
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


_lowerCamelCase	:    Dict          =      logging.getLogger(__name__)




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Union[str, Any]          = "summarization"
	_UpperCAmelCase	:		Optional[Any]          = ["loss"]
	_UpperCAmelCase	:		List[str]          = ROUGE_KEYS
	_UpperCAmelCase	:		Any          = "rouge2"



	def __init__(  self     :   Optional[Any]      ,  lowercase     :   Union[str, Any]      ,  **lowercase     :   List[Any]     ):


				'''simple docstring'''


				if hparams.sortish_sampler and hparams.gpus > 1:
							_snake_case          =      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__(lowercase      ,  num_labels=lowercase      ,  mode=self.mode      ,  **lowercase     )
				use_task_specific_params(self.model      ,  'summarization'     )
				save_git_info(self.hparams.output_dir     )
				_snake_case          =      Path(self.output_dir     ) / 'metrics.json'
				_snake_case          =      Path(self.output_dir     ) / 'hparams.pkl'
				pickle_save(self.hparams      ,  self.hparams_save_path     )
				_snake_case          =      0
				_snake_case          =      defaultdict(lowercase     )
				_snake_case          =      self.config.model_type
				_snake_case          =      self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

				_snake_case          =      {
				    '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()     )

				_snake_case          =      get_git_info()['repo_sha']
				_snake_case          =      hparams.num_workers
				_snake_case          =      None  # default to config
				if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,  lowercase     ):
							_snake_case          =      self.tokenizer.lang_code_to_id[hparams.tgt_lang]
							_snake_case          =      self.decoder_start_token_id
				_snake_case          =      (
				    SeqaSeqDataset if hasattr(self.tokenizer      ,  'prepare_seq2seq_batch'     ) else LegacySeqaSeqDataset
				)
				_snake_case          =      False
				_snake_case          =      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:
							_snake_case          =      self.hparams.eval_max_gen_length
				else:
							_snake_case          =      self.model.config.max_length
				_snake_case          =      self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



	def 			A							(  self     :   str      ,  lowercase     :   Dict[str, torch.Tensor]     ):


				'''simple docstring'''


				_snake_case          =      {
				    k: self.tokenizer.batch_decode(v.tolist()     ) if 'mask' not in k else v.shape for k, v in batch.items()
				}
				save_json(lowercase      ,  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'     )

				_snake_case          =      True
				return readable_batch



	def 			A							(  self     :   Dict      ,  lowercase     :   List[Any]      ,  **lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				return self.model(lowercase      ,  **lowercase     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[int]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer.batch_decode(
				    lowercase      ,  skip_special_tokens=lowercase      ,  clean_up_tokenization_spaces=lowercase     )
				return lmap(str.strip      ,  lowercase     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   dict     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer.pad_token_id
				_snake_case     , _snake_case          =      batch['input_ids'], batch['attention_mask']
				_snake_case          =      batch['labels']
				if isinstance(self.model      ,  lowercase     ):
							_snake_case          =      self.model._shift_right(lowercase     )
				else:
							_snake_case          =      shift_tokens_right(lowercase      ,  lowercase     )
				if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
							_snake_case          =      decoder_input_ids
							self.save_readable_batch(lowercase     )

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

							assert lm_logits.shape[-1] == self.vocab_size
							_snake_case          =      ce_loss_fct(lm_logits.view(-1      ,  lm_logits.shape[-1]     )      ,  tgt_ids.view(-1     )     )
				else:
							_snake_case          =      nn.functional.log_softmax(lowercase      ,  dim=-1     )
							_snake_case     , _snake_case          =      label_smoothed_nll_loss(
							    lowercase      ,  lowercase      ,  self.hparams.label_smoothing      ,  ignore_index=lowercase     )
				return (loss,)



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return self.tokenizer.pad_token_id



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      self._step(lowercase     )

