"""simple docstring"""







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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, GLPNConfig, GLPNForDepthEstimation, GLPNModel
						from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor



class 			_lowerCamelCase      (__lowerCamelCase  ):







				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				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 			_lowerCamelCase      :







				def __init__(			self     :							Union[str, Any]      ,       lowerCamelCase_     :							List[Any]      ,       lowerCamelCase_     :							int=1_3      ,       lowerCamelCase_     :							Optional[int]=6_4      ,       lowerCamelCase_     :							Optional[int]=3      ,       lowerCamelCase_     :							Dict=4      ,       lowerCamelCase_     :							Optional[Any]=[2, 2, 2, 2]      ,       lowerCamelCase_     :							int=[8, 4, 2, 1]      ,       lowerCamelCase_     :							List[str]=[1_6, 3_2, 6_4, 1_2_8]      ,       lowerCamelCase_     :							List[str]=[1, 4, 8, 1_6]      ,       lowerCamelCase_     :							Dict=[1, 2, 4, 8]      ,       lowerCamelCase_     :							Optional[Any]=True      ,       lowerCamelCase_     :							Any=True      ,       lowerCamelCase_     :							List[Any]="gelu"      ,       lowerCamelCase_     :							Optional[Any]=0.1      ,       lowerCamelCase_     :							str=0.1      ,       lowerCamelCase_     :							Dict=0.02      ,       lowerCamelCase_     :							List[Any]=1_6      ,       lowerCamelCase_     :							List[str]=3      ,       lowerCamelCase_     :							str=None      ,       ):

					"""simple docstring"""






					_lowercase			:     Optional[Any]      					=				parent
					_lowercase			:     Any      					=				batch_size
					_lowercase			:     Dict      					=				image_size
					_lowercase			:     Tuple      					=				num_channels
					_lowercase			:     str      					=				num_encoder_blocks
					_lowercase			:     List[Any]      					=				sr_ratios
					_lowercase			:     int      					=				depths
					_lowercase			:     Tuple      					=				hidden_sizes
					_lowercase			:     Any      					=				downsampling_rates
					_lowercase			:     Union[str, Any]      					=				num_attention_heads
					_lowercase			:     Optional[int]      					=				is_training
					_lowercase			:     List[str]      					=				use_labels
					_lowercase			:     List[Any]      					=				hidden_act
					_lowercase			:     Optional[int]      					=				hidden_dropout_prob
					_lowercase			:     List[Any]      					=				attention_probs_dropout_prob
					_lowercase			:     Dict      					=				initializer_range
					_lowercase			:     Dict      					=				decoder_hidden_size
					_lowercase			:     Tuple      					=				num_labels
					_lowercase			:     Optional[Any]      					=				scope







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     Tuple      					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

					_lowercase			:     Tuple      					=				self.get_config()
					return config, pixel_values, labels







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					return GLPNConfig(
					    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      ,       decoder_hidden_size=self.decoder_hidden_size      ,       )







				def 				__UpperCAmelCase       (			self     :							Any      ,       lowerCamelCase_     :							Dict      ,       lowerCamelCase_     :							Optional[int]      ,       lowerCamelCase_     :							Tuple    ):

					"""simple docstring"""






					_lowercase			:     int      					=				GLPNModel(config=lowerCamelCase_    )
					model.to(lowerCamelCase_    )
					model.eval()
					_lowercase			:     Tuple      					=				model(lowerCamelCase_    )
					_lowercase			:     List[str]      					=				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     :							Union[str, Any]      ,       lowerCamelCase_     :							Optional[int]      ,       lowerCamelCase_     :							int      ,       lowerCamelCase_     :							int    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				self.num_labels
					_lowercase			:     List[Any]      					=				GLPNForDepthEstimation(lowerCamelCase_    )
					model.to(lowerCamelCase_    )
					model.eval()
					_lowercase			:     Dict      					=				model(lowerCamelCase_    )
					self.parent.assertEqual(result.predicted_depth.shape      ,       (self.batch_size, self.image_size, self.image_size)    )
					_lowercase			:     int      					=				model(lowerCamelCase_      ,       labels=lowerCamelCase_    )
					self.parent.assertEqual(result.predicted_depth.shape      ,       (self.batch_size, self.image_size, self.image_size)    )







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				self.prepare_config_and_inputs()
					_lowercase					,   _lowercase					,   _lowercase			:     Any      					=				config_and_inputs
					_lowercase			:     Optional[int]      					=				{'pixel_values': pixel_values}
					return config, inputs_dict



@require_torch
class 			_lowerCamelCase      (__lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase  ):
				_snake_case  =	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
				_snake_case  =	(
				    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
				)

				_snake_case  =	False
				_snake_case  =	False
				_snake_case  =	False







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				GLPNModelTester(self    )
					_lowercase			:     Optional[int]      					=				GLPNConfigTester(self      ,       config_class=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					self.config_tester.run_common_tests()







