_specific_args(lowerCamelCase, lowerCamelCase)
           add_generic_args(lowerCamelCase, lowerCamelCase)
           parser.add_argument(
               '--max_source_length', default=10_24, type=lowerCamelCase, help=(
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               ), )
           parser.add_argument(
               '--max_target_length', default=56, type=lowerCamelCase, help=(
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               ), )
           parser.add_argument(
               '--val_max_target_length', default=1_42, type=lowerCamelCase, help=(
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               ), )
           parser.add_argument(
               '--test_max_target_length', default=1_42, type=lowerCamelCase, help=(
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               ), )
           parser.add_argument('--freeze_encoder', action='store_true')
           parser.add_argument('--freeze_embeds', action='store_true')
           parser.add_argument('--sortish_sampler', action='store_true', default=lowerCamelCase)
           parser.add_argument('--overwrite_output_dir', action='store_true', default=lowerCamelCase)
           parser.add_argument('--max_tokens_per_batch', type=lowerCamelCase, default=lowerCamelCase)
           parser.add_argument('--logger_name', type=lowerCamelCase, choices=['default', 'wandb', 'wandb_shared'], default='default')
           parser.add_argument('--n_train', type=lowerCamelCase, default=-1, required=lowerCamelCase, help='# examples. -1 means use all.')
           parser.add_argument('--n_val', type=lowerCamelCase, default=5_00, required=lowerCamelCase, help='# examples. -1 means use all.')
           parser.add_argument('--n_test', type=lowerCamelCase, default=-1, required=lowerCamelCase, help='# examples. -1 means use all.')
           parser.add_argument(
               '--task', type=lowerCamelCase, default='summarization', required=lowerCamelCase, help='# examples. -1 means use all.')
           parser.add_argument('--label_smoothing', type=lowerCamelCase, default=0.0, required=lowerCamelCase)
           parser.add_argument('--src_lang', type=lowerCamelCase, default='', required=lowerCamelCase)
           parser.add_argument('--tgt_lang', type=lowerCamelCase, default='', required=lowerCamelCase)
           parser.add_argument('--eval_beams', type=lowerCamelCase, default=lowerCamelCase, required=lowerCamelCase)
           parser.add_argument(
               '--val_metric', type=lowerCamelCase, default=lowerCamelCase, required=lowerCamelCase, choices=['bleu', 'rouge2', 'loss', None])
           parser.add_argument('--eval_max_gen_length', type=lowerCamelCase, default=lowerCamelCase, help='never generate more than n tokens')
           parser.add_argument('--save_top_k', type=lowerCamelCase, default=1, required=lowerCamelCase, help='How many checkpoints to save')
           parser.add_argument(
               '--early_stopping_patience', type=lowerCamelCase, default=-1, required=lowerCamelCase, help=(
                   '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
                   ' val_check_interval will effect it.'
               ), )
           return parser






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[Any]       					=     """translation"""
       lowercase_					:      List[Any]       					=     ["""loss"""]
       lowercase_					:      int       					=     ["""bleu"""]
       lowercase_					:      Dict       					=     """bleu"""
       def __init__(   self, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           super().__init__(lowerCamelCase, **lowerCamelCase)
           _lowercase			:				Dict 						=						hparams.src_lang
           _lowercase			:				int 						=						hparams.tgt_lang




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  dict:




           """simple docstring"""

           return calculate_bleu(lowerCamelCase, lowerCamelCase)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		SummarizationModule:
    Path(args.output_dir					).mkdir(exist_ok=lowerCamelCase_					)
    check_output_dir(lowerCamelCase_				,					expected_items=3					)

    if model is None:
        if "summarization" in args.task:
            _lowercase			:				SummarizationModule 						=						SummarizationModule(lowerCamelCase_					)
        else:
            _lowercase			:				SummarizationModule 						=						TranslationModule(lowerCamelCase_					)
    _lowercase			:				Tuple 						=						Path(args.data_dir					).name
    if (
        args.logger_name == "default"
        or args.fast_dev_run
        or str(args.output_dir					).startswith('/tmp'					)
        or str(args.output_dir					).startswith('/var'					)
    ):
        _lowercase			:				Dict 						=						True  # don't pollute wandb logs unnecessarily
    elif args.logger_name == "wandb":
        from pytorch_lightning.loggers import WandbLogger

        _lowercase			:				Optional[Any] 						=						os.environ.get('WANDB_PROJECT'				,					lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						WandbLogger(name=model.output_dir.name				,					project=lowerCamelCase_					)

    elif args.logger_name == "wandb_shared":
        from pytorch_lightning.loggers import WandbLogger