				_snake_case          =      dict(zip(self.loss_names      ,  lowercase     )     )
				# tokens per batch
				_snake_case          =      batch['input_ids'].ne(self.pad     ).sum() + batch['labels'].ne(self.pad     ).sum()
				_snake_case          =      batch['input_ids'].shape[0]
				_snake_case          =      batch['input_ids'].eq(self.pad     ).sum()
				_snake_case          =      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 			A							(  self     :   List[str]      ,  lowercase     :   Tuple      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				return self._generative_step(lowercase     )



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Dict      ,  lowercase     :   Any="val"     ):


				'''simple docstring'''


				self.step_count += 1
				_snake_case          =      {k: torch.stack([x[k] for x in outputs]     ).mean() for k in self.loss_names}
				_snake_case          =      losses['loss']
				_snake_case          =      {
				    k: np.array([x[k] for x in outputs]     ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
				}
				_snake_case          =      (
				    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
				)
				_snake_case          =      torch.tensor(lowercase     ).type_as(lowercase     )
				generative_metrics.update({k: v.item() for k, v in losses.items()}     )
				losses.update(lowercase     )
				_snake_case          =      {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
				_snake_case          =      self.step_count
				self.metrics[prefix].append(lowercase     )  # callback writes this to self.metrics_save_path
				_snake_case          =      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 			A							(  self     :   Optional[int]      ,  lowercase     :   List[str]      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				return calculate_rouge(lowercase      ,  lowercase     )



	def 			A							(  self     :   str      ,  lowercase     :   dict     ):


				'''simple docstring'''


				_snake_case          =      time.time()

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



	def 			A							(  self     :   Dict      ,  lowercase     :   Dict      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				return self._generative_step(lowercase     )



	def 			A							(  self     :   Any      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				return self.validation_epoch_end(lowercase      ,  prefix='test'     )



	def 			A							(  self     :   Any      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.n_obs[type_path]
				_snake_case          =      self.target_lens[type_path]
				_snake_case          =      self.dataset_class(
				    self.tokenizer      ,  type_path=lowercase      ,  n_obs=lowercase      ,  max_target_length=lowercase      ,  **self.dataset_kwargs      ,  )
				return dataset



	def 			A							(  self     :   str      ,  lowercase     :   str      ,  lowercase     :   int      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				_snake_case          =      self.get_dataset(lowercase     )

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

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



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dataloader('train'      ,  batch_size=self.hparams.train_batch_size      ,  shuffle=lowercase     )
				return dataloader



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


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





	@staticmethod
	def 			A							(  lowercase     :   Union[str, Any]      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				BaseTransformer.add_model_specific_args(lowercase      ,  lowercase     )
				add_generic_args(lowercase      ,  lowercase     )
				parser.add_argument(
				    '--max_source_length'      ,  default=1_024      ,  type=lowercase      ,  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=lowercase      ,  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=142      ,  type=lowercase      ,  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=142      ,  type=lowercase      ,  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=lowercase     )
				parser.add_argument('--overwrite_output_dir'      ,  action='store_true'      ,  default=lowercase     )
				parser.add_argument('--max_tokens_per_batch'      ,  type=lowercase      ,  default=lowercase     )
				parser.add_argument('--logger_name'      ,  type=lowercase      ,  choices=['default', 'wandb', 'wandb_shared']      ,  default='default'     )
				parser.add_argument('--n_train'      ,  type=lowercase      ,  default=-1      ,  required=lowercase      ,  help='# examples. -1 means use all.'     )
				parser.add_argument('--n_val'      ,  type=lowercase      ,  default=500      ,  required=lowercase      ,  help='# examples. -1 means use all.'     )
				parser.add_argument('--n_test'      ,  type=lowercase      ,  default=-1      ,  required=lowercase      ,  help='# examples. -1 means use all.'     )
				parser.add_argument(
				    '--task'      ,  type=lowercase      ,  default='summarization'      ,  required=lowercase      ,  help='# examples. -1 means use all.'     )
				parser.add_argument('--label_smoothing'      ,  type=lowercase      ,  default=0.0      ,  required=lowercase     )
				parser.add_argument('--src_lang'      ,  type=lowercase      ,  default=''      ,  required=lowercase     )
				parser.add_argument('--tgt_lang'      ,  type=lowercase      ,  default=''      ,  required=lowercase     )
				parser.add_argument('--eval_beams'      ,  type=lowercase      ,  default=lowercase      ,  required=lowercase     )
				parser.add_argument(
				    '--val_metric'      ,  type=lowercase      ,  default=lowercase      ,  required=lowercase      ,  choices=['bleu', 'rouge2', 'loss', None]     )
				parser.add_argument('--eval_max_gen_length'      ,  type=lowercase      ,  default=lowercase      ,  help='never generate more than n tokens'     )
				parser.add_argument('--save_top_k'      ,  type=lowercase      ,  default=1      ,  required=lowercase      ,  help='How many checkpoints to save'     )
				parser.add_argument(
				    '--early_stopping_patience'      ,  type=lowercase      ,  default=-1      ,  required=lowercase      ,  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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		str          = "translation"
	_UpperCAmelCase	:		List[str]          = ["loss"]
	_UpperCAmelCase	:		Tuple          = ["bleu"]
	_UpperCAmelCase	:		Union[str, Any]          = "bleu"



