
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowercase__							:							Optional[int]									=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def 	A_ (							snake_case							:   Dict )      ->    Optional[Any]:



						'''simple docstring'''

						if "token" in model_name_or_path:
												return "rag_token"
						if "sequence" in model_name_or_path:
												return "rag_sequence"
						if "bart" in model_name_or_path:
												return "bart"
						return None




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   List[str] ,      snake_case							:   Optional[int] )      ->    str:



						'''simple docstring'''

						return max(metric_fn(snake_case ,      snake_case ) for gt in ground_truths )




def 	A_ (							snake_case							:   Dict ,      snake_case							:   str ,      snake_case							:   str )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]
						__UpperCamelCase       =			[]

						if args.gold_data_mode == "qa":
												__UpperCamelCase       =			pd.read_csv(snake_case ,      sep='''\t''' ,      header=snake_case )
												for answer_list in data[1]:
																		__UpperCamelCase       =			ast.literal_eval(snake_case )
																		answers.append(snake_case )
						else:
												__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]
												__UpperCamelCase       =			[[reference] for reference in references]

						__UpperCamelCase       =			__UpperCamelCase       =			__UpperCamelCase       =			0
						for prediction, ground_truths in zip(snake_case ,      snake_case ):
												total += 1
												em += metric_max_over_ground_truths(snake_case ,      snake_case ,      snake_case )
												fa += metric_max_over_ground_truths(snake_case ,      snake_case ,      snake_case )

						__UpperCamelCase       =			100.0 * em / total
						__UpperCamelCase       =			100.0 * fa / total

						logger.info(f"F1: {fa:.2f}" )
						logger.info(f"EM: {em:.2f}" )




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   Any ,      snake_case							:   Dict )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			args.k
						__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]
						__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]

						__UpperCamelCase       =			__UpperCamelCase       =			0
						for hypo, reference in zip(snake_case ,      snake_case ):
												__UpperCamelCase       =			set(hypo.split('''\t''' )[:k] )
												__UpperCamelCase       =			set(reference.split('''\t''' ) )
												total += 1
												em += len(hypo_provenance & ref_provenance ) / k

						__UpperCamelCase       =			100.0 * em / total
						logger.info(f"Precision@{k}: {em: .2f}" )




def 	A_ (							snake_case							:   int ,      snake_case							:   List[Any] ,      snake_case							:   Optional[Any] )      ->    Tuple:



						'''simple docstring'''

						def strip_title(snake_case							:   Union[str, Any] ):
												if title.startswith('''"''' ):
																		__UpperCamelCase       =			title[1:]
												if title.endswith('''"''' ):
																		__UpperCamelCase       =			title[:-1]
												return title

						__UpperCamelCase       =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    snake_case ,      return_tensors='''pt''' ,      padding=snake_case ,      truncation=snake_case ,      )['''input_ids'''].to(args.device )

						__UpperCamelCase       =			rag_model.rag.question_encoder(snake_case )
						__UpperCamelCase       =			question_enc_outputs[0]

						__UpperCamelCase       =			rag_model.retriever(
						    snake_case ,      question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() ,      prefix=rag_model.rag.generator.config.prefix ,      n_docs=rag_model.config.n_docs ,      return_tensors='''pt''' ,      )
						__UpperCamelCase       =			rag_model.retriever.index.get_doc_dicts(result.doc_ids )
						__UpperCamelCase       =			[]
						for docs in all_docs:
												__UpperCamelCase       =			[strip_title(snake_case ) for title in docs['''title''']]
												provenance_strings.append('''\t'''.join(snake_case ) )
						return provenance_strings




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   List[Any] ,      snake_case							:   str )      ->    Tuple:



						'''simple docstring'''

						with torch.no_grad():
												__UpperCamelCase       =			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    snake_case ,      return_tensors='''pt''' ,      padding=snake_case ,      truncation=snake_case )

