





'''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 (						UpperCAmelCase_  ):




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Tuple:
						lowercase         = self.config_class(**self.inputs_dict				)
						self.parent.assertTrue(hasattr(_snake_case							,			"hidden_sizes"				)				)
						self.parent.assertTrue(hasattr(_snake_case							,			"num_attention_heads"				)				)
						self.parent.assertTrue(hasattr(_snake_case							,			"num_encoder_blocks"				)				)




class       UpperCAmelCase :




		def __init__(self      :	List[Any]							,			A__      :	Union[str, Any]							,			A__      :	Union[str, Any]=1_3							,			A__      :	Union[str, Any]=6_4							,			A__      :	int=3							,			A__      :	Dict=4							,			A__      :	Dict=[2, 2, 2, 2]							,			A__      :	Dict=[8, 4, 2, 1]							,			A__      :	Any=[1_6, 3_2, 6_4, 1_2_8]							,			A__      :	int=[1, 4, 8, 1_6]							,			A__      :	Any=[1, 2, 4, 8]							,			A__      :	Union[str, Any]=True							,			A__      :	List[Any]=True							,			A__      :	Any="gelu"							,			A__      :	str=0.1							,			A__      :	Any=0.1							,			A__      :	Optional[int]=0.0_2							,			A__      :	str=3							,			A__      :	str=None							,			)					-> Union[str, Any]:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = num_channels
						lowercase         = num_encoder_blocks
						lowercase         = sr_ratios
						lowercase         = depths
						lowercase         = hidden_sizes
						lowercase         = downsampling_rates
						lowercase         = num_attention_heads
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = initializer_range
						lowercase         = num_labels
						lowercase         = scope




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[int]:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

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




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





		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> List[str]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




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

		UpperCAmelCase					:	Any 			=		True
		UpperCAmelCase					:	List[Any] 			=		False
		UpperCAmelCase					:	Any 			=		False
		UpperCAmelCase					:	Union[str, Any] 			=		False




		def 				UpperCAmelCase__   (self      :	str				)					-> Tuple:
						lowercase         = SegformerModelTester(self				)
						lowercase         = SegformerConfigTester(self							,			config_class=_snake_case				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						self.config_tester.run_common_tests()




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_snake_case				)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[int]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_binary_image_segmentation(*_snake_case				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Dict:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_segmentation(*_snake_case				)




		@unittest.skip("SegFormer does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[str]:
						pass




		@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"				)
		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
						pass




		def 				UpperCAmelCase__   (self      :	str				)					-> Any:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			_snake_case				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						lowercase         = True

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

										lowercase         = sum(self.model_tester.depths				)
										self.assertEqual(len(_snake_case				)							,			_snake_case				)

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

										self.assertEqual(len(_snake_case				)							,			_snake_case				)

										# verify the first attentions (first block, first layer)
										lowercase         = (self.model_tester.image_size // 4) ** 2
										lowercase         = (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)
										lowercase         = (self.model_tester.image_size // 3_2) ** 2
										lowercase         = (self.model_tester.image_size // (3_2 * 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]							,			)
										lowercase         = len(_snake_case				)

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

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

										lowercase         = outputs.attentions

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


						def check_hidden_states_output(A__      :	Union[str, Any]							,			A__      :	Union[str, Any]							,			A__      :	str				):
										lowercase         = model_class(_snake_case				)
										model.to(_snake_case				)
										model.eval()

										with torch.no_grad():
														lowercase         = model(**self._prepare_for_class(_snake_case							,			_snake_case				)				)

										lowercase         = outputs.hidden_states

										lowercase         = self.model_tester.num_encoder_blocks
										self.assertEqual(len(_snake_case				)							,			_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,
										    ]							,			)

						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = True
										check_hidden_states_output(_snake_case							,			_snake_case							,			_snake_case				)

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

										check_hidden_states_output(_snake_case							,			_snake_case							,			_snake_case				)




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> List[str]:


						if not self.model_tester.is_training:
										return

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

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

										lowercase         = model_class(_snake_case				)
										model.to(_snake_case				)
										model.train()
										lowercase         = self._prepare_for_class(_snake_case							,			_snake_case							,			return_labels=_snake_case				)
										lowercase         = model(**_snake_case				).loss
										loss.backward()




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





		@slow
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Union[str, Any]:


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







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
				return image


@require_torch
class       UpperCAmelCase (						unittest.TestCase  ):




		@slow
		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[str]:
						lowercase         = SegformerImageProcessor(
						    image_scale=(5_1_2, 5_1_2)							,			keep_ratio=_snake_case							,			align=_snake_case							,			do_random_crop=_snake_case				)
						lowercase         = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"				).to(
						    _snake_case				)

						lowercase         = prepare_img()
						lowercase         = image_processor(images=_snake_case							,			return_tensors="pt"				)
						lowercase         = encoded_inputs.pixel_values.to(_snake_case				)

						with torch.no_grad():
										lowercase         = model(_snake_case				)

						lowercase         = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)				)
						self.assertEqual(outputs.logits.shape							,			_snake_case				)

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




		@slow
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> int:
						lowercase         = SegformerImageProcessor(
						    image_scale=(5_1_2, 5_1_2)							,			keep_ratio=_snake_case							,			align=_snake_case							,			do_random_crop=_snake_case				)
						lowercase         = SegformerForSemanticSegmentation.from_pretrained(
						    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"				).to(_snake_case				)

						lowercase         = prepare_img()
						lowercase         = image_processor(images=_snake_case							,			return_tensors="pt"				)
						lowercase         = encoded_inputs.pixel_values.to(_snake_case				)

						with torch.no_grad():
										lowercase         = model(_snake_case				)

						lowercase         = torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)				)
						self.assertEqual(outputs.logits.shape							,			_snake_case				)

						lowercase         = 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(_snake_case				)
						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]							,			_snake_case							,			atol=1e-1				)				)





		@slow
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict:
						lowercase         = SegformerImageProcessor(
						    image_scale=(5_1_2, 5_1_2)							,			keep_ratio=_snake_case							,			align=_snake_case							,			do_random_crop=_snake_case				)
						lowercase         = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"				).to(
						    _snake_case				)

						lowercase         = prepare_img()
						lowercase         = image_processor(images=_snake_case							,			return_tensors="pt"				)
						lowercase         = encoded_inputs.pixel_values.to(_snake_case				)

						with torch.no_grad():
										lowercase         = model(_snake_case				)

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

						lowercase         = image_processor.post_process_semantic_segmentation(outputs=_snake_case							,			target_sizes=[(5_0_0, 3_0_0)]				)
						lowercase         = torch.Size((5_0_0, 3_0_0)				)
						self.assertEqual(segmentation[0].shape							,			_snake_case				)

						lowercase         = image_processor.post_process_semantic_segmentation(outputs=_snake_case				)
						lowercase         = torch.Size((1_2_8, 1_2_8)				)
						self.assertEqual(segmentation[0].shape							,			_snake_case				)











'''simple docstring'''

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__lowerCamelCase : Any											=			open  # noqa: we just need to have a builtin inside this module to test it properly




