






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


__UpperCAmelCase								=						logging.getLogger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    List[Any]       						=  '''summarization'''
		_snake_case    :    Optional[int]       						=  ['''loss''']
		_snake_case    :    str       						=  ROUGE_KEYS
		_snake_case    :    Tuple       						=  '''rouge2'''



		def __init__(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							if hparams.sortish_sampler and hparams.gpus > 1:
												UpperCAmelCase_    :      Union[str, Any]            =						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__(_UpperCamelCase				,		num_labels=_UpperCamelCase				,		mode=self.mode				,		**_UpperCamelCase   )
							use_task_specific_params(self.model				,		'summarization'   )
							save_git_info(self.hparams.output_dir   )
							UpperCAmelCase_    :      Optional[int]            =						Path(self.output_dir   ) / 'metrics.json'
							UpperCAmelCase_    :      str            =						Path(self.output_dir   ) / 'hparams.pkl'
							pickle_save(self.hparams				,		self.hparams_save_path   )
							UpperCAmelCase_    :      int            =						0
							UpperCAmelCase_    :      Optional[int]            =						defaultdict(_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						self.config.model_type
							UpperCAmelCase_    :      Any            =						self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

							UpperCAmelCase_    :      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()   )

							UpperCAmelCase_    :      Any            =						get_git_info()['repo_sha']
							UpperCAmelCase_    :      int            =						hparams.num_workers
							UpperCAmelCase_    :      List[Any]            =						None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Dict            =						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
												UpperCAmelCase_    :      Dict            =						self.decoder_start_token_id
							UpperCAmelCase_    :      List[Any]            =						(
							    SeqaSeqDataset if hasattr(self.tokenizer				,		'prepare_seq2seq_batch'   ) else LegacySeqaSeqDataset
							)
							UpperCAmelCase_    :      str            =						False
							UpperCAmelCase_    :      List[Any]            =						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:
												UpperCAmelCase_    :      Optional[int]            =						self.hparams.eval_max_gen_length
							else:
												UpperCAmelCase_    :      Optional[Any]            =						self.model.config.max_length
							UpperCAmelCase_    :      List[str]            =						self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict[str, List[str]]:
							UpperCAmelCase_    :      Optional[Any]            =						{
							    k: self.tokenizer.batch_decode(v.tolist()   ) if 'mask' not in k else v.shape for k, v in batch.items()
							}
							save_json(_UpperCamelCase				,		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'   )

							UpperCAmelCase_    :      Union[str, Any]            =						True
							return readable_batch



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							return self.model(_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Dict            =						self.tokenizer.batch_decode(
							    _UpperCamelCase				,		skip_special_tokens=_UpperCamelCase				,		clean_up_tokenization_spaces=_UpperCamelCase   )
							return lmap(str.strip				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      int            =						self.tokenizer.pad_token_id
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						batch['input_ids'], batch['attention_mask']
							UpperCAmelCase_    :      int            =						batch['labels']
							if isinstance(self.model				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						self.model._shift_right(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      str            =						shift_tokens_right(_UpperCamelCase				,		_UpperCamelCase   )
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
												UpperCAmelCase_    :      Any            =						decoder_input_ids
												self.save_readable_batch(_UpperCamelCase   )

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

												assert lm_logits.shape[-1] == self.vocab_size
												UpperCAmelCase_    :      Optional[int]            =						ce_loss_fct(lm_logits.view(-1				,		lm_logits.shape[-1]   )				,		tgt_ids.view(-1   )   )
							else:
												UpperCAmelCase_    :      Optional[Any]            =						nn.functional.log_softmax(_UpperCamelCase				,		dim=-1   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						label_smoothed_nll_loss(
												    _UpperCamelCase				,		_UpperCamelCase				,		self.hparams.label_smoothing				,		ignore_index=_UpperCamelCase   )
							return (loss,)



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return self.tokenizer.pad_token_id



