





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 SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							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 SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=64						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=[2, 2, 2, 2]						,	_lowerCAmelCase=[8, 4, 2, 1]						,	_lowerCAmelCase=[16, 32, 64, 128]						,	_lowerCAmelCase=[1, 4, 8, 16]						,	_lowerCAmelCase=[1, 2, 4, 8]						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase="gelu"						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=16						,	_lowerCAmelCase=3						,	_lowerCAmelCase=None						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							image_size
										lowerCamelCase__      =							num_channels
										lowerCamelCase__      =							num_encoder_blocks
										lowerCamelCase__      =							sr_ratios
										lowerCamelCase__      =							depths
										lowerCamelCase__      =							hidden_sizes
										lowerCamelCase__      =							downsampling_rates
										lowerCamelCase__      =							num_attention_heads
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							hidden_dropout_prob
										lowerCamelCase__      =							attention_probs_dropout_prob
										lowerCamelCase__      =							initializer_range
										lowerCamelCase__      =							decoder_hidden_size
										lowerCamelCase__      =							num_labels
										lowerCamelCase__      =							scope





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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





					def        __magic_name__			(			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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							GLPNModel(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										lowerCamelCase__      =							lowerCamelCase__      =							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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							GLPNForDepthEstimation(_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.predicted_depth.shape						,	(self.batch_size, self.image_size, self.image_size)						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	labels=_lowerCAmelCase						)
										self.parent.assertEqual(result.predicted_depth.shape						,	(self.batch_size, self.image_size, self.image_size)						)






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

@require_torch
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
					A__								=     (
					    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
					)

					A__								=     False
					A__								=     False
					A__								=     False





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							GLPNModelTester(self						)
										lowerCamelCase__      =							GLPNConfigTester(self						,	config_class=_lowerCAmelCase						)





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										pass





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





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCamelCase__      =							["pixel_values"]
															self.assertListEqual(arg_names[:1]						,	_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__      =							True

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

															lowerCamelCase__      =							sum(self.model_tester.depths						)
															self.assertEqual(len(_lowerCAmelCase						)						,	_lowerCAmelCase						)

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

															self.assertEqual(len(_lowerCAmelCase						)						,	_lowerCAmelCase						)

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

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

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

															lowerCamelCase__      =							outputs.attentions

															self.assertEqual(len(_lowerCAmelCase						)						,	_lowerCAmelCase						)
															# verify the first attentions (first block, first layer)
															lowerCamelCase__      =							(self.model_tester.image_size // 4) ** 2
															lowerCamelCase__      =							(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        __magic_name__			(			self						):
										def check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															model.to(_lowerCAmelCase						)
															model.eval()

															with torch.no_grad():
																				lowerCamelCase__      =							model(**self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						)						)

															lowerCamelCase__      =							outputs.hidden_states

															lowerCamelCase__      =							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,
															    ]						,	)

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__      =							True
															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

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

															check_hidden_states_output(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)





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

										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__      =							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":
																				lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__      =							inputs_dict["pixel_values"].shape
																				lowerCamelCase__      =							torch.zeros(
																				    [self.model_tester.batch_size, height, width]						,	device=_lowerCAmelCase						).long()
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															model.to(_lowerCAmelCase						)
															model.train()
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							model(**_lowerCAmelCase						).loss
															loss.backward()






					@slow
					def        __magic_name__			(			self						):
										for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCamelCase__      =							GLPNModel.from_pretrained(_lowerCAmelCase						)
															self.assertIsNotNone(_lowerCAmelCase						)





def  __UpperCamelCase   (     )			->Optional[Any]:
					lowerCamelCase__      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image







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





					"""simple docstring"""


					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
										lowerCamelCase__      =							GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_lowerCAmelCase						)

										lowerCamelCase__      =							prepare_img()
										lowerCamelCase__      =							image_processor(images=_lowerCAmelCase						,	return_tensors="pt"						).to(_lowerCAmelCase						)

										# forward pass
										with torch.no_grad():
															lowerCamelCase__      =							model(**_lowerCAmelCase						)

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

										lowerCamelCase__      =							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(_lowerCAmelCase						)

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








from typing import Any

import numpy as np





def  __UpperCamelCase   (     a)			->bool:
					assert np.shape(a)[0] == np.shape(a)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(a,  matrix.T) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					lowerCamelCase__     ,		lowerCamelCase__      =							np.linalg.eigh(a)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0))





def  __UpperCamelCase   (     a)			->Any:
					lowerCamelCase__      =							np.random.randn(a,  a)
					lowerCamelCase__      =							np.dot(a,  random_matrix.T)
					assert _is_matrix_spd(a)
					return spd_matrix





def  __UpperCamelCase   (     a,  a,  a = 1000,  a = 1E-8,  )			->Any:
					assert np.shape(a)[0] == np.shape(a)[1]
					assert np.shape(a)[0] == np.shape(a)[0]
					assert _is_matrix_spd(a)

					# Initialize solution guess, residual, search direction.
					lowerCamelCase__      =							np.zeros((np.shape(a)[0], 1))
					lowerCamelCase__      =							np.copy(a)
					lowerCamelCase__      =							np.copy(a)

					# Set initial errors in solution guess and residual.
					lowerCamelCase__      =							1E9
					lowerCamelCase__      =							1E9
					lowerCamelCase__      =							1E9

					# Set iteration counter to threshold number of iterations.
					lowerCamelCase__      =							0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										lowerCamelCase__      =							np.dot(a,  a)

										# The main algorithm.

										# Update search direction magnitude.
										lowerCamelCase__      =							np.dot(ra.T,  a) / np.dot(pa.T,  a)
										# Update solution guess.
										lowerCamelCase__      =							xa + alpha * pa
										# Calculate new residual.
										lowerCamelCase__      =							ra - alpha * w
										# Calculate new Krylov subspace scale.
										lowerCamelCase__      =							np.dot(r.T,  a) / np.dot(ra.T,  a)
										# Calculate new A conjuage search direction.
										lowerCamelCase__      =							r + beta * pa

										# Calculate errors.
										lowerCamelCase__      =							np.linalg.norm(r - ra)
										lowerCamelCase__      =							np.linalg.norm(x - xa)
										lowerCamelCase__      =							np.maximum(a,  a)

										# Update variables.
										lowerCamelCase__      =							np.copy(a)
										lowerCamelCase__      =							np.copy(a)
										lowerCamelCase__      =							np.copy(a)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x





def  __UpperCamelCase   (     )			->None:
					lowerCamelCase__      =							3
					lowerCamelCase__      =							_create_spd_matrix(a)
					lowerCamelCase__      =							np.random.randn(a,  1)
					lowerCamelCase__      =							np.dot(a,  a)

					# Numpy solution.
					lowerCamelCase__      =							np.linalg.solve(a,  a)

					# Our implementation.
					lowerCamelCase__      =							conjugate_gradient(a,  a)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true) <= 1E-6
					assert np.linalg.norm(x_conjugate_gradient - x_true) <= 1E-6


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						test_conjugate_gradient()

