


'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__snake_case							      =     logging.getLogger(__name__)
@dataclass
class    lowercase	:

		"""simple docstring"""

		_a     =				field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
		_a     =				field(
		    default=A__	,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
		_a     =				field(
		    default=A__	,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
		_a     =				field(
		    default=A__	,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,		)
		_a     =				field(default=A__	,		metadata={'help': 'Whether tp freeze the encoder.'}					)
		_a     =				field(default=A__	,		metadata={'help': 'Whether  to freeze the embeddings.'}					)



@dataclass
class    lowercase	:

		"""simple docstring"""

		_a     =				field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
		_a     =				field(
		    default='summarization'	,		metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}	,		)
		_a     =				field(
		    default=10_24	,		metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,		)
		_a     =				field(
		    default=1_28	,		metadata={
		        'help': (
		            'The maximum total sequence length for target text after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,		)
		_a     =				field(
		    default=1_42	,		metadata={
		        'help': (
		            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded. '
		            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
		            'during ``evaluate`` and ``predict``.'
		        )
		    }	,		)
		_a     =				field(
		    default=1_42	,		metadata={
		        'help': (
		            'The maximum total sequence length for test target text after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,		)
		_a     =				field(default=-1	,		metadata={'help': '# training examples. -1 means use all.'}					)
		_a     =				field(default=-1	,		metadata={'help': '# validation examples. -1 means use all.'}					)
		_a     =				field(default=-1	,		metadata={'help': '# test examples. -1 means use all.'}					)
		_a     =				field(default=A__	,		metadata={'help': 'Source language id for translation.'}					)
		_a     =				field(default=A__	,		metadata={'help': 'Target language id for translation.'}					)
		_a     =				field(default=A__	,		metadata={'help': '# num_beams to use for evaluation.'}					)
		_a     =				field(
		    default=A__	,		metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}	,		)



def 			a      (			__a ,					__a ,					__a   )  ->	Optional[Any]:



					'''simple docstring'''






					logger.info(f'''***** {split} metrics *****'''   )
					for key in sorted(metrics.keys()   ):
										logger.info(f'''  {key} = {metrics[key]}'''   )
					save_json(__a ,					os.path.join(__a ,					f'''{split}_results.json'''   )   )


def 			a      (			)  ->	Optional[int]:



					'''simple docstring'''






					UpperCamelCase__   :Optional[Any]												= HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

					if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :int												= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
					else:
										UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= parser.parse_args_into_dataclasses()

					check_output_dir(__a   )

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,					datefmt='''%m/%d/%Y %H:%M:%S''' ,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,					)
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,					training_args.local_rank ,					training_args.device ,					training_args.n_gpu ,					bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   ) ,					training_args.fpaa ,					)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank   ):
										transformers.utils.logging.set_verbosity_info()
					logger.info('''Training/evaluation parameters %s''' ,					__a   )

					# Set seed
					set_seed(training_args.seed   )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					UpperCamelCase__   :int												= AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,					cache_dir=model_args.cache_dir ,					)

					UpperCamelCase__   :List[str]												= ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
					for p in extra_model_params:
										if getattr(__a ,					__a ,					__a   ):
															assert hasattr(__a ,					__a   ), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
															setattr(__a ,					__a ,					getattr(__a ,					__a   )   )

					UpperCamelCase__   :Optional[Any]												= AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,					cache_dir=model_args.cache_dir ,					)
					UpperCamelCase__   :int												= AutoModelForSeqaSeqLM.from_pretrained(
					    model_args.model_name_or_path ,					from_tf='''.ckpt''' in model_args.model_name_or_path ,					config=__a ,					cache_dir=model_args.cache_dir ,					)

					# use task specific params
					use_task_specific_params(__a ,					data_args.task   )

					# set num_beams for evaluation
					if data_args.eval_beams is None:
										UpperCamelCase__   :Any												= model.config.num_beams

					# set decoder_start_token_id for MBart
					if model.config.decoder_start_token_id is None and isinstance(__a ,					(MBartTokenizer, MBartTokenizerFast)   ):
										assert (
										    data_args.tgt_lang is not None and data_args.src_lang is not None
										), "mBart requires --tgt_lang and --src_lang"
										if isinstance(__a ,					__a   ):
															UpperCamelCase__   :Union[str, Any]												= tokenizer.lang_code_to_id[data_args.tgt_lang]
										else:
															UpperCamelCase__   :List[str]												= tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

					if model_args.freeze_embeds:
										freeze_embeds(__a   )
					if model_args.freeze_encoder:
										freeze_params(model.get_encoder()   )
										assert_all_frozen(model.get_encoder()   )