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      List[Any]            =						self._step(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						dict(zip(self.loss_names				,		_UpperCamelCase   )   )
							# tokens per batch
							UpperCAmelCase_    :      Tuple            =						batch['input_ids'].ne(self.pad   ).sum() + batch['labels'].ne(self.pad   ).sum()
							UpperCAmelCase_    :      Dict            =						batch['input_ids'].shape[0]
							UpperCAmelCase_    :      Optional[int]            =						batch['input_ids'].eq(self.pad   ).sum()
							UpperCAmelCase_    :      List[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				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							return self._generative_step(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase="val"   )	->							Dict:
							self.step_count += 1
							UpperCAmelCase_    :      int            =						{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
							UpperCAmelCase_    :      Optional[Any]            =						losses['loss']
							UpperCAmelCase_    :      Tuple            =						{
							    k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
							}
							UpperCAmelCase_    :      Tuple            =						(
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							UpperCAmelCase_    :      torch.FloatTensor            =						torch.tensor(_UpperCamelCase   ).type_as(_UpperCamelCase   )
							generative_metrics.update({k: v.item() for k, v in losses.items()}   )
							losses.update(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						{f"{prefix}_avg_{k}": x for k, x in losses.items()}
							UpperCAmelCase_    :      Optional[int]            =						self.step_count
							self.metrics[prefix].append(_UpperCamelCase   )  # callback writes this to self.metrics_save_path
							UpperCAmelCase_    :      int            =						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				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							return calculate_rouge(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							dict:
							UpperCAmelCase_    :      Dict            =						time.time()

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							return self._generative_step(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict:
							return self.validation_epoch_end(_UpperCamelCase				,		prefix='test'   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							SeqaSeqDataset:
							UpperCAmelCase_    :      int            =						self.n_obs[type_path]
							UpperCAmelCase_    :      List[str]            =						self.target_lens[type_path]
							UpperCAmelCase_    :      List[str]            =						self.dataset_class(
							    self.tokenizer				,		type_path=_UpperCamelCase				,		n_obs=_UpperCamelCase				,		max_target_length=_UpperCamelCase				,		**self.dataset_kwargs				,		)
							return dataset



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = False   )	->							DataLoader:
							UpperCAmelCase_    :      List[str]            =						self.get_dataset(_UpperCamelCase   )

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
												UpperCAmelCase_    :      Optional[Any]            =						dataset.make_sortish_sampler(_UpperCamelCase				,		distributed=self.hparams.gpus > 1   )
												return DataLoader(
												    _UpperCamelCase				,		batch_size=_UpperCamelCase				,		collate_fn=dataset.collate_fn				,		shuffle=_UpperCamelCase				,		num_workers=self.num_workers				,		sampler=_UpperCamelCase				,		)

							elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
												UpperCAmelCase_    :      str            =						dataset.make_dynamic_sampler(
												    self.hparams.max_tokens_per_batch				,		distributed=self.hparams.gpus > 1   )
												return DataLoader(
												    _UpperCamelCase				,		batch_sampler=_UpperCamelCase				,		collate_fn=dataset.collate_fn				,		num_workers=self.num_workers				,		)
							else:
												return DataLoader(
												    _UpperCamelCase				,		batch_size=_UpperCamelCase				,		collate_fn=dataset.collate_fn				,		shuffle=_UpperCamelCase				,		num_workers=self.num_workers				,		sampler=_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self   )	->							DataLoader:
							UpperCAmelCase_    :      List[Any]            =						self.get_dataloader('train'				,		batch_size=self.hparams.train_batch_size				,		shuffle=_UpperCamelCase   )
							return dataloader



