
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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	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       ,							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       ,							)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	parent
								_UpperCamelCase						    =	batch_size
								_UpperCamelCase						    =	image_size
								_UpperCamelCase						    =	num_channels
								_UpperCamelCase						    =	num_encoder_blocks
								_UpperCamelCase						    =	sr_ratios
								_UpperCamelCase						    =	depths
								_UpperCamelCase						    =	hidden_sizes
								_UpperCamelCase						    =	downsampling_rates
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	is_training
								_UpperCamelCase						    =	use_labels
								_UpperCamelCase						    =	hidden_act
								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	num_labels
								_UpperCamelCase						    =	scope

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


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

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

						def  A_				(			self				)					->      Dict:


								'''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  A_				(			self       ,							a       ,							a       ,							a				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	SegformerModel(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a				)
								_UpperCamelCase						    =	_UpperCamelCase						    =	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  A_				(			self       ,							a       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.num_labels
								_UpperCamelCase						    =	SegformerForSemanticSegmentation(a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	model(a				)
								self.parent.assertEqual(
								    result.logits.shape       ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
								_UpperCamelCase						    =	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  A_				(			self       ,							a       ,							a       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	1
								_UpperCamelCase						    =	SegformerForSemanticSegmentation(config=a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	torch.randint(0       ,							1       ,							(self.batch_size, self.image_size, self.image_size)				).to(a				)
								_UpperCamelCase						    =	model(a       ,							labels=a				)
								self.parent.assertGreater(result.loss       ,							0.0				)







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


								'''simple docstring'''

								_UpperCamelCase						    =	self.prepare_config_and_inputs()
								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	config_and_inputs
								_UpperCamelCase						    =	{"""pixel_values""": pixel_values}
								return config, inputs_dict







@require_torch
class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[str]	=		(
						    (
						        SegformerModel,
						        SegformerForSemanticSegmentation,
						        SegformerForImageClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_							: Optional[int]	=		(
						    {
						        "feature-extraction": SegformerModel,
						        "image-classification": SegformerForImageClassification,
						        "image-segmentation": SegformerForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase_							: List[Any]	=		True
						UpperCamelCase_							: Optional[Any]	=		False
						UpperCamelCase_							: List[str]	=		False
						UpperCamelCase_							: Optional[int]	=		False

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


								'''simple docstring'''

								_UpperCamelCase						    =	SegformerModelTester(self				)
								_UpperCamelCase						    =	SegformerConfigTester(self       ,							config_class=a				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								self.config_tester.run_common_tests()

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_binary_image_segmentation(*a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	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  A_				(			self				)					->      int:


								'''simple docstring'''

								pass

						@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""				)
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								pass

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

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

										_UpperCamelCase						    =	["""pixel_values"""]
										self.assertListEqual(arg_names[:1]       ,							a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

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

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

										_UpperCamelCase						    =	sum(self.model_tester.depths				)
										self.assertEqual(len(a				)       ,							a				)

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

										self.assertEqual(len(a				)       ,							a				)

										# verify the first attentions (first block, first layer)
										_UpperCamelCase						    =	(self.model_tester.image_size // 4) ** 2
										_UpperCamelCase						    =	(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)
										_UpperCamelCase						    =	(self.model_tester.image_size // 32) ** 2
										_UpperCamelCase						    =	(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]       ,							)
										_UpperCamelCase						    =	len(a				)

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

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

										_UpperCamelCase						    =	outputs.attentions

										self.assertEqual(len(a				)       ,							a				)
										# verify the first attentions (first block, first layer)
										_UpperCamelCase						    =	(self.model_tester.image_size // 4) ** 2
										_UpperCamelCase						    =	(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  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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

										with torch.no_grad():
												_UpperCamelCase						    =	model(**self._prepare_for_class(a       ,							a				)				)

										_UpperCamelCase						    =	outputs.hidden_states

										_UpperCamelCase						    =	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,
										    ]       ,							)

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										_UpperCamelCase						    =	True
										check_hidden_states_output(a       ,							a       ,							a				)

