


import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    FocalNetBackbone,
			    FocalNetForImageClassification,
			    FocalNetForMaskedImageModeling,
			    FocalNetModel,
			)
			from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor







class lowerCAmelCase_						:

	'''simple docstring'''

	def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=16		,				_UpperCAmelCase=[32, 64, 1_28]		,				_UpperCAmelCase=[1, 2, 1]		,				_UpperCAmelCase=[2, 2, 4]		,				_UpperCAmelCase=2		,				_UpperCAmelCase=2.0		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase=True		,				_UpperCAmelCase=None		,				_UpperCAmelCase=True		,				_UpperCAmelCase=10		,				_UpperCAmelCase=8		,				_UpperCAmelCase=["stage1", "stage2"]		,				_UpperCAmelCase=[1, 2]		,				):
							snake_case_	=				parent
							snake_case_	=				batch_size
							snake_case_	=				image_size
							snake_case_	=				patch_size
							snake_case_	=				num_channels
							snake_case_	=				embed_dim
							snake_case_	=				hidden_sizes
							snake_case_	=				depths
							snake_case_	=				num_heads
							snake_case_	=				window_size
							snake_case_	=				mlp_ratio
							snake_case_	=				qkv_bias
							snake_case_	=				hidden_dropout_prob
							snake_case_	=				attention_probs_dropout_prob
							snake_case_	=				drop_path_rate
							snake_case_	=				hidden_act
							snake_case_	=				use_absolute_embeddings
							snake_case_	=				patch_norm
							snake_case_	=				layer_norm_eps
							snake_case_	=				initializer_range
							snake_case_	=				is_training
							snake_case_	=				scope
							snake_case_	=				use_labels
							snake_case_	=				type_sequence_label_size
							snake_case_	=				encoder_stride
							snake_case_	=				out_features
							snake_case_	=				out_indices



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							snake_case_	=				None
							if self.use_labels:
													snake_case_	=				ids_tensor([self.batch_size]		,				self.type_sequence_label_size )

							snake_case_	=				self.get_config()

							return config, pixel_values, labels



	def 						UpperCamelCase__					(     self ):
							return FocalNetConfig(
							    image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				embed_dim=self.embed_dim		,				hidden_sizes=self.hidden_sizes		,				depths=self.depths		,				num_heads=self.num_heads		,				window_size=self.window_size		,				mlp_ratio=self.mlp_ratio		,				qkv_bias=self.qkv_bias		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				drop_path_rate=self.drop_path_rate		,				hidden_act=self.hidden_act		,				use_absolute_embeddings=self.use_absolute_embeddings		,				path_norm=self.patch_norm		,				layer_norm_eps=self.layer_norm_eps		,				initializer_range=self.initializer_range		,				encoder_stride=self.encoder_stride		,				out_features=self.out_features		,				out_indices=self.out_indices		,				)



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							snake_case_	=				FocalNetModel(config=_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()
							snake_case_	=				model(_UpperCAmelCase )

							snake_case_	=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
							snake_case_	=				int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

							self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, expected_seq_len, expected_dim) )



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							snake_case_	=				FocalNetBackbone(config=_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()
							snake_case_	=				model(_UpperCAmelCase )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps )		,				len(config.out_features ) )
							self.parent.assertListEqual(list(result.feature_maps[0].shape )		,				[self.batch_size, self.image_size, 8, 8] )

							# verify channels
							self.parent.assertEqual(len(model.channels )		,				len(config.out_features ) )
							self.parent.assertListEqual(model.channels		,				config.hidden_sizes[:-1] )

							# verify backbone works with out_features=None
							snake_case_	=				None
							snake_case_	=				FocalNetBackbone(config=_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()
							snake_case_	=				model(_UpperCAmelCase )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps )		,				1 )
							self.parent.assertListEqual(list(result.feature_maps[0].shape )		,				[self.batch_size, self.image_size * 2, 4, 4] )

							# verify channels
							self.parent.assertEqual(len(model.channels )		,				1 )
							self.parent.assertListEqual(model.channels		,				[config.hidden_sizes[-1]] )