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



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



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							BaseTransformer.add_model_specific_args(_UpperCamelCase				,		_UpperCamelCase   )
							add_generic_args(_UpperCamelCase				,		_UpperCamelCase   )
							parser.add_argument(
							    '--max_source_length'				,		default=1_0_2_4				,		type=_UpperCamelCase				,		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=5_6				,		type=_UpperCamelCase				,		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_4_2				,		type=_UpperCamelCase				,		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_4_2				,		type=_UpperCamelCase				,		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=_UpperCamelCase   )
							parser.add_argument('--overwrite_output_dir'				,		action='store_true'				,		default=_UpperCamelCase   )
							parser.add_argument('--max_tokens_per_batch'				,		type=_UpperCamelCase				,		default=_UpperCamelCase   )
							parser.add_argument('--logger_name'				,		type=_UpperCamelCase				,		choices=['default', 'wandb', 'wandb_shared']				,		default='default'   )
							parser.add_argument('--n_train'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--n_val'				,		type=_UpperCamelCase				,		default=5_0_0				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--n_test'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument(
							    '--task'				,		type=_UpperCamelCase				,		default='summarization'				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--label_smoothing'				,		type=_UpperCamelCase				,		default=0.0				,		required=_UpperCamelCase   )
							parser.add_argument('--src_lang'				,		type=_UpperCamelCase				,		default=''				,		required=_UpperCamelCase   )
							parser.add_argument('--tgt_lang'				,		type=_UpperCamelCase				,		default=''				,		required=_UpperCamelCase   )
							parser.add_argument('--eval_beams'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		required=_UpperCamelCase   )
							parser.add_argument(
							    '--val_metric'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		required=_UpperCamelCase				,		choices=['bleu', 'rouge2', 'loss', None]   )
							parser.add_argument('--eval_max_gen_length'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		help='never generate more than n tokens'   )
							parser.add_argument('--save_top_k'				,		type=_UpperCamelCase				,		default=1				,		required=_UpperCamelCase				,		help='How many checkpoints to save'   )
							parser.add_argument(
							    '--early_stopping_patience'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		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      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    int       						=  '''translation'''
		_snake_case    :    Dict       						=  ['''loss''']
		_snake_case    :    str       						=  ['''bleu''']
		_snake_case    :    str       						=  '''bleu'''



		def __init__(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							super().__init__(_UpperCamelCase				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						hparams.src_lang
							UpperCAmelCase_    :      List[str]            =						hparams.tgt_lang



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							dict:
							return calculate_bleu(_UpperCamelCase				,		_UpperCamelCase   )





def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							List[Any]=None  ):


					'''simple docstring'''


					Path(args.output_dir  ).mkdir(exist_ok=__snake_case  )
					check_output_dir(__snake_case     ,       expected_items=3  )

					if model is None:
										if "summarization" in args.task:
															UpperCAmelCase_    :      SummarizationModule            =						SummarizationModule(__snake_case  )
										else:
															UpperCAmelCase_    :      SummarizationModule            =						TranslationModule(__snake_case  )
					UpperCAmelCase_    :      List[str]            =						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'  )
					):
										UpperCAmelCase_    :      Dict            =						True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

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

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

										UpperCAmelCase_    :      str            =						WandbLogger(name=model.output_dir.name     ,       project=F"hf_{dataset}"  )

					if args.early_stopping_patience >= 0:
										UpperCAmelCase_    :      Any            =						get_early_stopping_callback(model.val_metric     ,       args.early_stopping_patience  )
					else:
										UpperCAmelCase_    :      Dict            =						False

					UpperCAmelCase_    :      Dict            =						args.val_metric == 'loss'
					UpperCAmelCase_    :      pl.Trainer            =						generic_train(
					    __snake_case     ,       __snake_case     ,       logging_callback=SeqaSeqLoggingCallback()     ,       checkpoint_callback=get_checkpoint_callback(
					        args.output_dir     ,       model.val_metric     ,       args.save_top_k     ,       __snake_case  )     ,       early_stopping_callback=__snake_case     ,       logger=__snake_case     ,       )
					pickle_save(model.hparams     ,       model.output_dir / 'hparams.pkl'  )
					if not args.do_predict:
										return model

