
"""simple docstring"""





import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_MAPPING,
				    SegformerForImageClassification,
				    SegformerForSemanticSegmentation,
				    SegformerModel,
				)
				from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import SegformerImageProcessor






class    _UpperCAmelCase(   lowerCamelCase     ):



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






							'''simple docstring'''
							_UpperCamelCase										=  self.config_class(**self.inputs_dict)
							self.parent.assertTrue(hasattr(__a    ,		'''hidden_sizes'''))
							self.parent.assertTrue(hasattr(__a    ,		'''num_attention_heads'''))
							self.parent.assertTrue(hasattr(__a    ,		'''num_encoder_blocks'''))






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=64    ,		__a=3    ,		__a=4    ,		__a=[2, 2, 2, 2]    ,		__a=[8, 4, 2, 1]    ,		__a=[16, 32, 64, 1_28]    ,		__a=[1, 4, 8, 16]    ,		__a=[1, 2, 4, 8]    ,		__a=True    ,		__a=True    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=0.02    ,		__a=3    ,		__a=None    ,		)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  num_encoder_blocks
							_UpperCamelCase										=  sr_ratios
							_UpperCamelCase										=  depths
							_UpperCamelCase										=  hidden_sizes
							_UpperCamelCase										=  downsampling_rates
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  num_labels
							_UpperCamelCase										=  scope



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase										=  None
							if self.use_labels:
												_UpperCamelCase										=  ids_tensor([self.batch_size, self.image_size, self.image_size]    ,		self.num_labels)

							_UpperCamelCase										=  self.get_config()
							return config, pixel_values, labels



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






							'''simple docstring'''
							return SegformerConfig(
							    image_size=self.image_size    ,		num_channels=self.num_channels    ,		num_encoder_blocks=self.num_encoder_blocks    ,		depths=self.depths    ,		hidden_sizes=self.hidden_sizes    ,		num_attention_heads=self.num_attention_heads    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		initializer_range=self.initializer_range    ,		)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  SegformerModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a)
							_UpperCamelCase										=  _UpperCamelCase										=  self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,		(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  SegformerForSemanticSegmentation(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a)
							self.parent.assertEqual(
							    result.logits.shape    ,		(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
							_UpperCamelCase										=  model(__a    ,		labels=__a)
							self.parent.assertEqual(
							    result.logits.shape    ,		(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
							self.parent.assertGreater(result.loss    ,		0.0)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  1
							_UpperCamelCase										=  SegformerForSemanticSegmentation(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  torch.randint(0    ,		1    ,		(self.batch_size, self.image_size, self.image_size)).to(__a)
							_UpperCamelCase										=  model(__a    ,		labels=__a)
							self.parent.assertGreater(result.loss    ,		0.0)




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






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  config_and_inputs
							_UpperCamelCase										=  {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (
		        SegformerModel,
		        SegformerForSemanticSegmentation,
		        SegformerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__      =				(
		    {
		        'feature-extraction': SegformerModel,
		        'image-classification': SegformerForImageClassification,
		        'image-segmentation': SegformerForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowercase__      =				True
		lowercase__      =				False
		lowercase__      =				False
		lowercase__      =				False



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  SegformerModelTester(self)
							_UpperCamelCase										=  SegformerConfigTester(self    ,		config_class=__a)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							self.config_tester.run_common_tests()



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






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*__a)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*__a)



		@unittest.skip('''SegFormer does not use inputs_embeds''')
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							pass



		@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods''')
		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							( self)	->	List[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(__a)
												_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]    ,		__a)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase										=  True

							for model_class in self.all_model_classes:
												_UpperCamelCase										=  True
												_UpperCamelCase										=  False
												_UpperCamelCase										=  True
												_UpperCamelCase										=  model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase										=  model(**self._prepare_for_class(__a    ,		__a))
												_UpperCamelCase										=  outputs.attentions

												_UpperCamelCase										=  sum(self.model_tester.depths)
												self.assertEqual(len(__a)    ,		__a)

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												_UpperCamelCase										=  True
												_UpperCamelCase										=  model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase										=  model(**self._prepare_for_class(__a    ,		__a))
												_UpperCamelCase										=  outputs.attentions

												self.assertEqual(len(__a)    ,		__a)

												# verify the first attentions (first block, first layer)
												_UpperCamelCase										=  (self.model_tester.image_size // 4) ** 2
												_UpperCamelCase										=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
												self.assertListEqual(
												    list(attentions[0].shape[-3:])    ,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,		)

												# verify the last attentions (last block, last layer)
												_UpperCamelCase										=  (self.model_tester.image_size // 32) ** 2
												_UpperCamelCase										=  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
												self.assertListEqual(
												    list(attentions[-1].shape[-3:])    ,		[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]    ,		)
												_UpperCamelCase										=  len(__a)

												# Check attention is always last and order is fine
												_UpperCamelCase										=  True
												_UpperCamelCase										=  True
												_UpperCamelCase										=  model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase										=  model(**self._prepare_for_class(__a    ,		__a))

												self.assertEqual(out_len + 1    ,		len(__a))

