"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_a					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
						return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
						return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
						return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
						torch.manual_seed(0)
						__snake_case      :							Any						=			VQModel(
						    block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,  latent_channels=3     ,  num_vq_embeddings=self.num_embed     ,  vq_embed_dim=3     ,  )
						return model



				@property
				def  _lowercase					(self    :				List[Any])   ->     Optional[int]:
						__snake_case      :							Dict						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
						return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
						torch.manual_seed(0)
						__snake_case      :							Any						=			CLIPTextConfig(
						    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=self.text_embedder_hidden_size     ,  intermediate_size=37     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=10_00     ,  )
						return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
						torch.manual_seed(0)

						__snake_case      :							Any						=			12
						__snake_case      :							int						=			12

						__snake_case      :							List[Any]						=			{
						    'attention_bias': True,
						    'cross_attention_dim': 32,
						    'attention_head_dim': height * width,
						    'num_attention_heads': 1,
						    'num_vector_embeds': self.num_embed,
						    'num_embeds_ada_norm': self.num_embeds_ada_norm,
						    'norm_num_groups': 32,
						    'sample_size': width,
						    'activation_fn': 'geglu-approximate',
						}

						__snake_case      :							Union[str, Any]						=			TransformeraDModel(**_A)
						return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
						__snake_case      :							Tuple						=			'cpu'

						__snake_case      :							List[str]						=			self.dummy_vqvae
						__snake_case      :							str						=			self.dummy_text_encoder
						__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
						__snake_case      :							Dict						=			self.dummy_transformer
						__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
						__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

						__snake_case      :							List[Any]						=			VQDiffusionPipeline(
						    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
						__snake_case      :							List[Any]						=			pipe.to(_A)
						pipe.set_progress_bar_config(disable=_A)

						__snake_case      :							Optional[Any]						=			'teddy bear playing in the pool'

						__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
						__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
						__snake_case      :							Optional[int]						=			output.images

						__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
						__snake_case      :							Tuple						=			pipe(
						    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

						__snake_case      :							str						=			image[0, -3:, -3:, -1]
						__snake_case      :							Union[str, Any]						=			image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 24, 24, 3)

						__snake_case      :							str						=			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
						__snake_case      :							Optional[Any]						=			'cpu'

						__snake_case      :							Optional[int]						=			self.dummy_vqvae
						__snake_case      :							List[str]						=			self.dummy_text_encoder
						__snake_case      :							Optional[int]						=			self.dummy_tokenizer
						__snake_case      :							Optional[Any]						=			self.dummy_transformer
						__snake_case      :							Union[str, Any]						=			VQDiffusionScheduler(self.num_embed)
						__snake_case      :							Optional[int]						=			LearnedClassifierFreeSamplingEmbeddings(
						    learnable=_A     ,  hidden_size=self.text_embedder_hidden_size     ,  length=tokenizer.model_max_length)

						__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline(
						    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
						__snake_case      :							Union[str, Any]						=			pipe.to(_A)
						pipe.set_progress_bar_config(disable=_A)

						__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

						__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
						__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
						__snake_case      :							Optional[Any]						=			output.images

						__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
						__snake_case      :							Dict						=			pipe(
						    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

						__snake_case      :							int						=			image[0, -3:, -3:, -1]
						__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 24, 24, 3)

						__snake_case      :							Optional[Any]						=			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch_gpu
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
						__snake_case      :							List[Any]						=			load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

						__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
						__snake_case      :							Tuple						=			pipeline.to(_A)
						pipeline.set_progress_bar_config(disable=_A)

						# requires GPU generator for gumbel softmax
						# don't use GPU generator in tests though
						__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
						__snake_case      :							int						=			pipeline(
						    'teddy bear playing in the pool'     ,  num_images_per_prompt=1     ,  generator=_A     ,  output_type='np'     ,  )

						__snake_case      :							int						=			output.images[0]

						assert image.shape == (2_56, 2_56, 3)
						assert np.abs(expected_image - image).max() < 2.0