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							snake_case_	=				FocalNetForMaskedImageModeling(config=_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()
							snake_case_	=				model(_UpperCAmelCase )
							self.parent.assertEqual(
							    result.reconstruction.shape		,				(self.batch_size, self.num_channels, self.image_size, self.image_size) )

							# test greyscale images
							snake_case_	=				1
							snake_case_	=				FocalNetForMaskedImageModeling(_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()

							snake_case_	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							snake_case_	=				model(_UpperCAmelCase )
							self.parent.assertEqual(result.reconstruction.shape		,				(self.batch_size, 1, self.image_size, self.image_size) )



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							snake_case_	=				self.type_sequence_label_size
							snake_case_	=				FocalNetForImageClassification(_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()
							snake_case_	=				model(_UpperCAmelCase		,				labels=_UpperCAmelCase )
							self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							snake_case_	=				1
							snake_case_	=				FocalNetForImageClassification(_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()

							snake_case_	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							snake_case_	=				model(_UpperCAmelCase )
							self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size) )





	def 						UpperCamelCase__					(     self ):
							snake_case_	=				self.prepare_config_and_inputs()

							snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs
							snake_case_	=				{'''pixel_values''': pixel_values}
							return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

	'''simple docstring'''

	__snake_case												=  (
	    (
	        FocalNetModel,
	        FocalNetForImageClassification,
	        FocalNetForMaskedImageModeling,
	        FocalNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	__snake_case												=  (
	    {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__snake_case												=  False

	__snake_case												=  False
	__snake_case												=  False
	__snake_case												=  False
	__snake_case												=  False



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				FocalNetModelTester(self )
							snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase		,				embed_dim=37		,				has_text_modality=_UpperCAmelCase )



	def 						UpperCamelCase__					(     self ):
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()



	def 						UpperCamelCase__					(     self ):
							return



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase )



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*_UpperCAmelCase )



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase )



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )



	@unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
	def 						UpperCamelCase__					(     self ):
							pass



	@unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
	def 						UpperCamelCase__					(     self ):
							pass



	def 						UpperCamelCase__					(     self ):
							snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
													snake_case_	=				model_class(_UpperCAmelCase )
													self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module) )
													snake_case_	=				model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_UpperCAmelCase		,				nn.Linear ) )



	def 						UpperCamelCase__					(     self ):
							snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
													snake_case_	=				model_class(_UpperCAmelCase )
													snake_case_	=				inspect.signature(model.forward )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													snake_case_	=				[*signature.parameters.keys()]

													snake_case_	=				['''pixel_values''']
													self.assertListEqual(arg_names[:1]		,				_UpperCAmelCase )



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							snake_case_	=				model_class(_UpperCAmelCase )
							model.to(_UpperCAmelCase )
							model.eval()

							with torch.no_grad():
													snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )

							snake_case_	=				outputs.hidden_states

							snake_case_	=				getattr(
							    self.model_tester		,				'''expected_num_hidden_layers'''		,				len(self.model_tester.depths ) + 1 )
							self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

							# FocalNet has a different seq_length
							snake_case_	=				(
							    config.patch_size
							    if isinstance(config.patch_size		,				collections.abc.Iterable )
							    else (config.patch_size, config.patch_size)
							)

							snake_case_	=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:] )		,				[num_patches, self.model_tester.embed_dim]		,				)

							snake_case_	=				outputs.reshaped_hidden_states
							self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

							snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				reshaped_hidden_states[0].shape
							snake_case_	=				(
							    reshaped_hidden_states[0].view(_UpperCAmelCase		,				_UpperCAmelCase		,				height * width ).permute(0		,				2		,				1 )
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:] )		,				[num_patches, self.model_tester.embed_dim]		,				)



	def 						UpperCamelCase__					(     self ):
							snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

							snake_case_	=				(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size		,				collections.abc.Iterable )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes[:-1]:
													snake_case_	=				True
													self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