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     str      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_    )







				@unittest.skip('GLPN does not use inputs_embeds'    )
				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					pass







				@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'    )
				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					pass







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					_lowercase					,   _lowercase			:     Any      					=				self.model_tester.prepare_config_and_inputs_for_common()

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

						_lowercase			:     str      					=				['pixel_values']
						self.assertListEqual(arg_names[:1]      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase					,   _lowercase			:     List[Any]      					=				self.model_tester.prepare_config_and_inputs_for_common()
					_lowercase			:     Union[str, Any]      					=				True

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

						_lowercase			:     List[Any]      					=				sum(self.model_tester.depths    )
						self.assertEqual(len(lowerCamelCase_    )      ,       lowerCamelCase_    )

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

						self.assertEqual(len(lowerCamelCase_    )      ,       lowerCamelCase_    )

						# verify the first attentions (first block, first layer)
						_lowercase			:     Union[str, Any]      					=				(self.model_tester.image_size // 4) ** 2
						_lowercase			:     List[str]      					=				(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			:     int      					=				(self.model_tester.image_size // 3_2) ** 2
						_lowercase			:     Dict      					=				(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			:     Tuple      					=				len(lowerCamelCase_    )

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

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

						_lowercase			:     Any      					=				outputs.attentions

						self.assertEqual(len(lowerCamelCase_    )      ,       lowerCamelCase_    )
						# verify the first attentions (first block, first layer)
						_lowercase			:     str      					=				(self.model_tester.image_size // 4) ** 2
						_lowercase			:     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 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






					def check_hidden_states_output(lowerCamelCase_     :							List[str]      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							str    ):
						_lowercase			:     int      					=				model_class(lowerCamelCase_    )
						model.to(lowerCamelCase_    )
						model.eval()

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

						_lowercase			:     Optional[int]      					=				outputs.hidden_states

						_lowercase			:     Union[str, Any]      					=				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,
						    ]      ,       )

					_lowercase					,   _lowercase			:     Tuple      					=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase			:     Union[str, Any]      					=				True
						check_hidden_states_output(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    )

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

						check_hidden_states_output(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					if not self.model_tester.is_training:
						return

					_lowercase					,   _lowercase			:     List[Any]      					=				self.model_tester.prepare_config_and_inputs_for_common()
					_lowercase			:     Any      					=				True

					for model_class in self.all_model_classes:
						if model_class in get_values(lowerCamelCase_    ):
							continue
						# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
						# this can then be incorporated into _prepare_for_class in test_modeling_common.py
						if model_class.__name__ == "GLPNForDepthEstimation":
							_lowercase					,   _lowercase					,   _lowercase					,   _lowercase			:     Optional[Any]      					=				inputs_dict['pixel_values'].shape
							_lowercase			:     int      					=				torch.zeros(
							    [self.model_tester.batch_size, height, width]      ,       device=lowerCamelCase_    ).long()
						_lowercase			:     Union[str, Any]      					=				model_class(lowerCamelCase_    )
						model.to(lowerCamelCase_    )
						model.train()
						_lowercase			:     Union[str, Any]      					=				self._prepare_for_class(lowerCamelCase_      ,       lowerCamelCase_      ,       return_labels=lowerCamelCase_    )
						_lowercase			:     Tuple      					=				model(**lowerCamelCase_    ).loss
						loss.backward()







				@slow
				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowercase			:     List[Any]      					=				GLPNModel.from_pretrained(lowerCamelCase_    )
						self.assertIsNotNone(lowerCamelCase_    )



def   __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase			:     Optional[Any]      					=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
	return image







@require_torch
@require_vision
@slow
class 			_lowerCamelCase      (unittest.TestCase  ):







				@slow
				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     Optional[int]      					=				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
					_lowercase			:     List[str]      					=				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowerCamelCase_    )

					_lowercase			:     Tuple      					=				prepare_img()
					_lowercase			:     str      					=				image_processor(images=lowerCamelCase_      ,       return_tensors='pt'    ).to(lowerCamelCase_    )

					# forward pass
					with torch.no_grad():
						_lowercase			:     Any      					=				model(**lowerCamelCase_    )

					# verify the predicted depth
					_lowercase			:     Union[str, Any]      					=				torch.Size([1, 4_8_0, 6_4_0]    )
					self.assertEqual(outputs.predicted_depth.shape      ,       lowerCamelCase_    )

					_lowercase			:     List[Any]      					=				torch.tensor(
					    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]    ).to(lowerCamelCase_    )

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




"""simple docstring"""







from __future__ import annotations

import re


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	def alphanum_key(__UpperCAmelCase      ):
		return [int(__UpperCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'  ,__UpperCAmelCase      )]

	return sorted(__UpperCAmelCase  ,key=__UpperCAmelCase      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