"""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      (   lowercase						):



				def  _lowercase					(self    :				Any)   ->     List[Any]:
					__snake_case      :							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 		UpperCamelCase      :



				def __init__(self    :				Optional[int]     ,  _A    :				Any     ,  _A    :				str=13     ,  _A    :				List[str]=64     ,  _A    :				List[Any]=3     ,  _A    :				Any=4     ,  _A    :				List[str]=[2, 2, 2, 2]     ,  _A    :				Tuple=[8, 4, 2, 1]     ,  _A    :				List[str]=[16, 32, 64, 1_28]     ,  _A    :				int=[1, 4, 8, 16]     ,  _A    :				List[str]=[1, 2, 4, 8]     ,  _A    :				Dict=True     ,  _A    :				Any=True     ,  _A    :				List[str]="gelu"     ,  _A    :				Optional[int]=0.1     ,  _A    :				Union[str, Any]=0.1     ,  _A    :				List[Any]=0.02     ,  _A    :				str=3     ,  _A    :				int=None     ,  )   ->     List[Any]:
					__snake_case      :							int						=			parent
					__snake_case      :							List[Any]						=			batch_size
					__snake_case      :							Optional[int]						=			image_size
					__snake_case      :							List[str]						=			num_channels
					__snake_case      :							Any						=			num_encoder_blocks
					__snake_case      :							Dict						=			sr_ratios
					__snake_case      :							Any						=			depths
					__snake_case      :							Tuple						=			hidden_sizes
					__snake_case      :							Tuple						=			downsampling_rates
					__snake_case      :							Union[str, Any]						=			num_attention_heads
					__snake_case      :							Optional[int]						=			is_training
					__snake_case      :							Any						=			use_labels
					__snake_case      :							List[Any]						=			hidden_act
					__snake_case      :							Union[str, Any]						=			hidden_dropout_prob
					__snake_case      :							Tuple						=			attention_probs_dropout_prob
					__snake_case      :							List[Any]						=			initializer_range
					__snake_case      :							Union[str, Any]						=			num_labels
					__snake_case      :							Dict						=			scope



				def  _lowercase					(self    :				List[Any])   ->     Tuple:
					__snake_case      :							Optional[Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

					__snake_case      :							Dict						=			self.get_config()
					return config, pixel_values, labels



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






				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			self.prepare_config_and_inputs()
					__snake_case		,    __snake_case		,    __snake_case      :							str						=			config_and_inputs
					__snake_case      :							List[str]						=			{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class 		UpperCamelCase      (   lowercase				,     lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				List[str]   =		(
				    (
				        SegformerModel,
				        SegformerForSemanticSegmentation,
				        SegformerForImageClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase    :				Dict   =		(
				    {
				        """feature-extraction""": SegformerModel,
				        """image-classification""": SegformerForImageClassification,
				        """image-segmentation""": SegformerForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase    :				Optional[int]   =		True
				UpperCAmelCase    :				Optional[int]   =		False
				UpperCAmelCase    :				List[Any]   =		False
				UpperCAmelCase    :				List[Any]   =		False



				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					__snake_case      :							Optional[int]						=			SegformerModelTester(self)
					__snake_case      :							Any						=			SegformerConfigTester(self     ,  config_class=_A)



				def  _lowercase					(self    :				List[str])   ->     Optional[int]:
					self.config_tester.run_common_tests()



				def  _lowercase					(self    :				List[Any])   ->     List[str]:
					__snake_case      :							int						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def  _lowercase					(self    :				Optional[int])   ->     str:
					__snake_case      :							List[Any]						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_binary_image_segmentation(*_A)



				def  _lowercase					(self    :				int)   ->     Union[str, Any]:
					__snake_case      :							Union[str, Any]						=			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  _lowercase					(self    :				Union[str, Any])   ->     str:
					pass



				@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
				def  _lowercase					(self    :				int)   ->     str:
					pass



				def  _lowercase					(self    :				List[str])   ->     Any:
					__snake_case		,    __snake_case      :							Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

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

						__snake_case      :							Optional[int]						=			['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  _A)



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					__snake_case		,    __snake_case      :							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case      :							Optional[int]						=			True

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

						__snake_case      :							int						=			sum(self.model_tester.depths)
						self.assertEqual(len(_A)     ,  _A)

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

						self.assertEqual(len(_A)     ,  _A)

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

						# Check attention is always last and order is fine
						__snake_case      :							Any						=			True
						__snake_case      :							Tuple						=			True
						__snake_case      :							Optional[Any]						=			model_class(_A)
						model.to(_A)
						model.eval()
						with torch.no_grad():
							__snake_case      :							Any						=			model(**self._prepare_for_class(_A     ,  _A))

