




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__							(_lowerCamelCase       ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Tuple:
							a   =      self.config_class(**self.inputs_dict							)
							self.parent.assertTrue(hasattr(__UpperCamelCase ,				"hidden_sizes"							)							)
							self.parent.assertTrue(hasattr(__UpperCamelCase ,				"num_attention_heads"							)							)
							self.parent.assertTrue(hasattr(__UpperCamelCase ,				"num_encoder_blocks"							)							)




class    snake_case__							:
				"""simple docstring"""



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





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

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

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





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					str:
							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    :					Optional[int] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					str							) ->					Union[str, Any]:
							a   =      GLPNModel(config=__UpperCamelCase							)
							model.to(__UpperCamelCase							)
							model.eval()
							a   =      model(__UpperCamelCase							)
							a   =      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    :					Tuple ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Union[str, Any]							) ->					Union[str, Any]:
							a   =      self.num_labels
							a   =      GLPNForDepthEstimation(__UpperCamelCase							)
							model.to(__UpperCamelCase							)
							model.eval()
							a   =      model(__UpperCamelCase							)
							self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)							)
							a   =      model(__UpperCamelCase ,				labels=__UpperCamelCase							)
							self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)							)





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Optional[int]:
							a   =      self.prepare_config_and_inputs()
							a   =      config_and_inputs
							a   =      {'pixel_values': pixel_values}
							return config, inputs_dict




@require_torch
class    snake_case__							(_lowerCamelCase   ,	_lowerCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Any										= (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= (
				    {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
				)

				SCREAMING_SNAKE_CASE_	:							Union[str, Any]										= False
				SCREAMING_SNAKE_CASE_	:							int										= False
				SCREAMING_SNAKE_CASE_	:							List[Any]										= False





				def 							__UpperCAmelCase							(				self    :					Any							) ->					str:
							a   =      GLPNModelTester(self							)
							a   =      GLPNConfigTester(self ,				config_class=__UpperCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					List[str]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Optional[int]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Tuple:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase							)





				@unittest.skip("GLPN does not use inputs_embeds"							)
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Any:
							pass





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





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Union[str, Any]:
							a   =      self.model_tester.prepare_config_and_inputs_for_common()

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

										a   =      ['pixel_values']
										self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Any:
							a   =      self.model_tester.prepare_config_and_inputs_for_common()
							a   =      True

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

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

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

										self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

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

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

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

										a   =      outputs.attentions

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







							def check_hidden_states_output(__lowerCamelCase    :					str ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Union[str, Any]							):
										a   =      model_class(__UpperCamelCase							)
										model.to(__UpperCamelCase							)
										model.eval()

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

										a   =      outputs.hidden_states

										a   =      self.model_tester.num_encoder_blocks
										self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

										# 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,
										    ] ,				)

							a   =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

										check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)





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







							if not self.model_tester.is_training:
										return

							a   =      self.model_tester.prepare_config_and_inputs_for_common()
							a   =      True

							for model_class in self.all_model_classes:
										if model_class in get_values(__UpperCamelCase							):
													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":
													a   =      inputs_dict['pixel_values'].shape
													a   =      torch.zeros(
													    [self.model_tester.batch_size, height, width] ,				device=__UpperCamelCase							).long()
										a   =      model_class(__UpperCamelCase							)
										model.to(__UpperCamelCase							)
										model.train()
										a   =      self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)
										a   =      model(**__UpperCamelCase							).loss
										loss.backward()





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







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




def       __magic_name__    (			):






			'''simple docstring'''







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






@require_torch
@require_vision
@slow
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					str:
							a   =      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
							a   =      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__UpperCamelCase							)

							a   =      prepare_img()
							a   =      image_processor(images=__UpperCamelCase ,				return_tensors="pt"							).to(__UpperCamelCase							)

							# forward pass
							with torch.no_grad():
										a   =      model(**__UpperCamelCase							)

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

							a   =      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(__UpperCamelCase							)

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












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






