



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 				a_    (						SCREAMING_SNAKE_CASE__							):

						def       __a			(       self      :Union[str, Any])						->   List[str]:
												UpperCAmelCase_					=	self.config_class(**self.inputs_dict)
												self.parent.assertTrue(hasattr(_lowercase  ,		'''hidden_sizes'''))
												self.parent.assertTrue(hasattr(_lowercase  ,		'''num_attention_heads'''))
												self.parent.assertTrue(hasattr(_lowercase  ,		'''num_encoder_blocks'''))







class 				a_    :

						def __init__(       self      :Optional[int]  ,		_lowercase      :int  ,		_lowercase      :List[Any]=13  ,		_lowercase      :List[Any]=64  ,		_lowercase      :Any=3  ,		_lowercase      :Union[str, Any]=4  ,		_lowercase      :Optional[int]=[2, 2, 2, 2]  ,		_lowercase      :Any=[8, 4, 2, 1]  ,		_lowercase      :Any=[16, 32, 64, 128]  ,		_lowercase      :Optional[int]=[1, 4, 8, 16]  ,		_lowercase      :Union[str, Any]=[1, 2, 4, 8]  ,		_lowercase      :List[Any]=True  ,		_lowercase      :Optional[Any]=True  ,		_lowercase      :int="gelu"  ,		_lowercase      :Optional[int]=0.1  ,		_lowercase      :Optional[Any]=0.1  ,		_lowercase      :List[Any]=0.02  ,		_lowercase      :Optional[Any]=16  ,		_lowercase      :List[str]=3  ,		_lowercase      :Dict=None  ,		)						->   Tuple:
												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_					=	decoder_hidden_size
												UpperCAmelCase_					=	num_labels
												UpperCAmelCase_					=	scope

						def       __a			(       self      :Optional[Any])						->   List[Any]:
												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      :Tuple)						->   List[Any]:
												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       __a			(       self      :Dict  ,		_lowercase      :Dict  ,		_lowercase      :Tuple  ,		_lowercase      :Tuple)						->   List[Any]:
												UpperCAmelCase_					=	GLPNModel(config=_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase)
												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      :Dict  ,		_lowercase      :Tuple  ,		_lowercase      :Tuple  ,		_lowercase      :Dict)						->   Tuple:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	GLPNForDepthEstimation(_lowercase)
												model.to(_lowercase)
												model.eval()
												UpperCAmelCase_					=	model(_lowercase)
												self.parent.assertEqual(result.predicted_depth.shape  ,		(self.batch_size, self.image_size, self.image_size))
												UpperCAmelCase_					=	model(_lowercase  ,		labels=_lowercase)
												self.parent.assertEqual(result.predicted_depth.shape  ,		(self.batch_size, self.image_size, self.image_size))




						def       __a			(       self      :int)						->   str:
												UpperCAmelCase_					=	self.prepare_config_and_inputs()
												UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	config_and_inputs
												UpperCAmelCase_					=	{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class 				a_    (						SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					unittest.TestCase							):
						UpperCamelCase__ :		int										=(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
						UpperCamelCase__ :		Dict										=(
						    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
						)

						UpperCamelCase__ :		int										=False
						UpperCamelCase__ :		List[Any]										=False
						UpperCamelCase__ :		Any										=False

						def       __a			(       self      :Union[str, Any])						->   List[str]:
												UpperCAmelCase_					=	GLPNModelTester(self)
												UpperCAmelCase_					=	GLPNConfigTester(self  ,		config_class=_lowercase)

						def       __a			(       self      :List[str])						->   Optional[Any]:
												self.config_tester.run_common_tests()

						def       __a			(       self      :Optional[Any])						->   Any:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_lowercase)

						def       __a			(       self      :Tuple)						->   List[Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*_lowercase)

						@unittest.skip('''GLPN does not use inputs_embeds''')
						def       __a			(       self      :Dict)						->   str:
												pass

						@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods''')
						def       __a			(       self      :int)						->   Optional[int]:
												pass

						def       __a			(       self      :Optional[int])						->   List[str]:
												UpperCAmelCase_				, UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		UpperCAmelCase_					=	model_class(_lowercase)
																		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]  ,		_lowercase)

						def       __a			(       self      :Dict)						->   Optional[int]:
												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(_lowercase)
																		model.to(_lowercase)
																		model.eval()
																		with torch.no_grad():
																								UpperCAmelCase_					=	model(**self._prepare_for_class(_lowercase  ,		_lowercase))
																		UpperCAmelCase_					=	outputs.attentions