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

													self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )



	def 						UpperCamelCase__					(     self ):
							snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_	=				3

							snake_case_	=				(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size		,				collections.abc.Iterable )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							snake_case_	=				(
							    config.patch_size
							    if isinstance(config.patch_size		,				collections.abc.Iterable )
							    else (config.patch_size, config.patch_size)
							)

							snake_case_	=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							snake_case_	=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes[:-1]:
													snake_case_	=				True
													self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				(padded_height, padded_width) )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													snake_case_	=				True
													self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				(padded_height, padded_width) )



	@slow
	def 						UpperCamelCase__					(     self ):
							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case_	=				FocalNetModel.from_pretrained(_UpperCAmelCase )
													self.assertIsNotNone(_UpperCAmelCase )





	def 						UpperCamelCase__					(     self ):
							snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

							snake_case_	=				_config_zero_init(_UpperCAmelCase )
							for model_class in self.all_model_classes:
													snake_case_	=				model_class(config=_UpperCAmelCase )
													for name, param in model.named_parameters():
																			if "embeddings" not in name and param.requires_grad:
																									self.assertIn(
																									    ((param.data.mean() * 1E9).round() / 1E9).item()		,				[0.0, 1.0]		,				msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''		,				)







@require_vision
@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

	'''simple docstring'''

	@cached_property
	def 						UpperCamelCase__					(     self ):
							# TODO update organization
							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None





	@slow
	def 						UpperCamelCase__					(     self ):
							snake_case_	=				FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(_UpperCAmelCase )
							snake_case_	=				self.default_image_processor

							snake_case_	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
							snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' ).to(_UpperCAmelCase )

							# forward pass
							with torch.no_grad():
													snake_case_	=				model(**_UpperCAmelCase )

							# verify the logits
							snake_case_	=				torch.Size((1, 10_00) )
							self.assertEqual(outputs.logits.shape		,				_UpperCAmelCase )
							snake_case_	=				torch.tensor([0.2_166, -0.4_368, 0.2_191] ).to(_UpperCAmelCase )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )
							self.assertTrue(outputs.logits.argmax(dim=-1 ).item()		,				2_81 )







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

	'''simple docstring'''

	__snake_case												=  (FocalNetBackbone,) if is_torch_available() else ()
	__snake_case												=  FocalNetConfig

	__snake_case												=  False



	def 						UpperCamelCase__					(     self ):
							snake_case_	=				FocalNetModelTester(self )



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(_UpperCAmelCase		,				'''hidden_sizes''' ) )
       self.parent.assertTrue(hasattr(_UpperCAmelCase		,				'''num_attention_heads''' ) )
       self.parent.assertTrue(hasattr(_UpperCAmelCase		,				'''num_encoder_blocks''' ) )







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=64		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=[2, 2, 2, 2]		,				_UpperCAmelCase=[8, 4, 2, 1]		,				_UpperCAmelCase=[16, 32, 64, 1_28]		,				_UpperCAmelCase=[1, 4, 8, 16]		,				_UpperCAmelCase=[1, 2, 4, 8]		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				image_size
       snake_case_	=				num_channels
       snake_case_	=				num_encoder_blocks
       snake_case_	=				sr_ratios
       snake_case_	=				depths
       snake_case_	=				hidden_sizes
       snake_case_	=				downsampling_rates
       snake_case_	=				num_attention_heads
       snake_case_	=				is_training
       snake_case_	=				use_labels
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				initializer_range
       snake_case_	=				num_labels
       snake_case_	=				scope



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

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



 def 						UpperCamelCase__					(     self ):
       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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				SegformerModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )
       snake_case_	=				snake_case_	=				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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.num_labels
       snake_case_	=				SegformerForSemanticSegmentation(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )
       self.parent.assertEqual(
           result.logits.shape		,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
       snake_case_	=				model(_UpperCAmelCase		,				labels=_UpperCAmelCase )
       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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				1
       snake_case_	=				SegformerForSemanticSegmentation(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				torch.randint(0		,				1		,				(self.batch_size, self.image_size, self.image_size) ).to(_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				labels=_UpperCAmelCase )
       self.parent.assertGreater(result.loss		,				0.0 )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs
       snake_case_	=				{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 __snake_case												=  (
     {
         "feature-extraction": SegformerModel,
         "image-classification": SegformerForImageClassification,
         "image-segmentation": SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __snake_case												=  True
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				SegformerModelTester(self )
       snake_case_	=				SegformerConfigTester(self		,				config_class=_UpperCAmelCase )



