
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase__							:							List[Any]									=			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 	A_ (							snake_case							:   Any ,      snake_case							:   int ,      snake_case							:   Dict ,      snake_case							:   Tuple ,      snake_case							:   Dict )      ->    Any:



						'''simple docstring'''

						for attribute in key.split('''.''' ):
												__UpperCamelCase       =			getattr(snake_case ,      snake_case )

						if weight_type is not None:
												__UpperCamelCase       =			getattr(snake_case ,      snake_case ).shape
						else:
												__UpperCamelCase       =			hf_pointer.shape

						assert hf_shape == value.shape, (
						    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
						    f" {value.shape} for {full_name}"
						)

						if weight_type == "weight":
												__UpperCamelCase       =			value
						elif weight_type == "weight_g":
												__UpperCamelCase       =			value
						elif weight_type == "weight_v":
												__UpperCamelCase       =			value
						elif weight_type == "bias":
												__UpperCamelCase       =			value
						else:
												__UpperCamelCase       =			value

						logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )




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



						'''simple docstring'''

						__UpperCamelCase       =			[]
						__UpperCamelCase       =			fairseq_model.state_dict()

						__UpperCamelCase       =			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												__UpperCamelCase       =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case ,      snake_case ,      snake_case ,      snake_case ,      hf_model.config.feat_extract_norm == '''group''' ,      )
																		__UpperCamelCase       =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								__UpperCamelCase       =			'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																								if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
																														__UpperCamelCase       =			True
																														if "*" in mapped_key:
																																				__UpperCamelCase       =			name.split(snake_case )[0].split('''.''' )[-2]
																																				__UpperCamelCase       =			mapped_key.replace('''*''' ,      snake_case )
																														if "weight_g" in name:
																																				__UpperCamelCase       =			'''weight_g'''
																														elif "weight_v" in name:
																																				__UpperCamelCase       =			'''weight_v'''
																														elif "weight" in name:
																																				__UpperCamelCase       =			'''weight'''
																														elif "bias" in name:
																																				__UpperCamelCase       =			'''bias'''
																														else:
																																				__UpperCamelCase       =			None
																														set_recursively(snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )
																								continue
												if not is_used:
																		unused_weights.append(snake_case )

						logger.warning(f"Unused weights: {unused_weights}" )




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



						'''simple docstring'''

						__UpperCamelCase       =			full_name.split('''conv_layers.''' )[-1]
						__UpperCamelCase       =			name.split('''.''' )
						__UpperCamelCase       =			int(items[0] )
						__UpperCamelCase       =			int(items[1] )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    f"{full_name} has size {value.shape}, but"
																		    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
																		)
																		__UpperCamelCase       =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    f"{full_name} has size {value.shape}, but"
																		    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
																		)
																		__UpperCamelCase       =			value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
																		    " found."
																		)
																		__UpperCamelCase       =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    f"{full_name} has size {value.shape}, but"
																		    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
																		)
																		__UpperCamelCase       =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
						else:
												unused_weights.append(snake_case )




def 	A_ (							snake_case							:   Dict ,      snake_case							:   int )      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			SEWConfig()
						if is_finetuned:
												__UpperCamelCase       =			model.wav_encoder.wav_model.cfg
						else:
												__UpperCamelCase       =			model.cfg

						__UpperCamelCase       =			fs_config.conv_bias
						__UpperCamelCase       =			eval(fs_config.conv_feature_layers )
						__UpperCamelCase       =			[x[0] for x in conv_layers]
						__UpperCamelCase       =			[x[1] for x in conv_layers]
						__UpperCamelCase       =			[x[2] for x in conv_layers]
						__UpperCamelCase       =			'''gelu'''
						__UpperCamelCase       =			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						__UpperCamelCase       =			0.0
						__UpperCamelCase       =			fs_config.activation_fn.name
						__UpperCamelCase       =			fs_config.encoder_embed_dim
						__UpperCamelCase       =			0.02
						__UpperCamelCase       =			fs_config.encoder_ffn_embed_dim
						__UpperCamelCase       =			1e-5
						__UpperCamelCase       =			fs_config.encoder_layerdrop
						__UpperCamelCase       =			fs_config.encoder_attention_heads
						__UpperCamelCase       =			fs_config.conv_pos_groups
						__UpperCamelCase       =			fs_config.conv_pos
						__UpperCamelCase       =			len(snake_case )
						__UpperCamelCase       =			fs_config.encoder_layers
						__UpperCamelCase       =			fs_config.squeeze_factor