																		UpperCAmelCase_					=	sum(self.model_tester.depths)
																		self.assertEqual(len(_lowercase)  ,		_lowercase)

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

																		self.assertEqual(len(_lowercase)  ,		_lowercase)

																		# 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(_lowercase)

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

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

																		UpperCAmelCase_					=	outputs.attentions

																		self.assertEqual(len(_lowercase)  ,		_lowercase)
																		# 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      :List[Any])						->   List[str]:







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

																		with torch.no_grad():
																								UpperCAmelCase_					=	model(**self._prepare_for_class(_lowercase  ,		_lowercase))

																		UpperCAmelCase_					=	outputs.hidden_states

																		UpperCAmelCase_					=	self.model_tester.num_encoder_blocks
																		self.assertEqual(len(_lowercase)  ,		_lowercase)

																		# 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(_lowercase  ,		_lowercase  ,		_lowercase)

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

																		check_hidden_states_output(_lowercase  ,		_lowercase  ,		_lowercase)

						def       __a			(       self      :Tuple)						->   Any:







												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(_lowercase):
																								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_					=	inputs_dict['''pixel_values'''].shape
																								UpperCAmelCase_					=	torch.zeros(
																								    [self.model_tester.batch_size, height, width]  ,		device=_lowercase).long()
																		UpperCAmelCase_					=	model_class(_lowercase)
																		model.to(_lowercase)
																		model.train()
																		UpperCAmelCase_					=	self._prepare_for_class(_lowercase  ,		_lowercase  ,		return_labels=_lowercase)
																		UpperCAmelCase_					=	model(**_lowercase).loss
																		loss.backward()




						@slow
						def       __a			(       self      :Tuple)						->   str:







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







def 							A     (						)			->			Dict:



						'''simple docstring'''






						UpperCAmelCase_					=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
						return image







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

						@slow
						def       __a			(       self      :int)						->   str:
												UpperCAmelCase_					=	GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
												UpperCAmelCase_					=	GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_lowercase)

												UpperCAmelCase_					=	prepare_img()
												UpperCAmelCase_					=	image_processor(images=_lowercase  ,		return_tensors='''pt''').to(_lowercase)

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

												# verify the predicted depth
												UpperCAmelCase_					=	torch.Size([1, 480, 640])
												self.assertEqual(outputs.predicted_depth.shape  ,		_lowercase)

												UpperCAmelCase_					=	torch.tensor(
												    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]).to(_lowercase)

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












from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
a_    :				List[str]					=			TypeVar('T', bound='Enum')



@unique
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							80
  _a								=							70
  _a								=							60
  _a								=							50
  _a								=							40
  _a								=							30
  _a								=							20
  _a								=							10



@unique
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							24
  _a								=							21
  _a								=							18
  _a								=							15
  _a								=							12
  _a								=							9
  _a								=							6
  _a								=							3
  _a								=							2
  _a								=							1
  _a								=							-1
  _a								=							-2
  _a								=							-3
  _a								=							-6
  _a								=							-9
  _a								=							-12
  _a								=							-15
  _a								=							-18
  _a								=							-21
  _a								=							-24





  @classmethod
  def 			__A      (  cls						)		->   dict:





       '''simple docstring'''







       return {unit.name: unit.value for unit in cls if unit.value > 0}







  @classmethod
  def 			__A      (  cls						)		->   dict:





       '''simple docstring'''







       return {unit.name: unit.value for unit in cls if unit.value < 0}




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     __magic_name__				=       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
     for name_prefix, value_prefix in prefixes.items():
          __magic_name__				=       value / (10**value_prefix)
          if numerical_part > 1:
               return f'{numerical_part!s} {name_prefix}'
     return str(snake_case_						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     for prefix in BinaryUnit:
          __magic_name__				=       value / (2**prefix.value)
          if numerical_part > 1:
               return f'{numerical_part!s} {prefix.name}'
     return str(snake_case_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()