






'''simple docstring'''







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 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=2   ,_lowerCAmelCase=True   ,_lowerCAmelCase=False   ,_lowerCAmelCase=10   ,_lowerCAmelCase=3   ,_lowerCAmelCase=32 * 8   ,_lowerCAmelCase=32 * 8   ,_lowerCAmelCase=4   ,_lowerCAmelCase=64   ,):
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  is_training
								lowerCamelCase__         =  use_auxiliary_loss
								lowerCamelCase__         =  num_queries
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  min_size
								lowerCamelCase__         =  max_size
								lowerCamelCase__         =  num_labels
								lowerCamelCase__         =  hidden_dim
								lowerCamelCase__         =  hidden_dim






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
								    snake_case_       )

								lowerCamelCase__         =  torch.ones([self.batch_size, self.min_size, self.max_size]   ,device=snake_case_       )

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

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  MaskaFormerConfig(
								    hidden_size=self.hidden_dim   ,)
								lowerCamelCase__         =  self.num_queries
								lowerCamelCase__         =  self.num_labels

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

								lowerCamelCase__         =  64
								lowerCamelCase__         =  1_28
								lowerCamelCase__         =  self.hidden_dim
								lowerCamelCase__         =  self.hidden_dim
								lowerCamelCase__         =  self.hidden_dim
								return config






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  self.prepare_config_and_inputs()
								lowerCamelCase__         =  {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
								return config, inputs_dict






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  output.encoder_hidden_states
								lowerCamelCase__         =  output.pixel_decoder_hidden_states
								lowerCamelCase__         =  output.transformer_decoder_hidden_states

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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase=False       ):
								with torch.no_grad():
														lowerCamelCase__         =  MaskaFormerModel(config=snake_case_       )
														model.to(snake_case_       )
														model.eval()

														lowerCamelCase__         =  model(pixel_values=snake_case_   ,pixel_mask=snake_case_       )
														lowerCamelCase__         =  model(snake_case_   ,output_hidden_states=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(snake_case_   ,snake_case_       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  MaskaFormerForUniversalSegmentation(config=snake_case_       )
								model.to(snake_case_       )
								model.eval()

								def comm_check_on_output(_lowerCAmelCase       ):
														# 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():
														lowerCamelCase__         =  model(pixel_values=snake_case_   ,pixel_mask=snake_case_       )
														lowerCamelCase__         =  model(snake_case_       )

														comm_check_on_output(snake_case_       )

														lowerCamelCase__         =  model(
														    pixel_values=snake_case_   ,pixel_mask=snake_case_   ,mask_labels=snake_case_   ,class_labels=snake_case_       )

								comm_check_on_output(snake_case_       )

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




@require_torch
class 		UpperCamelCase__    (_a   ,_a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		_UpperCamelCase				  =					{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  MaskaFormerModelTester(self       )
								lowerCamelCase__         =  ConfigTester(self   ,config_class=snake_case_   ,has_text_modality=snake_case_       )






		def 			UpperCamelCase_					(      self       ):
								self.config_tester.run_common_tests()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(snake_case_   ,**snake_case_   ,output_hidden_states=snake_case_       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case_       )






		@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		@unittest.skip(reason="""Mask2Former is not a generative model"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		@unittest.skip(reason="""Mask2Former does not use token embeddings"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






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






		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCamelCase__         =  ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]   ,snake_case_       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														lowerCamelCase__         =  MaskaFormerModel.from_pretrained(snake_case_       )
														self.assertIsNotNone(snake_case_       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  (self.model_tester.min_size,) * 2
								lowerCamelCase__         =  {
								    """pixel_values""": torch.randn((2, 3, *size)   ,device=snake_case_       ),
								    """mask_labels""": torch.randn((2, 10, *size)   ,device=snake_case_       ),
								    """class_labels""": torch.zeros(2   ,10   ,device=snake_case_       ).long(),
								}
								lowerCamelCase__         =  self.model_tester.get_config()

								lowerCamelCase__         =  MaskaFormerForUniversalSegmentation(snake_case_       ).to(snake_case_       )
								lowerCamelCase__         =  model(**snake_case_       )
								self.assertTrue(outputs.loss is not None       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(snake_case_   ,**snake_case_   ,output_hidden_states=snake_case_       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()

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






		def 			UpperCamelCase_					(      self       ):
								if not self.model_tester.is_training:
														return

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

								lowerCamelCase__         =  model_class(snake_case_       )
								model.to(snake_case_       )
								model.train()

								lowerCamelCase__         =  model(snake_case_   ,mask_labels=snake_case_   ,class_labels=snake_case_       ).loss
								loss.backward()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.all_model_classes[1]
								lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								lowerCamelCase__         =  True
								lowerCamelCase__         =  True

								lowerCamelCase__         =  model_class(snake_case_       ).to(snake_case_       )
								model.train()

								lowerCamelCase__         =  model(snake_case_   ,mask_labels=snake_case_   ,class_labels=snake_case_       )

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

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

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

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

								outputs.loss.backward(retain_graph=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       )



UpperCamelCase    :      List[str]  			=		1E-4
def 						A__			(				):
						lowerCamelCase__         =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image





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




		'''simple docstring'''





		@cached_property
		def 			UpperCamelCase_					(      self       ):
								return "facebook/mask2former-swin-small-coco-instance"