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

										check_hidden_states_output(a       ,							a       ,							a				)

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


								'''simple docstring'''

								if not self.model_tester.is_training:
										return

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

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

										_UpperCamelCase						    =	model_class(a				)
										model.to(a				)
										model.train()
										_UpperCamelCase						    =	self._prepare_for_class(a       ,							a       ,							return_labels=a				)
										_UpperCamelCase						    =	model(**a				).loss
										loss.backward()

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


								'''simple docstring'''

								pass







						@slow
						def  A_				(			self				)					->      str:


								'''simple docstring'''

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




def 							__A()		->				Optional[Any]:

		"""simple docstring"""






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







@require_torch
class        lowerCAmelCase__      (   unittest.TestCase	):

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


								'''simple docstring'''

								_UpperCamelCase						    =	SegformerImageProcessor(
								    image_scale=(5_12, 5_12)       ,							keep_ratio=a       ,							align=a       ,							do_random_crop=a				)
								_UpperCamelCase						    =	SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""				).to(
								    a				)

								_UpperCamelCase						    =	prepare_img()
								_UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				)
								_UpperCamelCase						    =	encoded_inputs.pixel_values.to(a				)

								with torch.no_grad():
										_UpperCamelCase						    =	model(a				)

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

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

						@slow
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	SegformerImageProcessor(
								    image_scale=(5_12, 5_12)       ,							keep_ratio=a       ,							align=a       ,							do_random_crop=a				)
								_UpperCamelCase						    =	SegformerForSemanticSegmentation.from_pretrained(
								    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""				).to(a				)

								_UpperCamelCase						    =	prepare_img()
								_UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				)
								_UpperCamelCase						    =	encoded_inputs.pixel_values.to(a				)

								with torch.no_grad():
										_UpperCamelCase						    =	model(a				)

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

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







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


								'''simple docstring'''

								_UpperCamelCase						    =	SegformerImageProcessor(
								    image_scale=(5_12, 5_12)       ,							keep_ratio=a       ,							align=a       ,							do_random_crop=a				)
								_UpperCamelCase						    =	SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""				).to(
								    a				)

								_UpperCamelCase						    =	prepare_img()
								_UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				)
								_UpperCamelCase						    =	encoded_inputs.pixel_values.to(a				)

								with torch.no_grad():
										_UpperCamelCase						    =	model(a				)

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

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

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





from math import isclose, sqrt


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float, float]:

		"""simple docstring"""






		_UpperCamelCase						    =	point_y / 4 / point_x
		_UpperCamelCase						    =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
		_UpperCamelCase						    =	(1 - normal_gradient * normal_gradient) / (
		    1 + normal_gradient * normal_gradient
		)
		_UpperCamelCase						    =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

		# to find the next point, solve the simultaeneous equations:
		# y^2 + 4x^2 = 100
		# y - b = m * (x - a)
		# ==> A x^2 + B x + C = 0
		_UpperCamelCase						    =	outgoing_gradient**2 + 4
		_UpperCamelCase						    =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
		_UpperCamelCase						    =	(point_y - outgoing_gradient * point_x) ** 2 - 1_0_0

		_UpperCamelCase						    =	(
		    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)
		_UpperCamelCase						    =	(
		    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)

		# two solutions, one of which is our input point
		_UpperCamelCase						    =	x_minus if isclose(lowerCAmelCase						,  lowerCAmelCase ) else x_plus
		_UpperCamelCase						    =	point_y + outgoing_gradient * (next_x - point_x)

		return next_x, next_y, outgoing_gradient


def 							__A(lowerCAmelCase = 1.4						,  lowerCAmelCase = -9.6 )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	first_x_coord
		_UpperCamelCase						    =	first_y_coord
		_UpperCamelCase						    =	(10.1 - point_y) / (0.0 - point_x)

		while not (-0.01 <= point_x <= 0.01 and point_y > 0):
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	next_point(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
				num_reflections += 1

		return num_reflections


if __name__ == "__main__":
		print(F"""{solution() = }""")