					UpperCAmelCase_    :      Tuple            =						''
					UpperCAmelCase_    :      List[Any]            =						sorted(glob.glob(os.path.join(args.output_dir     ,       '*.ckpt'  )     ,       recursive=__snake_case  )  )
					if checkpoints:
										UpperCAmelCase_    :      int            =						checkpoints[-1]
										UpperCAmelCase_    :      Optional[Any]            =						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__":
		__UpperCAmelCase								=						argparse.ArgumentParser()
		__UpperCAmelCase								=						pl.Trainer.add_argparse_args(parser)
		__UpperCAmelCase								=						SummarizationModule.add_model_specific_args(parser, os.getcwd())

		__UpperCAmelCase								=						parser.parse_args()

		main(args)








"""simple docstring"""




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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


_A			:       List[Any]   		=       logging.get_logger(__name__)

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""pixel_values"""]
   def __init__(			self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BILINEAR	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = None	,    _a = True	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				Optional[Any]       		=							size if size is not None else {"shortest_edge": 224}
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				str       		=							crop_size if crop_size is not None else {"height": 256, "width": 256}
          lowercase    :				List[str]       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							do_resize
          lowercase    :				Optional[int]       		=							size
          lowercase    :				str       		=							resample
          lowercase    :				List[Any]       		=							do_rescale
          lowercase    :				Union[str, Any]       		=							rescale_factor
          lowercase    :				Optional[int]       		=							do_center_crop
          lowercase    :				Union[str, Any]       		=							crop_size
          lowercase    :				Optional[Any]       		=							do_flip_channel_order
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PIL.Image.BILINEAR	,    _a = None	,    **_a	,    ):
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
          lowercase    :				Union[str, Any]       		=							get_resize_output_image_size(_a	,    size=size["shortest_edge"]	,    default_to_square=_a				)
          return resize(_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				str       		=							get_size_dict(_a				)
          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 center_crop(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          return flip_channel_order(_a	,    data_format=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Optional[Any]       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Tuple       		=							resample if resample is not None else self.resample
          lowercase    :				List[str]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				List[Any]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				Union[str, Any]       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				Optional[int]       		=							(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          lowercase    :				str       		=							size if size is not None else self.size
          lowercase    :				Any       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				int       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Any       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							make_list_of_images(_a				)

          if not valid_images(_a				):
                 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."				)

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

          # All transformations expect numpy arrays.
          lowercase    :				Any       		=							[to_numpy_array(_a				) for image in images]

          if do_resize:
                 lowercase    :				Optional[int]       		=							[self.resize(image=_a	,    size=_a	,    resample=_a				) for image in images]

          if do_center_crop:
                 lowercase    :				str       		=							[self.center_crop(image=_a	,    size=_a				) for image in images]

          if do_rescale:
                 lowercase    :				Union[str, Any]       		=							[self.rescale(image=_a	,    scale=_a				) for image in images]

          # the pretrained checkpoints assume images are BGR, not RGB
          if do_flip_channel_order:
                 lowercase    :				int       		=							[self.flip_channel_order(image=_a				) for image in images]

          lowercase    :				int       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

          lowercase    :				Optional[Any]       		=							{"pixel_values": images}
          return BatchFeature(data=_a	,    tensor_type=_a				)

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[int]       		=							outputs.logits

          # Resize logits and compute semantic segmentation maps
          if target_sizes is not None:
                 if len(_a				) != len(_a				):
                        raise ValueError(
                            "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

                 if is_torch_tensor(_a				):
                        lowercase    :				Tuple       		=							target_sizes.numpy()

                 lowercase    :				List[Any]       		=							[]

                 for idx in range(len(_a				)				):
                        lowercase    :				Dict       		=							torch.nn.functional.interpolate(
                            logits[idx].unsqueeze(dim=0				)	,    size=target_sizes[idx]	,    mode="bilinear"	,    align_corners=_a				)
                        lowercase    :				Tuple       		=							resized_logits[0].argmax(dim=0				)
                        semantic_segmentation.append(_a				)
          else:
                 lowercase    :				str       		=							logits.argmax(dim=1				)
                 lowercase    :				Dict       		=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

          return semantic_segmentation


