





'''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     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def       a	(					self   ):
								_UpperCamelCase											=   self.config_class(**self.inputs_dict   )
								self.parent.assertTrue(hasattr(A_			,     "hidden_sizes"   )   )
								self.parent.assertTrue(hasattr(A_			,     "num_attention_heads"   )   )
								self.parent.assertTrue(hasattr(A_			,     "num_encoder_blocks"   )   )




class     A_ :

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=13			,     A_=64			,     A_=3			,     A_=4			,     A_=[2, 2, 2, 2]			,     A_=[8, 4, 2, 1]			,     A_=[16, 32, 64, 1_28]			,     A_=[1, 4, 8, 16]			,     A_=[1, 2, 4, 8]			,     A_=True			,     A_=True			,     A_="gelu"			,     A_=0.1			,     A_=0.1			,     A_=0.02			,     A_=16			,     A_=3			,     A_=None			,     ):
								_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   ):
								_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   ):
								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			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   GLPNModel(config=A_   )
								model.to(A_   )
								model.eval()
								_UpperCamelCase											=   model(A_   )
								_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			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   self.num_labels
								_UpperCamelCase											=   GLPNForDepthEstimation(A_   )
								model.to(A_   )
								model.eval()
								_UpperCamelCase											=   model(A_   )
								self.parent.assertEqual(result.predicted_depth.shape			,     (self.batch_size, self.image_size, self.image_size)   )
								_UpperCamelCase											=   model(A_			,     labels=A_   )
								self.parent.assertEqual(result.predicted_depth.shape			,     (self.batch_size, self.image_size, self.image_size)   )




			def       a	(					self   ):
								_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_ (  lowerCAmelCase_    ,			lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





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

			_lowerCAmelCase						=    False
			_lowerCAmelCase						=    False
			_lowerCAmelCase						=    False




			def       a	(					self   ):
								_UpperCamelCase											=   GLPNModelTester(self   )
								_UpperCamelCase											=   GLPNConfigTester(self			,     config_class=A_   )




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




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A_   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*A_   )




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




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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													_UpperCamelCase											=   model_class(A_   )
													_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]			,     A_   )




			def       a	(					self   ):
								_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(A_   )
													model.to(A_   )
													model.eval()
													with torch.no_grad():
																		_UpperCamelCase											=   model(**self._prepare_for_class(A_			,     A_   )   )
													_UpperCamelCase											=   outputs.attentions

													_UpperCamelCase											=   sum(self.model_tester.depths   )
													self.assertEqual(len(A_   )			,     A_   )

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

													self.assertEqual(len(A_   )			,     A_   )

													# 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(A_   )

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

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

													_UpperCamelCase											=   outputs.attentions

													self.assertEqual(len(A_   )			,     A_   )
													# 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   ):
								def check_hidden_states_output(A_			,     A_			,     A_   ):
													_UpperCamelCase											=   model_class(A_   )
													model.to(A_   )
													model.eval()

													with torch.no_grad():
																		_UpperCamelCase											=   model(**self._prepare_for_class(A_			,     A_   )   )

													_UpperCamelCase											=   outputs.hidden_states

													_UpperCamelCase											=   self.model_tester.num_encoder_blocks
													self.assertEqual(len(A_   )			,     A_   )

													# 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(A_			,     A_			,     A_   )

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

													check_hidden_states_output(A_			,     A_			,     A_   )




			def       a	(					self   ):
								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(A_   ):
																		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=A_   ).long()
													_UpperCamelCase											=   model_class(A_   )
													model.to(A_   )
													model.train()
													_UpperCamelCase											=   self._prepare_for_class(A_			,     A_			,     return_labels=A_   )
													_UpperCamelCase											=   model(**A_   ).loss
													loss.backward()




			@slow
			def       a	(					self   ):
								for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCamelCase											=   GLPNModel.from_pretrained(A_   )
													self.assertIsNotNone(A_   )




def  lowercase__(		)->   int:






					"""simple docstring"""

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

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

			'''simple docstring'''





			@slow
			def       a	(					self   ):
								_UpperCamelCase											=   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
								_UpperCamelCase											=   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(A_   )

								_UpperCamelCase											=   prepare_img()
								_UpperCamelCase											=   image_processor(images=A_			,     return_tensors="pt"   ).to(A_   )

								# forward pass
								with torch.no_grad():
													_UpperCamelCase											=   model(**A_   )

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

								_UpperCamelCase											=   torch.tensor(
								    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]   ).to(A_   )

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







'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