	def __init__(  self     :   List[str]      ,  lowercase     :   List[str]      ,  **lowercase     :   int     ):


				'''simple docstring'''


				super().__init__(lowercase      ,  **lowercase     )
				_snake_case          =      hparams.src_lang
				_snake_case          =      hparams.tgt_lang





	def 			A							(  self     :   Dict      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				return calculate_bleu(lowercase      ,  lowercase     )






def 	a_		(	__lowercase       :     List[str]	, __lowercase       :     Optional[Any]=None			)			->  SummarizationModule:
			Path(args.output_dir			).mkdir(exist_ok=__lowercase			)
			check_output_dir(__lowercase	, expected_items=3			)

			if model is None:
						if "summarization" in args.task:
									_snake_case          =      SummarizationModule(__lowercase			)
						else:
									_snake_case          =      TranslationModule(__lowercase			)
			_snake_case          =      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'			)
			):
						_snake_case          =      True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						_snake_case          =      os.environ.get('WANDB_PROJECT'	, __lowercase			)
						_snake_case          =      WandbLogger(name=model.output_dir.name	, project=__lowercase			)

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

						_snake_case          =      WandbLogger(name=model.output_dir.name	, project=f'''hf_{dataset}'''			)

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

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

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

			_lowerCamelCase	:    List[str]          =      parser.parse_args()

			main(args)

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import DeformableDetrImageProcessor




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def __init__(  self     :   int      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   str=7      ,  lowercase     :   Union[str, Any]=3      ,  lowercase     :   Tuple=30      ,  lowercase     :   Optional[Any]=400      ,  lowercase     :   List[Any]=True      ,  lowercase     :   Any=None      ,  lowercase     :   str=True      ,  lowercase     :   Tuple=[0.5, 0.5, 0.5]      ,  lowercase     :   List[Any]=[0.5, 0.5, 0.5]      ,  lowercase     :   Union[str, Any]=True      ,  lowercase     :   List[Any]=1 / 255      ,  lowercase     :   int=True      ,  ):


				'''simple docstring'''


				_snake_case          =      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				_snake_case          =      parent
				_snake_case          =      batch_size
				_snake_case          =      num_channels
				_snake_case          =      min_resolution
				_snake_case          =      max_resolution
				_snake_case          =      do_resize
				_snake_case          =      size
				_snake_case          =      do_normalize
				_snake_case          =      image_mean
				_snake_case          =      image_std
				_snake_case          =      do_rescale
				_snake_case          =      rescale_factor
				_snake_case          =      do_pad



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}





	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   Tuple=False     ):


