



"""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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.config_class(**self.inputs_dict)
				self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "hidden_sizes"))
				self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "num_attention_heads"))
				self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "num_encoder_blocks"))






class      A__  :




	'''simple docstring'''





	def __init__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=13			,     _SCREAMING_SNAKE_CASE:						Optional[int]=64			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=3			,     _SCREAMING_SNAKE_CASE:						Tuple=4			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=[2, 2, 2, 2]			,     _SCREAMING_SNAKE_CASE:						Optional[int]=[8, 4, 2, 1]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=[16, 32, 64, 128]			,     _SCREAMING_SNAKE_CASE:						Optional[int]=[1, 4, 8, 16]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=[1, 2, 4, 8]			,     _SCREAMING_SNAKE_CASE:						List[Any]=True			,     _SCREAMING_SNAKE_CASE:						Dict=True			,     _SCREAMING_SNAKE_CASE:						Optional[int]="gelu"			,     _SCREAMING_SNAKE_CASE:						Dict=0.1			,     _SCREAMING_SNAKE_CASE:						str=0.1			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=3			,     _SCREAMING_SNAKE_CASE:						Dict=None			,     )							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			parent
				__lowerCAmelCase :    Optional[int]				=			batch_size
				__lowerCAmelCase :    Dict				=			image_size
				__lowerCAmelCase :    List[str]				=			num_channels
				__lowerCAmelCase :    List[Any]				=			num_encoder_blocks
				__lowerCAmelCase :    Any				=			sr_ratios
				__lowerCAmelCase :    int				=			depths
				__lowerCAmelCase :    str				=			hidden_sizes
				__lowerCAmelCase :    List[Any]				=			downsampling_rates
				__lowerCAmelCase :    int				=			num_attention_heads
				__lowerCAmelCase :    Union[str, Any]				=			is_training
				__lowerCAmelCase :    Optional[Any]				=			use_labels
				__lowerCAmelCase :    Optional[Any]				=			hidden_act
				__lowerCAmelCase :    int				=			hidden_dropout_prob
				__lowerCAmelCase :    List[str]				=			attention_probs_dropout_prob
				__lowerCAmelCase :    int				=			initializer_range
				__lowerCAmelCase :    Union[str, Any]				=			num_labels
				__lowerCAmelCase :    str				=			scope

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

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				__lowerCAmelCase :    List[Any]				=			None
				if self.use_labels:
							__lowerCAmelCase :    str				=			ids_tensor([self.batch_size, self.image_size, self.image_size]			,     self.num_labels)

				__lowerCAmelCase :    Any				=			self.get_config()
				return config, pixel_values, labels

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Any:

				"""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 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Dict)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			SegformerModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    int				=			model(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Dict				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			self.num_labels
				__lowerCAmelCase :    Optional[int]				=			SegformerForSemanticSegmentation(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    int				=			model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(
				    result.logits.shape			,     (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
				__lowerCAmelCase :    Union[str, Any]				=			model(_SCREAMING_SNAKE_CASE			,     labels=_SCREAMING_SNAKE_CASE)
				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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			1
				__lowerCAmelCase :    Any				=			SegformerForSemanticSegmentation(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    List[str]				=			torch.randint(0			,     1			,     (self.batch_size, self.image_size, self.image_size)).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			model(_SCREAMING_SNAKE_CASE			,     labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertGreater(result.loss			,     0.0)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			self.prepare_config_and_inputs()
				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			config_and_inputs
				__lowerCAmelCase :    Optional[Any]				=			{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





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

	SCREAMING_SNAKE_CASE        =      True
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			SegformerModelTester(self)
				__lowerCAmelCase :    str				=			SegformerConfigTester(self			,     config_class=_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				self.config_tester.run_common_tests()

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

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_binary_image_segmentation(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_segmentation(*_SCREAMING_SNAKE_CASE)

	@unittest.skip("SegFormer does not use inputs_embeds")
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				pass

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

				"""simple docstring"""

				pass

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    str				=			self.model_tester.prepare_config_and_inputs_for_common()

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

							__lowerCAmelCase :    Tuple				=			["pixel_values"]
							self.assertListEqual(arg_names[:1]			,     _SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    int				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    Dict				=			True

				for model_class in self.all_model_classes:
							__lowerCAmelCase :    Tuple				=			True
							__lowerCAmelCase :    Any				=			False
							__lowerCAmelCase :    int				=			True
							__lowerCAmelCase :    Any				=			model_class(_SCREAMING_SNAKE_CASE)
							model.to(_SCREAMING_SNAKE_CASE)
							model.eval()
							with torch.no_grad():
										__lowerCAmelCase :    Dict				=			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))
							__lowerCAmelCase :    Optional[int]				=			outputs.attentions

							__lowerCAmelCase :    Union[str, Any]				=			sum(self.model_tester.depths)
							self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

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

							self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

							# verify the first attentions (first block, first layer)
							__lowerCAmelCase :    Optional[int]				=			(self.model_tester.image_size // 4) ** 2
							__lowerCAmelCase :    Dict				=			(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)
							__lowerCAmelCase :    Tuple				=			(self.model_tester.image_size // 32) ** 2
							__lowerCAmelCase :    List[Any]				=			(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]			,     )
							__lowerCAmelCase :    List[str]				=			len(_SCREAMING_SNAKE_CASE)

							# Check attention is always last and order is fine
							__lowerCAmelCase :    List[str]				=			True
							__lowerCAmelCase :    Optional[int]				=			True
							__lowerCAmelCase :    int				=			model_class(_SCREAMING_SNAKE_CASE)
							model.to(_SCREAMING_SNAKE_CASE)
							model.eval()
							with torch.no_grad():
										__lowerCAmelCase :    List[Any]				=			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))

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

							__lowerCAmelCase :    Optional[int]				=			outputs.attentions

							self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
							# verify the first attentions (first block, first layer)
							__lowerCAmelCase :    Dict				=			(self.model_tester.image_size // 4) ** 2
							__lowerCAmelCase :    Any				=			(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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			int:

				"""simple docstring"""

				def check_hidden_states_output(_SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						List[str]):
							__lowerCAmelCase :    int				=			model_class(_SCREAMING_SNAKE_CASE)
							model.to(_SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
										__lowerCAmelCase :    Tuple				=			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))

							__lowerCAmelCase :    Tuple				=			outputs.hidden_states

							__lowerCAmelCase :    Union[str, Any]				=			self.model_tester.num_encoder_blocks
							self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

							# 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,
							    ]			,     )

				__lowerCAmelCase		,      __lowerCAmelCase :    Union[str, Any]				=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase :    List[Any]				=			True
							check_hidden_states_output(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__lowerCAmelCase :    Any				=			True

							check_hidden_states_output(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				if not self.model_tester.is_training:
							return

				__lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    Any				=			True

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

							__lowerCAmelCase :    List[Any]				=			model_class(_SCREAMING_SNAKE_CASE)
							model.to(_SCREAMING_SNAKE_CASE)
							model.train()
							__lowerCAmelCase :    Union[str, Any]				=			self._prepare_for_class(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     return_labels=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    List[Any]				=			model(**_SCREAMING_SNAKE_CASE).loss
							loss.backward()

	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Optional[int]:

				"""simple docstring"""

				pass







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			List[Any]:

				"""simple docstring"""

				for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase :    Dict				=			SegformerModel.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






def      _lowercase							(    )		->  List[Any]:
			__lowerCAmelCase :    str				=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
			return image





@require_torch
class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			SegformerImageProcessor(
				    image_scale=(512, 512)			,     keep_ratio=_SCREAMING_SNAKE_CASE			,     align=_SCREAMING_SNAKE_CASE			,     do_random_crop=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
				    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Any				=			prepare_img()
				__lowerCAmelCase :    Any				=			image_processor(images=_SCREAMING_SNAKE_CASE			,     return_tensors="pt")
				__lowerCAmelCase :    Any				=			encoded_inputs.pixel_values.to(_SCREAMING_SNAKE_CASE)

				with torch.no_grad():
							__lowerCAmelCase :    Dict				=			model(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			torch.Size((1, model.config.num_labels, 128, 128))
				self.assertEqual(outputs.logits.shape			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Any				=			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(_SCREAMING_SNAKE_CASE)
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,     _SCREAMING_SNAKE_CASE			,     atol=1e-4))

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

				"""simple docstring"""

				__lowerCAmelCase :    int				=			SegformerImageProcessor(
				    image_scale=(512, 512)			,     keep_ratio=_SCREAMING_SNAKE_CASE			,     align=_SCREAMING_SNAKE_CASE			,     do_random_crop=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Dict				=			SegformerForSemanticSegmentation.from_pretrained(
				    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024").to(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			prepare_img()
				__lowerCAmelCase :    Optional[Any]				=			image_processor(images=_SCREAMING_SNAKE_CASE			,     return_tensors="pt")
				__lowerCAmelCase :    Tuple				=			encoded_inputs.pixel_values.to(_SCREAMING_SNAKE_CASE)

				with torch.no_grad():
							__lowerCAmelCase :    Any				=			model(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[int]				=			torch.Size((1, model.config.num_labels, 128, 128))
				self.assertEqual(outputs.logits.shape			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			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(_SCREAMING_SNAKE_CASE)
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,     _SCREAMING_SNAKE_CASE			,     atol=1e-1))







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			SegformerImageProcessor(
				    image_scale=(512, 512)			,     keep_ratio=_SCREAMING_SNAKE_CASE			,     align=_SCREAMING_SNAKE_CASE			,     do_random_crop=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
				    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Any				=			prepare_img()
				__lowerCAmelCase :    Dict				=			image_processor(images=_SCREAMING_SNAKE_CASE			,     return_tensors="pt")
				__lowerCAmelCase :    List[str]				=			encoded_inputs.pixel_values.to(_SCREAMING_SNAKE_CASE)

				with torch.no_grad():
							__lowerCAmelCase :    int				=			model(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Tuple				=			outputs.logits.detach().cpu()

				__lowerCAmelCase :    Any				=			image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE			,     target_sizes=[(500, 300)])
				__lowerCAmelCase :    Union[str, Any]				=			torch.Size((500, 300))
				self.assertEqual(segmentation[0].shape			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			torch.Size((128, 128))
				self.assertEqual(segmentation[0].shape			,     _SCREAMING_SNAKE_CASE)




"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()