        _lowercase			:				List[str] 						=						WandbLogger(name=model.output_dir.name				,					project=F'''hf_{dataset}'''					)

    if args.early_stopping_patience >= 0:
        _lowercase			:				Any 						=						get_early_stopping_callback(model.val_metric				,					args.early_stopping_patience					)
    else:
        _lowercase			:				Union[str, Any] 						=						False

    _lowercase			:				Optional[int] 						=						args.val_metric == 'loss'
    _lowercase			:				pl.Trainer 						=						generic_train(
        lowerCamelCase_				,					lowerCamelCase_				,					logging_callback=SeqaSeqLoggingCallback()				,					checkpoint_callback=get_checkpoint_callback(
            args.output_dir				,					model.val_metric				,					args.save_top_k				,					lowerCamelCase_					)				,					early_stopping_callback=lowerCamelCase_				,					logger=lowerCamelCase_				,					)
    pickle_save(model.hparams				,					model.output_dir / 'hparams.pkl'					)
    if not args.do_predict:
        return model

    _lowercase			:				int 						=						''
    _lowercase			:				int 						=						sorted(glob.glob(os.path.join(args.output_dir				,					'*.ckpt'					)				,					recursive=lowerCamelCase_					)					)
    if checkpoints:
        _lowercase			:				List[str] 						=						checkpoints[-1]
        _lowercase			:				str 						=						checkpoints[-1]
    trainer.logger.log_hyperparams(model.hparams					)

    # test() without a model tests using the best checkpoint automatically
    trainer.test()
    return model


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: int				   =					argparse.ArgumentParser()
 SCREAMING_SNAKE_CASE			: str				   =					pl.Trainer.add_argparse_args(parser)
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					SummarizationModule.add_model_specific_args(parser, os.getcwd())

 SCREAMING_SNAKE_CASE			: int				   =					parser.parse_args()

 main(args)









'''simple docstring'''


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				int								=       ["pixel_values"]




 def __init__(						self      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = 1 / 255      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = IMAGENET_DEFAULT_MEAN      ,  _lowerCAmelCase = IMAGENET_DEFAULT_STD      ,  **_lowerCAmelCase      ,  )     ->				None:
     super().__init__(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   size if size is not None else {"shortest_edge": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   do_resize
     _lowerCAmelCase						      =   size
     _lowerCAmelCase						      =   resample
     _lowerCAmelCase						      =   do_center_crop
     _lowerCAmelCase						      =   crop_size
     _lowerCAmelCase						      =   do_rescale
     _lowerCAmelCase						      =   rescale_factor
     _lowerCAmelCase						      =   do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     _lowerCAmelCase						      =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
         _lowerCAmelCase						      =   int((256 / 224) * size["shortest_edge"]		)
         _lowerCAmelCase						      =   get_resize_output_image_size(_lowerCAmelCase      ,  size=_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
         _lowerCAmelCase						      =   {"height": output_size[0], "width": output_size[1]}
     if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
     return resize(
         _lowerCAmelCase      ,  size=(size_dict["height"], size_dict["width"])      ,  resample=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase		)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return center_crop(_lowerCAmelCase      ,  size=(size["height"], size["width"])      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return rescale(_lowerCAmelCase      ,  scale=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return normalize(_lowerCAmelCase      ,  mean=_lowerCAmelCase      ,  std=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = ChannelDimension.FIRST      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase						      =   resample if resample is not None else self.resample
     _lowerCAmelCase						      =   do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase						      =   do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase						      =   rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase						      =   do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase						      =   image_std if image_std is not None else self.image_std

     _lowerCAmelCase						      =   size if size is not None else self.size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   make_list_of_images(_lowerCAmelCase		)

     if not valid_images(_lowerCAmelCase		):
         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_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."		)

     # All transformations expect numpy arrays.
     _lowerCAmelCase						      =   [to_numpy_array(_lowerCAmelCase		) for image in images]

     if do_resize:
         _lowerCAmelCase						      =   [self.resize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     if do_center_crop:
         _lowerCAmelCase						      =   [self.center_crop(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     if do_rescale:
         _lowerCAmelCase						      =   [self.rescale(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     if do_normalize:
         _lowerCAmelCase						      =   [self.normalize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   [to_channel_dimension_format(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase      ,  tensor_type=_lowerCAmelCase		)