		@cached_property
		def 			UpperCamelCase_					(      self       ):
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(snake_case_       )
								lowerCamelCase__         =  self.default_image_processor
								lowerCamelCase__         =  prepare_img()
								lowerCamelCase__         =  image_processor(snake_case_   ,return_tensors="""pt"""       ).to(snake_case_       )
								lowerCamelCase__         =  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(snake_case_   ,(1, 3, 3_84, 3_84)       )

								with torch.no_grad():
														lowerCamelCase__         =  model(**snake_case_       )

								lowerCamelCase__         =  torch.tensor(
								    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]       ).to(snake_case_       )
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,snake_case_   ,atol=snake_case_       )       )

								lowerCamelCase__         =  torch.tensor(
								    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]       ).to(snake_case_       )
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,snake_case_   ,atol=snake_case_       )       )

								lowerCamelCase__         =  torch.tensor(
								    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]       ).to(snake_case_       )
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,snake_case_   ,atol=snake_case_       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(snake_case_       ).eval()
								lowerCamelCase__         =  self.default_image_processor
								lowerCamelCase__         =  prepare_img()
								lowerCamelCase__         =  image_processor(snake_case_   ,return_tensors="""pt"""       ).to(snake_case_       )
								lowerCamelCase__         =  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(snake_case_   ,(1, 3, 3_84, 3_84)       )

								with torch.no_grad():
														lowerCamelCase__         =  model(**snake_case_       )
								# masks_queries_logits
								lowerCamelCase__         =  outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape   ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
								lowerCamelCase__         =  [
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								lowerCamelCase__         =  torch.tensor(snake_case_       ).to(snake_case_       )
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,snake_case_   ,atol=snake_case_       )       )
								# class_queries_logits
								lowerCamelCase__         =  outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape   ,(1, model.config.num_queries, model.config.num_labels + 1)       )
								lowerCamelCase__         =  torch.tensor(
								    [
								        [1.8324, -8.0835, -4.1922],
								        [0.8450, -9.0050, -3.6053],
								        [0.3045, -7.7293, -3.0275],
								    ]       ).to(snake_case_       )
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,snake_case_   ,atol=snake_case_       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(snake_case_       ).eval()
								lowerCamelCase__         =  self.default_image_processor

								lowerCamelCase__         =  image_processor(
								    [np.zeros((3, 8_00, 13_33)       ), np.zeros((3, 8_00, 13_33)       )]   ,segmentation_maps=[np.zeros((3_84, 3_84)       ).astype(np.floataa       ), np.zeros((3_84, 3_84)       ).astype(np.floataa       )]   ,return_tensors="""pt"""   ,)

								lowerCamelCase__         =  inputs["""pixel_values"""].to(snake_case_       )
								lowerCamelCase__         =  [el.to(snake_case_       ) for el in inputs["""mask_labels"""]]
								lowerCamelCase__         =  [el.to(snake_case_       ) for el in inputs["""class_labels"""]]

								with torch.no_grad():
														lowerCamelCase__         =  model(**snake_case_       )

								self.assertTrue(outputs.loss is not None       )









'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						lowerCamelCase__         =  [
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """encoder.embed_positions._float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Tuple						):
						lowerCamelCase__       ,			lowerCamelCase__         =  emb.weight.shape
						lowerCamelCase__         =  nn.Linear(__lowerCAmelCase   ,    __lowerCAmelCase   ,    bias=__lowerCAmelCase						)
						lowerCamelCase__         =  emb.weight.data
						return lin_layer
def 						A__			(				__lowerCAmelCase		:			Dict						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)
						lowerCamelCase__         =  mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						lowerCamelCase__         =  mam_aaa["""model"""]
						remove_ignore_keys_(__lowerCAmelCase						)
						lowerCamelCase__         =  state_dict["""encoder.embed_tokens.weight"""].shape[0]

						lowerCamelCase__         =  MaMaaaConfig(
						    vocab_size=__lowerCAmelCase   ,    max_position_embeddings=1024   ,    encoder_layers=args.encoder_layers   ,    decoder_layers=args.decoder_layers   ,    encoder_attention_heads=args.encoder_attention_heads   ,    decoder_attention_heads=args.decoder_attention_heads   ,    encoder_ffn_dim=args.encoder_ffn_embed_dim   ,    decoder_ffn_dim=args.decoder_ffn_embed_dim   ,    d_model=args.encoder_embed_dim   ,    encoder_layerdrop=args.encoder_layerdrop   ,    decoder_layerdrop=args.decoder_layerdrop   ,    dropout=args.dropout   ,    attention_dropout=args.attention_dropout   ,    activation_dropout=args.activation_dropout   ,    activation_function="""relu"""   ,    )

						lowerCamelCase__         =  state_dict["""decoder.embed_tokens.weight"""]
						lowerCamelCase__         =  MaMaaaForConditionalGeneration(__lowerCAmelCase						)
						model.model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase						)
						lowerCamelCase__         =  make_linear_from_emb(model.model.shared						)

						return model


if __name__ == "__main__":
							UpperCamelCase    :      Optional[Any]  			=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
							parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							UpperCamelCase    :      Tuple  			=		parser.parse_args()
							UpperCamelCase    :      List[Any]  			=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)