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												__UpperCamelCase       =			model.cfg
												__UpperCamelCase       =			fs_config.final_dropout
												__UpperCamelCase       =			fs_config.layerdrop
						__UpperCamelCase       =			fs_config.activation_dropout
						__UpperCamelCase       =			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						__UpperCamelCase       =			fs_config.attention_dropout
						__UpperCamelCase       =			fs_config.dropout_input
						__UpperCamelCase       =			fs_config.dropout
						__UpperCamelCase       =			fs_config.mask_channel_length
						__UpperCamelCase       =			fs_config.mask_channel_prob
						__UpperCamelCase       =			fs_config.mask_length
						__UpperCamelCase       =			fs_config.mask_prob

						__UpperCamelCase       =			'''Wav2Vec2FeatureExtractor'''
						__UpperCamelCase       =			'''Wav2Vec2CTCTokenizer'''

						return config




@torch.no_grad()
def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   List[Any] ,      snake_case							:   List[str]=None ,      snake_case							:   Tuple=None ,      snake_case							:   Optional[int]=True )      ->    str:



						'''simple docstring'''

						if is_finetuned:
												__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
						else:
												__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

						if config_path is not None:
												__UpperCamelCase       =			SEWConfig.from_pretrained(snake_case )
						else:
												__UpperCamelCase       =			convert_config(model[0] ,      snake_case )
						__UpperCamelCase       =			model[0].eval()

						__UpperCamelCase       =			True if config.feat_extract_norm == '''layer''' else False
						__UpperCamelCase       =			WavaVecaFeatureExtractor(
						    feature_size=1 ,      sampling_rate=16000 ,      padding_value=0 ,      do_normalize=snake_case ,      return_attention_mask=snake_case ,      )

						if is_finetuned:
												if dict_path:
																		__UpperCamelCase       =			Dictionary.load(snake_case )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__UpperCamelCase       =			target_dict.pad_index
																		__UpperCamelCase       =			target_dict.bos_index
																		__UpperCamelCase       =			target_dict.pad_index
																		__UpperCamelCase       =			target_dict.bos_index
																		__UpperCamelCase       =			target_dict.eos_index
																		__UpperCamelCase       =			len(target_dict.symbols )
																		__UpperCamelCase       =			os.path.join(snake_case ,      '''vocab.json''' )
																		if not os.path.isdir(snake_case ):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case ) )
																								return
																		os.makedirs(snake_case ,      exist_ok=snake_case )
																		with open(snake_case ,      '''w''' ,      encoding='''utf-8''' ) as vocab_handle:
																								json.dump(target_dict.indices ,      snake_case )
																		__UpperCamelCase       =			WavaVecaCTCTokenizer(
																		    snake_case ,      unk_token=target_dict.unk_word ,      pad_token=target_dict.pad_word ,      bos_token=target_dict.bos_word ,      eos_token=target_dict.eos_word ,      word_delimiter_token='''|''' ,      do_lower_case=snake_case ,      )
																		__UpperCamelCase       =			WavaVecaProcessor(feature_extractor=snake_case ,      tokenizer=snake_case )
																		processor.save_pretrained(snake_case )

												__UpperCamelCase       =			SEWForCTC(snake_case )
						else:
												__UpperCamelCase       =			SEWModel(snake_case )
												feature_extractor.save_pretrained(snake_case )

						recursively_load_weights(snake_case ,      snake_case ,      snake_case )

						hf_model.save_pretrained(snake_case )


if __name__ == "__main__":
				lowercase__							:							List[str]									=			argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				lowercase__							:							str									=			parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)






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			)