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_binary_image_segmentation(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase )



 @unittest.skip('''SegFormer does not use inputs_embeds''' )
 def 						UpperCamelCase__					(     self ):
       pass



 @unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods''' )
 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case_	=				['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_	=				True

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

             snake_case_	=				sum(self.model_tester.depths )
             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

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

             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

             # verify the first attentions (first block, first layer)
             snake_case_	=				(self.model_tester.image_size // 4) ** 2
             snake_case_	=				(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)
             snake_case_	=				(self.model_tester.image_size // 32) ** 2
             snake_case_	=				(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]		,				)
             snake_case_	=				len(_UpperCAmelCase )

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

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

             snake_case_	=				outputs.attentions

             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )
             # verify the first attentions (first block, first layer)
             snake_case_	=				(self.model_tester.image_size // 4) ** 2
             snake_case_	=				(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 ):
       def check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				model_class(_UpperCAmelCase )
             model.to(_UpperCAmelCase )
             model.eval()

             with torch.no_grad():
                   snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )

             snake_case_	=				outputs.hidden_states

             snake_case_	=				self.model_tester.num_encoder_blocks
             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

             # 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,
                 ]		,				)

       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             snake_case_	=				True
             check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

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

             check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )



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

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

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

             snake_case_	=				model_class(_UpperCAmelCase )
             model.to(_UpperCAmelCase )
             model.train()
             snake_case_	=				self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase		,				return_labels=_UpperCAmelCase )
             snake_case_	=				model(**_UpperCAmelCase ).loss
             loss.backward()



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





 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				SegformerModel.from_pretrained(_UpperCAmelCase )
             self.assertIsNotNone(_UpperCAmelCase )






def 	__lowerCAmelCase   ()->  List[str]:




      """simple docstring"""

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







@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @slow
 def 						UpperCamelCase__					(     self ):
       # only resize + normalize
       snake_case_	=				SegformerImageProcessor(
           image_scale=(5_12, 5_12)		,				keep_ratio=_UpperCAmelCase		,				align=_UpperCAmelCase		,				do_random_crop=_UpperCAmelCase )
       snake_case_	=				SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''' ).to(
           _UpperCAmelCase )

       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				encoded_inputs.pixel_values.to(_UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(_UpperCAmelCase )

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

       snake_case_	=				torch.tensor(
           [
               [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
               [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
               [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
           ] ).to(_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )



 @slow
 def 						UpperCamelCase__					(     self ):
       # only resize + normalize
       snake_case_	=				SegformerImageProcessor(
           image_scale=(5_12, 5_12)		,				keep_ratio=_UpperCAmelCase		,				align=_UpperCAmelCase		,				do_random_crop=_UpperCAmelCase )
       snake_case_	=				SegformerForSemanticSegmentation.from_pretrained(
           '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024''' ).to(_UpperCAmelCase )

       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				encoded_inputs.pixel_values.to(_UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(_UpperCAmelCase )

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

       snake_case_	=				torch.tensor(
           [
               [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
               [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
               [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
           ] ).to(_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,				_UpperCAmelCase		,				atol=1E-1 ) )





 @slow
 def 						UpperCamelCase__					(     self ):
       # only resize + normalize
       snake_case_	=				SegformerImageProcessor(
           image_scale=(5_12, 5_12)		,				keep_ratio=_UpperCAmelCase		,				align=_UpperCAmelCase		,				do_random_crop=_UpperCAmelCase )
       snake_case_	=				SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''' ).to(
           _UpperCAmelCase )

       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				encoded_inputs.pixel_values.to(_UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(_UpperCAmelCase )

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

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

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