												_UpperCamelCase										=  outputs.attentions

												self.assertEqual(len(__a)    ,		__a)
												# verify the first attentions (first block, first layer)
												_UpperCamelCase										=  (self.model_tester.image_size // 4) ** 2
												_UpperCamelCase										=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:])    ,		[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,		)



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






							'''simple docstring'''
							def check_hidden_states_output(__a    ,		__a    ,		__a):
												_UpperCamelCase										=  model_class(__a)
												model.to(__a)
												model.eval()

												with torch.no_grad():
																	_UpperCamelCase										=  model(**self._prepare_for_class(__a    ,		__a))

												_UpperCamelCase										=  outputs.hidden_states

												_UpperCamelCase										=  self.model_tester.num_encoder_blocks
												self.assertEqual(len(__a)    ,		__a)

												# verify the first hidden states (first block)
												self.assertListEqual(
												    list(hidden_states[0].shape[-3:])    ,		[
												        self.model_tester.hidden_sizes[0],
												        self.model_tester.image_size // 4,
												        self.model_tester.image_size // 4,
												    ]    ,		)

							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase										=  True
												check_hidden_states_output(__a    ,		__a    ,		__a)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_UpperCamelCase										=  True

												check_hidden_states_output(__a    ,		__a    ,		__a)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							if not self.model_tester.is_training:
												return

							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase										=  True

							for model_class in self.all_model_classes:
												if model_class in get_values(__a):
																	continue

												_UpperCamelCase										=  model_class(__a)
												model.to(__a)
												model.train()
												_UpperCamelCase										=  self._prepare_for_class(__a    ,		__a    ,		return_labels=__a)
												_UpperCamelCase										=  model(**__a).loss
												loss.backward()



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






							'''simple docstring'''
							pass




		@slow
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  SegformerModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def 				lowerCamelCase__		(      )							->		Any:


					"""simple docstring"""





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






@require_torch
class    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							# only resize + normalize
							_UpperCamelCase										=  SegformerImageProcessor(
							    image_scale=(5_12, 5_12)    ,		keep_ratio=__a    ,		align=__a    ,		do_random_crop=__a)
							_UpperCamelCase										=  SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''').to(
							    __a)

							_UpperCamelCase										=  prepare_img()
							_UpperCamelCase										=  image_processor(images=__a    ,		return_tensors='''pt''')
							_UpperCamelCase										=  encoded_inputs.pixel_values.to(__a)

							with torch.no_grad():
												_UpperCamelCase										=  model(__a)

							_UpperCamelCase										=  torch.Size((1, model.config.num_labels, 1_28, 1_28))
							self.assertEqual(outputs.logits.shape    ,		__a)

							_UpperCamelCase										=  torch.tensor(
							    [
							        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
							        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
							        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
							    ]).to(__a)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,		__a    ,		atol=1e-4))



		@slow
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							# only resize + normalize
							_UpperCamelCase										=  SegformerImageProcessor(
							    image_scale=(5_12, 5_12)    ,		keep_ratio=__a    ,		align=__a    ,		do_random_crop=__a)
							_UpperCamelCase										=  SegformerForSemanticSegmentation.from_pretrained(
							    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024''').to(__a)

							_UpperCamelCase										=  prepare_img()
							_UpperCamelCase										=  image_processor(images=__a    ,		return_tensors='''pt''')
							_UpperCamelCase										=  encoded_inputs.pixel_values.to(__a)

							with torch.no_grad():
												_UpperCamelCase										=  model(__a)

							_UpperCamelCase										=  torch.Size((1, model.config.num_labels, 1_28, 1_28))
							self.assertEqual(outputs.logits.shape    ,		__a)

							_UpperCamelCase										=  torch.tensor(
							    [
							        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
							        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
							        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
							    ]).to(__a)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,		__a    ,		atol=1e-1))




		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							# only resize + normalize
							_UpperCamelCase										=  SegformerImageProcessor(
							    image_scale=(5_12, 5_12)    ,		keep_ratio=__a    ,		align=__a    ,		do_random_crop=__a)
							_UpperCamelCase										=  SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''').to(
							    __a)

							_UpperCamelCase										=  prepare_img()
							_UpperCamelCase										=  image_processor(images=__a    ,		return_tensors='''pt''')
							_UpperCamelCase										=  encoded_inputs.pixel_values.to(__a)

							with torch.no_grad():
												_UpperCamelCase										=  model(__a)

							_UpperCamelCase										=  outputs.logits.detach().cpu()

							_UpperCamelCase										=  image_processor.post_process_semantic_segmentation(outputs=__a    ,		target_sizes=[(5_00, 3_00)])
							_UpperCamelCase										=  torch.Size((5_00, 3_00))
							self.assertEqual(segmentation[0].shape    ,		__a)

							_UpperCamelCase										=  image_processor.post_process_semantic_segmentation(outputs=__a)
							_UpperCamelCase										=  torch.Size((1_28, 1_28))
							self.assertEqual(segmentation[0].shape    ,		__a)








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