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

						__snake_case      :							List[Any]						=			outputs.attentions

						self.assertEqual(len(_A)     ,  _A)
						# verify the first attentions (first block, first layer)
						__snake_case      :							Any						=			(self.model_tester.image_size // 4) ** 2
						__snake_case      :							List[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  _lowercase					(self    :				str)   ->     List[str]:
					def check_hidden_states_output(_A    :				Union[str, Any]     ,  _A    :				List[str]     ,  _A    :				Tuple):
						__snake_case      :							Tuple						=			model_class(_A)
						model.to(_A)
						model.eval()

						with torch.no_grad():
							__snake_case      :							Optional[int]						=			model(**self._prepare_for_class(_A     ,  _A))

						__snake_case      :							List[str]						=			outputs.hidden_states

						__snake_case      :							Tuple						=			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,
						    ]     ,  )

					__snake_case		,    __snake_case      :							Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						__snake_case      :							List[str]						=			True
						check_hidden_states_output(_A     ,  _A     ,  _A)

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

						check_hidden_states_output(_A     ,  _A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     int:
					if not self.model_tester.is_training:
						return

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

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

						__snake_case      :							Tuple						=			model_class(_A)
						model.to(_A)
						model.train()
						__snake_case      :							str						=			self._prepare_for_class(_A     ,  _A     ,  return_labels=_A)
						__snake_case      :							Dict						=			model(**_A).loss
						loss.backward()



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






				@slow
				def  _lowercase					(self    :				Any)   ->     List[str]:
					for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							List[Any]						=			SegformerModel.from_pretrained(_A)
						self.assertIsNotNone(_A)







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

	'''simple docstring'''





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

@require_torch
class 		UpperCamelCase      (   unittest.TestCase						):



				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     Any:
					# only resize + normalize
					__snake_case      :							List[str]						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Tuple						=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
					    _A)

					__snake_case      :							Optional[Any]						=			prepare_img()
					__snake_case      :							Tuple						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							List[str]						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Optional[int]						=			torch.Size((1, model.config.num_labels, 1_28, 1_28))
					self.assertEqual(outputs.logits.shape     ,  _A)

					__snake_case      :							Tuple						=			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(_A)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,  _A     ,  atol=1E-4))



				@slow
				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# only resize + normalize
					__snake_case      :							int						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Tuple						=			SegformerForSemanticSegmentation.from_pretrained(
					    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(_A)

					__snake_case      :							str						=			prepare_img()
					__snake_case      :							Union[str, Any]						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							str						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Any						=			torch.Size((1, model.config.num_labels, 1_28, 1_28))
					self.assertEqual(outputs.logits.shape     ,  _A)

					__snake_case      :							List[Any]						=			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(_A)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,  _A     ,  atol=1E-1))






				@slow
				def  _lowercase					(self    :				Optional[int])   ->     Union[str, Any]:
					# only resize + normalize
					__snake_case      :							List[Any]						=			SegformerImageProcessor(
					    image_scale=(5_12, 5_12)     ,  keep_ratio=_A     ,  align=_A     ,  do_random_crop=_A)
					__snake_case      :							Optional[Any]						=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
					    _A)

					__snake_case      :							Optional[Any]						=			prepare_img()
					__snake_case      :							Optional[Any]						=			image_processor(images=_A     ,  return_tensors='pt')
					__snake_case      :							Union[str, Any]						=			encoded_inputs.pixel_values.to(_A)

					with torch.no_grad():
						__snake_case      :							Any						=			model(_A)

					__snake_case      :							Optional[Any]						=			outputs.logits.detach().cpu()

					__snake_case      :							Any						=			image_processor.post_process_semantic_segmentation(outputs=_A     ,  target_sizes=[(5_00, 3_00)])
					__snake_case      :							Any						=			torch.Size((5_00, 3_00))
					self.assertEqual(segmentation[0].shape     ,  _A)

					__snake_case      :							Any						=			image_processor.post_process_semantic_segmentation(outputs=_A)
					__snake_case      :							str						=			torch.Size((1_28, 1_28))
					self.assertEqual(segmentation[0].shape     ,  _A)