												__UpperCamelCase       =			inputs_dict.input_ids.to(args.device )
												__UpperCamelCase       =			inputs_dict.attention_mask.to(args.device )
												__UpperCamelCase       =			rag_model.generate(  # rag_model overwrites generate
												    snake_case ,      attention_mask=snake_case ,      num_beams=args.num_beams ,      min_length=args.min_length ,      max_length=args.max_length ,      early_stopping=snake_case ,      num_return_sequences=1 ,      bad_words_ids=[[0, 0]] ,      )
												__UpperCamelCase       =			rag_model.retriever.generator_tokenizer.batch_decode(snake_case ,      skip_special_tokens=snake_case )

												if args.print_predictions:
																		for q, a in zip(snake_case ,      snake_case ):
																								logger.info('''Q: {} - A: {}'''.format(snake_case ,      snake_case ) )

												return answers




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



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_type''' ,      choices=['''rag_sequence''', '''rag_token''', '''bart'''] ,      type=snake_case ,      help=(
						        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
						        ''' model_name_or_path'''
						    ) ,      )
						parser.add_argument(
						    '''--index_name''' ,      default=snake_case ,      choices=['''exact''', '''compressed''', '''legacy'''] ,      type=snake_case ,      help='''RAG model retriever type''' ,      )
						parser.add_argument(
						    '''--index_path''' ,      default=snake_case ,      type=snake_case ,      help='''Path to the retrieval index''' ,      )
						parser.add_argument('''--n_docs''' ,      default=5 ,      type=snake_case ,      help='''Number of retrieved docs''' )
						parser.add_argument(
						    '''--model_name_or_path''' ,      default=snake_case ,      type=snake_case ,      required=snake_case ,      help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' ,      )
						parser.add_argument(
						    '''--eval_mode''' ,      choices=['''e2e''', '''retrieval'''] ,      default='''e2e''' ,      type=snake_case ,      help=(
						        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
						        ''' precision@k.'''
						    ) ,      )
						parser.add_argument('''--k''' ,      default=1 ,      type=snake_case ,      help='''k for the precision@k calculation''' )
						parser.add_argument(
						    '''--evaluation_set''' ,      default=snake_case ,      type=snake_case ,      required=snake_case ,      help='''Path to a file containing evaluation samples''' ,      )
						parser.add_argument(
						    '''--gold_data_path''' ,      default=snake_case ,      type=snake_case ,      required=snake_case ,      help='''Path to a tab-separated file with gold samples''' ,      )
						parser.add_argument(
						    '''--gold_data_mode''' ,      default='''qa''' ,      type=snake_case ,      choices=['''qa''', '''ans'''] ,      help=(
						        '''Format of the gold data file'''
						        '''qa - a single line in the following format: question [tab] answer_list'''
						        '''ans - a single line of the gold file contains the expected answer string'''
						    ) ,      )
						parser.add_argument(
						    '''--predictions_path''' ,      type=snake_case ,      default='''predictions.txt''' ,      help='''Name of the predictions file, to be stored in the checkpoints directory''' ,      )
						parser.add_argument(
						    '''--eval_all_checkpoints''' ,      action='''store_true''' ,      help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' ,      )
						parser.add_argument(
						    '''--eval_batch_size''' ,      default=8 ,      type=snake_case ,      help='''Batch size per GPU/CPU for evaluation.''' ,      )
						parser.add_argument(
						    '''--recalculate''' ,      help='''Recalculate predictions even if the prediction file exists''' ,      action='''store_true''' ,      )
						parser.add_argument(
						    '''--num_beams''' ,      default=4 ,      type=snake_case ,      help='''Number of beams to be used when generating answers''' ,      )
						parser.add_argument('''--min_length''' ,      default=1 ,      type=snake_case ,      help='''Min length of the generated answers''' )
						parser.add_argument('''--max_length''' ,      default=50 ,      type=snake_case ,      help='''Max length of the generated answers''' )

						parser.add_argument(
						    '''--print_predictions''' ,      action='''store_true''' ,      help='''If True, prints predictions while evaluating.''' ,      )
						parser.add_argument(
						    '''--print_docs''' ,      action='''store_true''' ,      help='''If True, prints docs retried while generating.''' ,      )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
						return args




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



						'''simple docstring'''

						__UpperCamelCase       =			{}
						if args.model_type is None:
												__UpperCamelCase       =			infer_model_type(args.model_name_or_path )
												assert args.model_type is not None
						if args.model_type.startswith('''rag''' ):
												__UpperCamelCase       =			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												__UpperCamelCase       =			args.n_docs
												if args.index_name is not None:
																		__UpperCamelCase       =			args.index_name
												if args.index_path is not None:
																		__UpperCamelCase       =			args.index_path
						else:
												__UpperCamelCase       =			BartForConditionalGeneration

						__UpperCamelCase       =			(
						    [f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
						    if args.eval_all_checkpoints
						    else [args.model_name_or_path]
						)

						logger.info('''Evaluate the following checkpoints: %s''' ,      snake_case )

