






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_      (			UpperCamelCase__				):



	'''simple docstring'''




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							self.config_class(**self.inputs_dict					)
								self.parent.assertTrue(hasattr(lowerCAmelCase__       ,			'hidden_sizes'					)					)
								self.parent.assertTrue(hasattr(lowerCAmelCase__       ,			'num_attention_heads'					)					)
								self.parent.assertTrue(hasattr(lowerCAmelCase__       ,			'num_encoder_blocks'					)					)






class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=64       ,			a=3       ,			a=4       ,			a=[2, 2, 2, 2]       ,			a=[8, 4, 2, 1]       ,			a=[16, 32, 64, 1_28]       ,			a=[1, 4, 8, 16]       ,			a=[1, 2, 4, 8]       ,			a=True       ,			a=True       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=0.02       ,			a=3       ,			a=None       ,			)       ->    Union[str, Any]:
								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					)       ->    Union[str, Any]:
								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					)       ->    List[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 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    List[Any]:
								snake_case_				=							SegformerModel(config=lowerCAmelCase__					)
								model.to(lowerCAmelCase__					)
								model.eval()
								snake_case_				=							model(lowerCAmelCase__					)
								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       ,			a       ,			a       ,			a					)       ->    Any:
								snake_case_				=							self.num_labels
								snake_case_				=							SegformerForSemanticSegmentation(lowerCAmelCase__					)
								model.to(lowerCAmelCase__					)
								model.eval()
								snake_case_				=							model(lowerCAmelCase__					)
								self.parent.assertEqual(
								    result.logits.shape       ,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)					)
								snake_case_				=							model(lowerCAmelCase__       ,			labels=lowerCAmelCase__					)
								self.parent.assertEqual(
								    result.logits.shape       ,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)					)
								self.parent.assertGreater(result.loss       ,			0.0					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    str:
								snake_case_				=							1
								snake_case_				=							SegformerForSemanticSegmentation(config=lowerCAmelCase__					)
								model.to(lowerCAmelCase__					)
								model.eval()
								snake_case_				=							torch.randint(0       ,			1       ,			(self.batch_size, self.image_size, self.image_size)					).to(lowerCAmelCase__					)
								snake_case_				=							model(lowerCAmelCase__       ,			labels=lowerCAmelCase__					)
								self.parent.assertGreater(result.loss       ,			0.0					)






	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_				=							config_and_inputs
								snake_case_				=							{"pixel_values": pixel_values}
								return config, inputs_dict






@require_torch
class  UpperCamelCase_      (			UpperCamelCase__       ,					UpperCamelCase__       ,					unittest.TestCase				):



	'''simple docstring'''




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

	lowerCAmelCase													= True
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							SegformerModelTester(self					)
								snake_case_				=							SegformerConfigTester(self       ,			config_class=lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_binary_image_segmentation(*lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_segmentation(*lowerCAmelCase__					)




	@unittest.skip('SegFormer does not use inputs_embeds'					)
	def 							_UpperCamelCase			(       self					)       ->    int:
								pass




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




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															snake_case_				=							model_class(lowerCAmelCase__					)
															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]       ,			lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								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(lowerCAmelCase__					)
															model.to(lowerCAmelCase__					)
															model.eval()
															with torch.no_grad():
																						snake_case_				=							model(**self._prepare_for_class(lowerCAmelCase__       ,			lowerCAmelCase__					)					)
															snake_case_				=							outputs.attentions

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

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

															self.assertEqual(len(lowerCAmelCase__					)       ,			lowerCAmelCase__					)

															# 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(lowerCAmelCase__					)

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

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

															snake_case_				=							outputs.attentions

															self.assertEqual(len(lowerCAmelCase__					)       ,			lowerCAmelCase__					)
															# 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					)       ->    Union[str, Any]:




								def check_hidden_states_output(a       ,			a       ,			a					):
															snake_case_				=							model_class(lowerCAmelCase__					)
															model.to(lowerCAmelCase__					)
															model.eval()

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

															snake_case_				=							outputs.hidden_states

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

															# 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_				=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

															check_hidden_states_output(lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__					)




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




								if not self.model_tester.is_training:
															return

								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(lowerCAmelCase__					):
																						continue

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




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






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




								for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_				=							SegformerModel.from_pretrained(lowerCAmelCase__					)
															self.assertIsNotNone(lowerCAmelCase__					)






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




@require_torch
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							SegformerImageProcessor(
								    image_scale=(5_12, 5_12)       ,			keep_ratio=lowerCAmelCase__       ,			align=lowerCAmelCase__       ,			do_random_crop=lowerCAmelCase__					)
								snake_case_				=							SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'					).to(
								    lowerCAmelCase__					)

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

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

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

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




	@slow
	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							SegformerImageProcessor(
								    image_scale=(5_12, 5_12)       ,			keep_ratio=lowerCAmelCase__       ,			align=lowerCAmelCase__       ,			do_random_crop=lowerCAmelCase__					)
								snake_case_				=							SegformerForSemanticSegmentation.from_pretrained(
								    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'					).to(lowerCAmelCase__					)

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

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

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

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






	@slow
	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							SegformerImageProcessor(
								    image_scale=(5_12, 5_12)       ,			keep_ratio=lowerCAmelCase__       ,			align=lowerCAmelCase__       ,			do_random_crop=lowerCAmelCase__					)
								snake_case_				=							SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'					).to(
								    lowerCAmelCase__					)

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

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

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

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

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












import numpy as np
def      _A       (		SCREAMING_SNAKE_CASE     :   np.array						):





  """simple docstring"""



  return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
     import doctest

     doctest.testmod()