					UpperCamelCase__   :str												= SeqaSeqDataset

					# Get datasets
					UpperCamelCase__   :List[Any]												= (
					    dataset_class(
					        __a ,					type_path='''train''' ,					data_dir=data_args.data_dir ,					n_obs=data_args.n_train ,					max_target_length=data_args.max_target_length ,					max_source_length=data_args.max_source_length ,					prefix=model.config.prefix or '''''' ,					)
					    if training_args.do_train
					    else None
					)
					UpperCamelCase__   :List[str]												= (
					    dataset_class(
					        __a ,					type_path='''val''' ,					data_dir=data_args.data_dir ,					n_obs=data_args.n_val ,					max_target_length=data_args.val_max_target_length ,					max_source_length=data_args.max_source_length ,					prefix=model.config.prefix or '''''' ,					)
					    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
					    else None
					)
					UpperCamelCase__   :Optional[int]												= (
					    dataset_class(
					        __a ,					type_path='''test''' ,					data_dir=data_args.data_dir ,					n_obs=data_args.n_test ,					max_target_length=data_args.test_max_target_length ,					max_source_length=data_args.max_source_length ,					prefix=model.config.prefix or '''''' ,					)
					    if training_args.do_predict
					    else None
					)

					# Initialize our Trainer
					UpperCamelCase__   :List[str]												= (
					    build_compute_metrics_fn(data_args.task ,					__a   ) if training_args.predict_with_generate else None
					)
					UpperCamelCase__   :Optional[int]												= SeqaSeqTrainer(
					    model=__a ,					args=__a ,					data_args=__a ,					train_dataset=__a ,					eval_dataset=__a ,					data_collator=SeqaSeqDataCollator(
					        __a ,					__a ,					model.config.decoder_start_token_id ,					training_args.tpu_num_cores   ) ,					compute_metrics=__a ,					tokenizer=__a ,					)

					UpperCamelCase__   :Any												= {}
					# Training
					if training_args.do_train:
										logger.info('''*** Train ***'''   )

										UpperCamelCase__   :List[str]												= trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
										UpperCamelCase__   :Any												= train_result.metrics
										UpperCamelCase__   :str												= data_args.n_train

										trainer.save_model()  # this also saves the tokenizer

										if trainer.is_world_process_zero():
															handle_metrics('''train''' ,					__a ,					training_args.output_dir   )
															all_metrics.update(__a   )

															# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
															trainer.state.save_to_json(os.path.join(training_args.output_dir ,					'''trainer_state.json'''   )   )

															# For convenience, we also re-save the tokenizer to the same directory,
															# so that you can share your model easily on huggingface.co/models =)
															tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''   )

										UpperCamelCase__   :Optional[int]												= trainer.evaluate(metric_key_prefix='''val'''   )
										UpperCamelCase__   :Union[str, Any]												= data_args.n_val
										UpperCamelCase__   :List[Any]												= round(metrics['''val_loss'''] ,					4   )

										if trainer.is_world_process_zero():
															handle_metrics('''val''' ,					__a ,					training_args.output_dir   )
															all_metrics.update(__a   )

					if training_args.do_predict:
										logger.info('''*** Predict ***'''   )

										UpperCamelCase__   :Any												= trainer.predict(test_dataset=__a ,					metric_key_prefix='''test'''   )
										UpperCamelCase__   :Optional[int]												= test_output.metrics
										UpperCamelCase__   :Union[str, Any]												= data_args.n_test

										if trainer.is_world_process_zero():
															UpperCamelCase__   :str												= round(metrics['''test_loss'''] ,					4   )
															handle_metrics('''test''' ,					__a ,					training_args.output_dir   )
															all_metrics.update(__a   )

															if training_args.predict_with_generate:
																				UpperCamelCase__   :Dict												= tokenizer.batch_decode(
																				    test_output.predictions ,					skip_special_tokens=__a ,					clean_up_tokenization_spaces=__a   )
																				UpperCamelCase__   :List[str]												= lmap(str.strip ,					__a   )
																				write_txt_file(__a ,					os.path.join(training_args.output_dir ,					'''test_generations.txt'''   )   )

					if trainer.is_world_process_zero():
										save_json(__a ,					os.path.join(training_args.output_dir ,					'''all_results.json'''   )   )

					return all_metrics


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					main()


if __name__ == "__main__":
		main()



'''simple docstring'''
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 ConditionalDetrImageProcessor
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=True    ,   UpperCamelCase_=1 / 255    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							UpperCamelCase__   :str												= parent
							UpperCamelCase__   :List[Any]												= batch_size
							UpperCamelCase__   :Dict												= num_channels
							UpperCamelCase__   :str												= min_resolution
							UpperCamelCase__   :Optional[Any]												= max_resolution
							UpperCamelCase__   :int												= do_resize
							UpperCamelCase__   :Optional[Any]												= size
							UpperCamelCase__   :Tuple												= do_normalize
							UpperCamelCase__   :List[Any]												= image_mean
							UpperCamelCase__   :Dict												= image_std
							UpperCamelCase__   :Union[str, Any]												= do_rescale
							UpperCamelCase__   :Union[str, Any]												= rescale_factor
							UpperCamelCase__   :Union[str, Any]												= do_pad




		def     lowerCAmelCase__				(		self							):

							'''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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							if not batched:
												UpperCamelCase__   :List[str]												= image_inputs[0]
												if isinstance(UpperCamelCase_    ,   Image.Image							):
																	UpperCamelCase__					,				UpperCamelCase__   :List[str]												= image.size
												else:
																	UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= image.shape[1], image.shape[2]
												if w < h:
																	UpperCamelCase__   :int												= int(self.size['''shortest_edge'''] * h / w							)
																	UpperCamelCase__   :Dict												= self.size['''shortest_edge''']
												elif w > h:
																	UpperCamelCase__   :int												= self.size['''shortest_edge''']
																	UpperCamelCase__   :Tuple												= int(self.size['''shortest_edge'''] * w / h							)
												else:
																	UpperCamelCase__   :str												= self.size['''shortest_edge''']
																	UpperCamelCase__   :str												= self.size['''shortest_edge''']

							else:
												UpperCamelCase__   :Any												= []
												for image in image_inputs:
																	UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.get_expected_values([image]							)
																	expected_values.append((expected_height, expected_width)							)
												UpperCamelCase__   :List[Any]												= max(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: item[0]							)[0]
												UpperCamelCase__   :Optional[int]												= max(UpperCamelCase_    ,   key=lambda UpperCamelCase_							: item[1]							)[1]

							return expected_height, expected_width



@require_torch
@require_vision
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				ConditionalDetrImageProcessor if is_vision_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''image_mean'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''image_std'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''do_normalize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''do_resize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''size'''							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 18, '''longest_edge''': 1333}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= self.image_processing_class.from_dict(
							    self.image_processor_dict    ,   size=42    ,   max_size=84    ,   pad_and_return_pixel_mask=UpperCamelCase_							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 42, '''longest_edge''': 84}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :List[str]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   Image.Image							)

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

							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__   :Any												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_    ,   numpify=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   np.ndarray							)

							# Test not batched input
							UpperCamelCase__   :Union[str, Any]												= image_processing(image_inputs[0]    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__   :Union[str, Any]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__   :Any												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_    ,   torchify=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   torch.Tensor							)

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

							UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

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




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Optional[int]												= json.loads(f.read()							)

							UpperCamelCase__   :Any												= {'''image_id''': 39769, '''annotations''': target}

							# encode them
							UpperCamelCase__   :str												= ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''							)
							UpperCamelCase__   :List[Any]												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :List[str]												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :str												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :str												= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Optional[Any]												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[Any]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :int												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :List[str]												= torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify orig_size
							UpperCamelCase__   :Tuple												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Tuple												= json.loads(f.read()							)

							UpperCamelCase__   :List[str]												= {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

							UpperCamelCase__   :Any												= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

							# encode them
							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessor(format='''coco_panoptic'''							)
							UpperCamelCase__   :Dict												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   masks_path=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :str												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :Tuple												= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Any												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[str]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :str												= torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify masks
							UpperCamelCase__   :Optional[Any]												= 822873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,   UpperCamelCase_							)
							# verify orig_size
							UpperCamelCase__   :List[str]												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :List[Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)