						__UpperCamelCase       =			get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						__UpperCamelCase       =			evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

						for checkpoint in checkpoints:
												if os.path.exists(args.predictions_path ) and (not args.recalculate):
																		logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path ) )
																		score_fn(snake_case ,      args.predictions_path ,      args.gold_data_path )
																		continue

												logger.info('''***** Running evaluation for {} *****'''.format(snake_case ) )
												logger.info('''  Batch size = %d''' ,      args.eval_batch_size )
												logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path ) )

												if args.model_type.startswith('''rag''' ):
																		__UpperCamelCase       =			RagRetriever.from_pretrained(snake_case ,      **snake_case )
																		__UpperCamelCase       =			model_class.from_pretrained(snake_case ,      retriever=snake_case ,      **snake_case )
																		model.retriever.init_retrieval()
												else:
																		__UpperCamelCase       =			model_class.from_pretrained(snake_case ,      **snake_case )
												model.to(args.device )

												with open(args.evaluation_set ,      '''r''' ) as eval_file, open(args.predictions_path ,      '''w''' ) as preds_file:
																		__UpperCamelCase       =			[]
																		for line in tqdm(snake_case ):
																								questions.append(line.strip() )
																								if len(snake_case ) == args.eval_batch_size:
																														__UpperCamelCase       =			evaluate_batch_fn(snake_case ,      snake_case ,      snake_case )
																														preds_file.write('''\n'''.join(snake_case ) + '''\n''' )
																														preds_file.flush()
																														__UpperCamelCase       =			[]
																		if len(snake_case ) > 0:
																								__UpperCamelCase       =			evaluate_batch_fn(snake_case ,      snake_case ,      snake_case )
																								preds_file.write('''\n'''.join(snake_case ) )
																								preds_file.flush()

																		score_fn(snake_case ,      args.predictions_path ,      args.gold_data_path )


if __name__ == "__main__":
				lowercase__							:							List[str]									=			get_args()
				main(args)






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

				if is_vision_available():
								from transformers import MaskaFormerImageProcessor

if is_vision_available():
				from PIL import Image






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=32 * 8     ,	SCREAMING_SNAKE_CASE_=32 * 8     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=64     ,	)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_auxiliary_loss
													__UpperCamelCase       =			num_queries
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			min_size
													__UpperCamelCase       =			max_size
													__UpperCamelCase       =			num_labels
													__UpperCamelCase       =			hidden_dim
													__UpperCamelCase       =			hidden_dim



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
													    SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.ones([self.batch_size, self.min_size, self.max_size]     ,	device=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			(
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,	device=SCREAMING_SNAKE_CASE_			) > 0.5
													).float()
													__UpperCamelCase       =			(torch.rand((self.batch_size, self.num_labels)     ,	device=SCREAMING_SNAKE_CASE_			) > 0.5).long()

													__UpperCamelCase       =			self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerConfig(
													    hidden_size=self.hidden_dim     ,	)
													__UpperCamelCase       =			self.num_queries
													__UpperCamelCase       =			self.num_labels

													__UpperCamelCase       =			[1, 1, 1, 1]
													__UpperCamelCase       =			self.num_channels

													__UpperCamelCase       =			64
													__UpperCamelCase       =			128
													__UpperCamelCase       =			self.hidden_dim
													__UpperCamelCase       =			self.hidden_dim
													__UpperCamelCase       =			self.hidden_dim
													return config



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.prepare_config_and_inputs()
													__UpperCamelCase       =			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
													return config, inputs_dict



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			output.encoder_hidden_states
													__UpperCamelCase       =			output.pixel_decoder_hidden_states
													__UpperCamelCase       =			output.transformer_decoder_hidden_states

													self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			)     ,	len(config.backbone_config.depths			)			)
													self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			)     ,	len(config.backbone_config.depths			)			)
													self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			)     ,	config.decoder_layers			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False			)->   Tuple:





													'''simple docstring'''






													with torch.no_grad():
																			__UpperCamelCase       =			MaskaFormerModel(config=SCREAMING_SNAKE_CASE_			)
																			model.to(SCREAMING_SNAKE_CASE_			)
																			model.eval()

