

'''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  _snake_case						(__SCREAMING_SNAKE_CASE):






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Tuple      =   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  _snake_case						:






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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

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






			def 						UpperCamelCase__				(   self			):
				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       ,_snake_case       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	int      =   GLPNModel(config=_snake_case			)
				model.to(_snake_case			)
				model.eval()
				UpperCAmelCase_			:	List[Any]      =   model(_snake_case			)
				UpperCAmelCase_			:	Dict      =   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       ,_snake_case       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	Dict      =   self.num_labels
				UpperCAmelCase_			:	Optional[int]      =   GLPNForDepthEstimation(_snake_case			)
				model.to(_snake_case			)
				model.eval()
				UpperCAmelCase_			:	Dict      =   model(_snake_case			)
				self.parent.assertEqual(result.predicted_depth.shape       ,(self.batch_size, self.image_size, self.image_size)			)
				UpperCAmelCase_			:	int      =   model(_snake_case       ,labels=_snake_case			)
				self.parent.assertEqual(result.predicted_depth.shape       ,(self.batch_size, self.image_size, self.image_size)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[int]      =   self.prepare_config_and_inputs()
				UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_			:	Dict      =   config_and_inputs
				UpperCAmelCase_			:	Any      =   {"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     Tuple          =(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
			__A   :     List[str]          =(
			    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
			)

			__A   :     Tuple          =False
			__A   :     List[str]          =False
			__A   :     int          =False






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[Any]      =   GLPNModelTester(self			)
				UpperCAmelCase_			:	Optional[int]      =   GLPNConfigTester(self       ,config_class=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				self.config_tester.run_common_tests()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Any      =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	List[Any]      =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*_snake_case			)






			@unittest.skip("GLPN does not use inputs_embeds"			)
			def 						UpperCamelCase__				(   self			):
				pass






			@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"			)
			def 						UpperCamelCase__				(   self			):
				pass






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	List[Any]      =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_			:	Optional[int]      =   model_class(_snake_case			)
					UpperCAmelCase_			:	str      =   inspect.signature(model.forward			)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					UpperCAmelCase_			:	Union[str, Any]      =   [*signature.parameters.keys()]

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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	List[Any]      =   self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_			:	List[str]      =   True

				for model_class in self.all_model_classes:
					UpperCAmelCase_			:	Tuple      =   True
					UpperCAmelCase_			:	Any      =   False
					UpperCAmelCase_			:	Any      =   True
					UpperCAmelCase_			:	Optional[Any]      =   model_class(_snake_case			)
					model.to(_snake_case			)
					model.eval()
					with torch.no_grad():
						UpperCAmelCase_			:	Optional[Any]      =   model(**self._prepare_for_class(_snake_case       ,_snake_case			)			)
					UpperCAmelCase_			:	Optional[Any]      =   outputs.attentions

					UpperCAmelCase_			:	Optional[int]      =   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"]
					UpperCAmelCase_			:	Tuple      =   True
					UpperCAmelCase_			:	Dict      =   model_class(_snake_case			)
					model.to(_snake_case			)
					model.eval()
					with torch.no_grad():
						UpperCAmelCase_			:	Optional[int]      =   model(**self._prepare_for_class(_snake_case       ,_snake_case			)			)
					UpperCAmelCase_			:	Any      =   outputs.attentions

					self.assertEqual(len(_snake_case			)       ,_snake_case			)

					# verify the first attentions (first block, first layer)
					UpperCAmelCase_			:	List[str]      =   (self.model_tester.image_size // 4) ** 2
					UpperCAmelCase_			:	int      =   (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_			:	Optional[Any]      =   (self.model_tester.image_size // 32) ** 2
					UpperCAmelCase_			:	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]       ,)
					UpperCAmelCase_			:	Optional[Any]      =   len(_snake_case			)

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

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

					UpperCAmelCase_			:	Optional[Any]      =   outputs.attentions

					self.assertEqual(len(_snake_case			)       ,_snake_case			)
					# verify the first attentions (first block, first layer)
					UpperCAmelCase_			:	Optional[Any]      =   (self.model_tester.image_size // 4) ** 2
					UpperCAmelCase_			:	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 						UpperCamelCase__				(   self			):
				def check_hidden_states_output(_snake_case       ,_snake_case       ,_snake_case			):
					UpperCAmelCase_			:	Union[str, Any]      =   model_class(_snake_case			)
					model.to(_snake_case			)
					model.eval()

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

					UpperCAmelCase_			:	Any      =   outputs.hidden_states

					UpperCAmelCase_			:	Any      =   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,
					    ]       ,)

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

				for model_class in self.all_model_classes:
					UpperCAmelCase_			:	List[Any]      =   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"]
					UpperCAmelCase_			:	Optional[int]      =   True

					check_hidden_states_output(_snake_case       ,_snake_case       ,_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if not self.model_tester.is_training:
					return

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

				for model_class in self.all_model_classes:
					if model_class in get_values(_snake_case			):
						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":
						UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   inputs_dict["pixel_values"].shape
						UpperCAmelCase_			:	Optional[Any]      =   torch.zeros(
						    [self.model_tester.batch_size, height, width]       ,device=_snake_case			).long()
					UpperCAmelCase_			:	List[str]      =   model_class(_snake_case			)
					model.to(_snake_case			)
					model.train()
					UpperCAmelCase_			:	List[Any]      =   self._prepare_for_class(_snake_case       ,_snake_case       ,return_labels=_snake_case			)
					UpperCAmelCase_			:	Optional[int]      =   model(**_snake_case			).loss
					loss.backward()






			@slow
			def 						UpperCamelCase__				(   self			):
				for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_			:	Optional[int]      =   GLPNModel.from_pretrained(_snake_case			)
					self.assertIsNotNone(_snake_case			)




def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image





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






			@slow
			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	int      =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
				UpperCAmelCase_			:	int      =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(_snake_case			)

				UpperCAmelCase_			:	List[str]      =   prepare_img()
				UpperCAmelCase_			:	List[Any]      =   image_processor(images=_snake_case       ,return_tensors="pt"			).to(_snake_case			)

				# forward pass
				with torch.no_grad():
					UpperCAmelCase_			:	List[Any]      =   model(**_snake_case			)

				# verify the predicted depth
				UpperCAmelCase_			:	int      =   torch.Size([1, 4_80, 6_40]			)
				self.assertEqual(outputs.predicted_depth.shape       ,_snake_case			)

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

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







'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





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

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