				'''simple docstring'''


				if not batched:
							_snake_case          =      image_inputs[0]
							if isinstance(lowercase      ,  Image.Image     ):
										_snake_case     , _snake_case          =      image.size
							else:
										_snake_case     , _snake_case          =      image.shape[1], image.shape[2]
							if w < h:
										_snake_case          =      int(self.size['shortest_edge'] * h / w     )
										_snake_case          =      self.size['shortest_edge']
							elif w > h:
										_snake_case          =      self.size['shortest_edge']
										_snake_case          =      int(self.size['shortest_edge'] * w / h     )
							else:
										_snake_case          =      self.size['shortest_edge']
										_snake_case          =      self.size['shortest_edge']

				else:
							_snake_case          =      []
							for image in image_inputs:
										_snake_case     , _snake_case          =      self.get_expected_values([image]     )
										expected_values.append((expected_height, expected_width)     )
							_snake_case          =      max(lowercase      ,  key=lambda lowercase     : item[0]     )[0]
							_snake_case          =      max(lowercase      ,  key=lambda lowercase     : item[1]     )[1]

				return expected_height, expected_width




@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = DeformableDetrImageProcessor if is_vision_available() else None



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      DeformableDetrImageProcessingTester(self     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(lowercase      ,  'image_mean'     )     )
				self.assertTrue(hasattr(lowercase      ,  'image_std'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_normalize'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_resize'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_rescale'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_pad'     )     )
				self.assertTrue(hasattr(lowercase      ,  'size'     )     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class.from_dict(self.image_processor_dict     )
				self.assertEqual(image_processor.size      ,  {'shortest_edge': 18, 'longest_edge': 1_333}     )
				self.assertEqual(image_processor.do_pad      ,  lowercase     )

				_snake_case          =      self.image_processing_class.from_dict(
				    self.image_processor_dict      ,  size=42      ,  max_size=84      ,  pad_and_return_pixel_mask=lowercase     )
				self.assertEqual(image_processor.size      ,  {'shortest_edge': 42, 'longest_edge': 84}     )
				self.assertEqual(image_processor.do_pad      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random PIL images
				_snake_case          =      prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase     )
				for image in image_inputs:
							self.assertIsInstance(lowercase      ,  Image.Image     )

				# Test not batched input
				_snake_case          =      image_processing(image_inputs[0]      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

				self.assertEqual(
				    encoded_images.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

				# Test batched
				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,  )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random numpy tensors
				_snake_case          =      prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase      ,  numpify=lowercase     )
				for image in image_inputs:
							self.assertIsInstance(lowercase      ,  np.ndarray     )

				# Test not batched input
				_snake_case          =      image_processing(image_inputs[0]      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

				self.assertEqual(
				    encoded_images.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				self.assertEqual(
				    encoded_images.shape      ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,  )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				# create random PyTorch tensors
				_snake_case          =      prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase      ,  torchify=lowercase     )
				for image in image_inputs:
							self.assertIsInstance(lowercase      ,  torch.Tensor     )

				# Test not batched input
				_snake_case          =      image_processing(image_inputs[0]      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

				self.assertEqual(
				    encoded_images.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				self.assertEqual(
				    encoded_images.shape      ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,  )



	@slow
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,  'r'     ) as f:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'image_id': 39_769, 'annotations': target}

				# encode them
				_snake_case          =      DeformableDetrImageProcessor()
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

				_snake_case          =      torch.tensor([0.2796, 0.3138, 0.3481]     )
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,  lowercase      ,  atol=1E-4     )     )

				# verify area
				_snake_case          =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([75, 75, 63, 65, 17, 17]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )





	@slow
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,  'r'     ) as f:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

				_snake_case          =      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

				# encode them
				_snake_case          =      DeformableDetrImageProcessor(format='coco_panoptic'     )
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  masks_path=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

				_snake_case          =      torch.tensor([0.2796, 0.3138, 0.3481]     )
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,  lowercase      ,  atol=1E-4     )     )

				# verify area
				_snake_case          =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([17, 17, 63, 75, 75, 93]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify masks
				_snake_case          =      822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,  lowercase     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )