

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_      (	_lowercase	):




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			Union[str, Any]		    =			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      lowerCamelCase_      :




	def __init__(     self						:     List[str]					,      __A						:     str					,      __A						:     str=13					,      __A						:     str=64					,      __A						:     Any=3					,      __A						:     List[str]=4					,      __A						:     Union[str, Any]=[2, 2, 2, 2]					,      __A						:     Any=[8, 4, 2, 1]					,      __A						:     int=[16, 32, 64, 128]					,      __A						:     List[str]=[1, 4, 8, 16]					,      __A						:     Optional[int]=[1, 2, 4, 8]					,      __A						:     Optional[int]=True					,      __A						:     List[Any]=True					,      __A						:     List[Any]="gelu"					,      __A						:     Union[str, Any]=0.1					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=0.0_2					,      __A						:     Dict=3					,      __A						:     Any=None					,      ):
		__A :			Optional[Any]		    =			parent
		__A :			int		    =			batch_size
		__A :			Optional[int]		    =			image_size
		__A :			Tuple		    =			num_channels
		__A :			List[Any]		    =			num_encoder_blocks
		__A :			List[Any]		    =			sr_ratios
		__A :			Optional[int]		    =			depths
		__A :			str		    =			hidden_sizes
		__A :			str		    =			downsampling_rates
		__A :			Any		    =			num_attention_heads
		__A :			Optional[int]		    =			is_training
		__A :			Union[str, Any]		    =			use_labels
		__A :			Any		    =			hidden_act
		__A :			Union[str, Any]		    =			hidden_dropout_prob
		__A :			Tuple		    =			attention_probs_dropout_prob
		__A :			Any		    =			initializer_range
		__A :			Dict		    =			num_labels
		__A :			List[Any]		    =			scope




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			List[str]		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

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




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		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        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Dict					,      __A						:     List[Any]					,      __A						:     str							):
		__A :			List[Any]		    =			SegformerModel(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			Dict		    =			model(__A							)
		__A :			Union[str, Any]		    =			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        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     int					,      __A						:     Tuple					,      __A						:     List[str]							):
		__A :			int		    =			self.num_labels
		__A :			int		    =			SegformerForSemanticSegmentation(__A							)
		model.to(__A							)
		model.eval()
		__A :			int		    =			model(__A							)
		self.parent.assertEqual(
		    result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)							)
		__A :			Union[str, Any]		    =			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        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     List[Any]					,      __A						:     List[str]					,      __A						:     Optional[Any]							):
		__A :			List[str]		    =			1
		__A :			Union[str, Any]		    =			SegformerForSemanticSegmentation(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			Any		    =			torch.randint(0					,      1					,      (self.batch_size, self.image_size, self.image_size)							).to(__A							)
		__A :			int		    =			model(__A					,      labels=__A							)
		self.parent.assertGreater(result.loss					,      0.0							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Optional[int]		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A :			Tuple		    =			config_and_inputs
		__A :			str		    =			{"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   List[Any]     					=				(
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowercase		:   List[Any]     					=				(
	    {
	        '''feature-extraction''': SegformerModel,
	        '''image-classification''': SegformerForImageClassification,
	        '''image-segmentation''': SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_lowercase		:   int     					=				True
	_lowercase		:   Optional[int]     					=				False
	_lowercase		:   Union[str, Any]     					=				False
	_lowercase		:   int     					=				False




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			str		    =			SegformerModelTester(self							)
		__A :			Optional[int]		    =			SegformerConfigTester(self					,      config_class=__A							)




	def        lowerCAmelCase_       (     self						:     List[str]							):
		self.config_tester.run_common_tests()




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			List[Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_binary_image_segmentation(*__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[int]		    =			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        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		pass




	@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""							)
	def        lowerCAmelCase_       (     self						:     Tuple							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A    ,				__A :			Optional[int]		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

			__A :			str		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()
		__A :			Union[str, Any]		    =			True

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

			__A :			List[str]		    =			sum(self.model_tester.depths							)
			self.assertEqual(len(__A							)					,      __A							)

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

			self.assertEqual(len(__A							)					,      __A							)

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

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

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

			__A :			List[Any]		    =			outputs.attentions

			self.assertEqual(len(__A							)					,      __A							)
			# verify the first attentions (first block, first layer)
			__A :			Tuple		    =			(self.model_tester.image_size // 4) ** 2
			__A :			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        lowerCAmelCase_       (     self						:     Tuple							):
		def check_hidden_states_output(__A						:     Optional[int]					,      __A						:     int					,      __A						:     Dict							):
			__A :			List[str]		    =			model_class(__A							)
			model.to(__A							)
			model.eval()

			with torch.no_grad():
				__A :			Any		    =			model(**self._prepare_for_class(__A					,      __A							)							)

			__A :			Optional[Any]		    =			outputs.hidden_states

			__A :			Dict		    =			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,
			    ]					,      )

		__A    ,				__A :			Dict		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			Optional[int]		    =			True
			check_hidden_states_output(__A					,      __A					,      __A							)

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

			check_hidden_states_output(__A					,      __A					,      __A							)




	def        lowerCAmelCase_       (     self						:     str							):
		if not self.model_tester.is_training:
			return

		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()
		__A :			List[str]		    =			True

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

			__A :			List[str]		    =			model_class(__A							)
			model.to(__A							)
			model.train()
			__A :			Any		    =			self._prepare_for_class(__A					,      __A					,      return_labels=__A							)
			__A :			Optional[int]		    =			model(**__A							).loss
			loss.backward()




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




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__A :			Dict		    =			SegformerModel.from_pretrained(__A							)
			self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				Optional[Any]:
	__A :			Optional[Any]		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image







@require_torch
class      lowerCamelCase_      (	unittest.TestCase	):




	@slow
	def        lowerCAmelCase_       (     self						:     str							):
		# only resize + normalize
		__A :			Optional[int]		    =			SegformerImageProcessor(
		    image_scale=(512, 512)					,      keep_ratio=__A					,      align=__A					,      do_random_crop=__A							)
		__A :			Optional[int]		    =			SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""							).to(
		    __A							)

		__A :			int		    =			prepare_img()
		__A :			List[Any]		    =			image_processor(images=__A					,      return_tensors="""pt"""							)
		__A :			Any		    =			encoded_inputs.pixel_values.to(__A							)

		with torch.no_grad():
			__A :			List[str]		    =			model(__A							)

		__A :			Tuple		    =			torch.Size((1, model.config.num_labels, 128, 128)							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Any		    =			torch.tensor(
		    [
		        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
		        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
		        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
		    ]							).to(__A							)
		self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,      __A					,      atol=1e-4							)							)




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		# only resize + normalize
		__A :			str		    =			SegformerImageProcessor(
		    image_scale=(512, 512)					,      keep_ratio=__A					,      align=__A					,      do_random_crop=__A							)
		__A :			Tuple		    =			SegformerForSemanticSegmentation.from_pretrained(
		    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""							).to(__A							)

		__A :			Optional[int]		    =			prepare_img()
		__A :			int		    =			image_processor(images=__A					,      return_tensors="""pt"""							)
		__A :			List[Any]		    =			encoded_inputs.pixel_values.to(__A							)

		with torch.no_grad():
			__A :			Dict		    =			model(__A							)

		__A :			int		    =			torch.Size((1, model.config.num_labels, 128, 128)							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Optional[int]		    =			torch.tensor(
		    [
		        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
		        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
		        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
		    ]							).to(__A							)
		self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,      __A					,      atol=1e-1							)							)




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# only resize + normalize
		__A :			Tuple		    =			SegformerImageProcessor(
		    image_scale=(512, 512)					,      keep_ratio=__A					,      align=__A					,      do_random_crop=__A							)
		__A :			List[Any]		    =			SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""							).to(
		    __A							)

		__A :			Any		    =			prepare_img()
		__A :			str		    =			image_processor(images=__A					,      return_tensors="""pt"""							)
		__A :			int		    =			encoded_inputs.pixel_values.to(__A							)

		with torch.no_grad():
			__A :			int		    =			model(__A							)

		__A :			Optional[int]		    =			outputs.logits.detach().cpu()

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

		__A :			Optional[Any]		    =			image_processor.post_process_semantic_segmentation(outputs=__A							)
		__A :			Dict		    =			torch.Size((128, 128)							)
		self.assertEqual(segmentation[0].shape					,      __A							)









'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