																			__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)

													self.parent.assertEqual(
													    output.transformer_decoder_last_hidden_state.shape     ,	(self.batch_size, self.num_queries, self.hidden_dim)     ,	)
													# let's ensure the other two hidden state exists
													self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
													self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

													if output_hidden_states:
																			self.check_output_hidden_state(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()

													def comm_check_on_output(SCREAMING_SNAKE_CASE_			):
																			# let's still check that all the required stuff is there
																			self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
																			self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
																			self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
																			# okay, now we need to check the logits shape
																			# due to the encoder compression, masks have a //4 spatial size
																			self.parent.assertEqual(
																			    result.masks_queries_logits.shape     ,	(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,	)
																			# + 1 for null class
																			self.parent.assertEqual(
																			    result.class_queries_logits.shape     ,	(self.batch_size, self.num_queries, self.num_labels + 1)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)

																			comm_check_on_output(SCREAMING_SNAKE_CASE_			)

																			__UpperCamelCase       =			model(
																			    pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			)

													comm_check_on_output(SCREAMING_SNAKE_CASE_			)

													self.parent.assertTrue(result.loss is not None			)
													self.parent.assertEqual(result.loss.shape     ,	torch.Size([1]			)			)






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_snake_case     		=  {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_			)



							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''			)
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former is not a generative model'''			)
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former does not use token embeddings'''			)
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													pass



							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''			)
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													pass



							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def     A__		(  self			)->   str:





													'''simple docstring'''






													pass



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			inspect.signature(model.forward			)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase       =			[*signature.parameters.keys()]

																			__UpperCamelCase       =			['''pixel_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																			__UpperCamelCase       =			MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			(self.model_tester.min_size,) * 2
													__UpperCamelCase       =			{
													    '''pixel_values''': torch.randn((2, 3, *size)     ,	device=SCREAMING_SNAKE_CASE_			),
													    '''mask_labels''': torch.randn((2, 10, *size)     ,	device=SCREAMING_SNAKE_CASE_			),
													    '''class_labels''': torch.zeros(2     ,	10     ,	device=SCREAMING_SNAKE_CASE_			).long(),
													}
													__UpperCamelCase       =			self.model_tester.get_config()

													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													self.assertTrue(outputs.loss is not None			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_     ,	output_attentions=SCREAMING_SNAKE_CASE_			)
																			self.assertTrue(outputs.attentions is not None			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													if not self.model_tester.is_training:
																			return

													__UpperCamelCase       =			self.all_model_classes[1]
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()

													__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.train()

													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			).loss
													loss.backward()



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.all_model_classes[1]
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													__UpperCamelCase       =			True
													__UpperCamelCase       =			True

													__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													model.train()

													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.pixel_decoder_hidden_states[0]
													pixel_decoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_			)

													self.assertIsNotNone(encoder_hidden_states.grad			)
													self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
													self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
													self.assertIsNotNone(attentions.grad			)





lowercase__							:							Any									=			1e-4




def 	A_ (							)      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
						return image






@require_vision
@slow
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													return "facebook/mask2former-swin-small-coco-instance"



							@cached_property
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''pixel_values'''].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
													# check size
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	(1, 3, 384, 384)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.tensor(
													    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)

													__UpperCamelCase       =			torch.tensor(
													    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)

													__UpperCamelCase       =			torch.tensor(
													    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''pixel_values'''].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
													# check size
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	(1, 3, 384, 384)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													# masks_queries_logits
													__UpperCamelCase       =			outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape     ,	(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
													__UpperCamelCase       =			[
													    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
													    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
													    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
													]
													__UpperCamelCase       =			torch.tensor(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)
													# class_queries_logits
													__UpperCamelCase       =			outputs.class_queries_logits
													self.assertEqual(class_queries_logits.shape     ,	(1, model.config.num_queries, model.config.num_labels + 1)			)
													__UpperCamelCase       =			torch.tensor(
													    [
													        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
													        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
													        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
													    ]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
													__UpperCamelCase       =			self.default_image_processor

													__UpperCamelCase       =			image_processor(
													    [np.zeros((3, 800, 1333)			), np.zeros((3, 800, 1333)			)]     ,	segmentation_maps=[np.zeros((384, 384)			).astype(np.floataa			), np.zeros((384, 384)			).astype(np.floataa			)]     ,	return_tensors='''pt'''     ,	)

													__UpperCamelCase       =			inputs['''pixel_values'''].to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			[el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['''mask_labels''']]
													__UpperCamelCase       =			[el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['''class_labels''']]

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													self.assertTrue(outputs.loss is not None			)




