


"""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      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  self.config_class(**self.inputs_dict      )
					self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							'''hidden_sizes'''      )      )
					self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							'''num_attention_heads'''      )      )
					self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							'''num_encoder_blocks'''      )      )






class     A:

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=13				,							SCREAMING_SNAKE_CASE__=64				,							SCREAMING_SNAKE_CASE__=3				,							SCREAMING_SNAKE_CASE__=4				,							SCREAMING_SNAKE_CASE__=[2, 2, 2, 2]				,							SCREAMING_SNAKE_CASE__=[8, 4, 2, 1]				,							SCREAMING_SNAKE_CASE__=[16, 32, 64, 1_28]				,							SCREAMING_SNAKE_CASE__=[1, 4, 8, 16]				,							SCREAMING_SNAKE_CASE__=[1, 2, 4, 8]				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__="gelu"				,							SCREAMING_SNAKE_CASE__=0.1				,							SCREAMING_SNAKE_CASE__=0.1				,							SCREAMING_SNAKE_CASE__=0.0_2				,							SCREAMING_SNAKE_CASE__=16				,							SCREAMING_SNAKE_CASE__=3				,							SCREAMING_SNAKE_CASE__=None				,							)			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  parent
					_UpperCamelCase				:int				    =  batch_size
					_UpperCamelCase				:Tuple				    =  image_size
					_UpperCamelCase				:List[Any]				    =  num_channels
					_UpperCamelCase				:Optional[int]				    =  num_encoder_blocks
					_UpperCamelCase				:str				    =  sr_ratios
					_UpperCamelCase				:List[str]				    =  depths
					_UpperCamelCase				:Dict				    =  hidden_sizes
					_UpperCamelCase				:List[Any]				    =  downsampling_rates
					_UpperCamelCase				:List[Any]				    =  num_attention_heads
					_UpperCamelCase				:List[Any]				    =  is_training
					_UpperCamelCase				:str				    =  use_labels
					_UpperCamelCase				:Optional[int]				    =  hidden_act
					_UpperCamelCase				:Optional[int]				    =  hidden_dropout_prob
					_UpperCamelCase				:Tuple				    =  attention_probs_dropout_prob
					_UpperCamelCase				:Optional[int]				    =  initializer_range
					_UpperCamelCase				:Optional[Any]				    =  decoder_hidden_size
					_UpperCamelCase				:List[Any]				    =  num_labels
					_UpperCamelCase				:Optional[int]				    =  scope
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

					_UpperCamelCase				:Optional[Any]				    =  None
					if self.use_labels:
								_UpperCamelCase				:Union[str, Any]				    =  ids_tensor([self.batch_size, self.image_size, self.image_size]				,							self.num_labels      )

					_UpperCamelCase				:Optional[Any]				    =  self.get_config()
					return config, pixel_values, labels
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					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				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  GLPNModel(config=SCREAMING_SNAKE_CASE__      )
					model.to(SCREAMING_SNAKE_CASE__      )
					model.eval()
					_UpperCamelCase				:List[Any]				    =  model(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Optional[Any]				    =  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				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  self.num_labels
					_UpperCamelCase				:Optional[Any]				    =  GLPNForDepthEstimation(SCREAMING_SNAKE_CASE__      )
					model.to(SCREAMING_SNAKE_CASE__      )
					model.eval()
					_UpperCamelCase				:Union[str, Any]				    =  model(SCREAMING_SNAKE_CASE__      )
					self.parent.assertEqual(result.predicted_depth.shape				,							(self.batch_size, self.image_size, self.image_size)      )
					_UpperCamelCase				:Any				    =  model(SCREAMING_SNAKE_CASE__				,							labels=SCREAMING_SNAKE_CASE__      )
					self.parent.assertEqual(result.predicted_depth.shape				,							(self.batch_size, self.image_size, self.image_size)      )





		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Optional[int]				    =  self.prepare_config_and_inputs()
					_UpperCamelCase    ,			_UpperCamelCase    ,			_UpperCamelCase				:Dict				    =  config_and_inputs
					_UpperCamelCase				:Tuple				    =  {'''pixel_values''': pixel_values}
					return config, inputs_dict






@require_torch
class     A(     lowerCamelCase__							,       lowerCamelCase__							,       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      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  GLPNModelTester(self      )
					_UpperCamelCase				:Optional[int]				    =  GLPNConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Optional[Any]:

					"""simple docstring"""



					self.config_tester.run_common_tests()
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE__      )
		@unittest.skip('''GLPN does not use inputs_embeds'''      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



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

					"""simple docstring"""



					pass
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					_UpperCamelCase    ,			_UpperCamelCase				:Any				    =  self.model_tester.prepare_config_and_inputs_for_common()

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

								_UpperCamelCase				:Any				    =  ['''pixel_values''']
								self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase    ,			_UpperCamelCase				:Any				    =  self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCamelCase				:int				    =  True

					for model_class in self.all_model_classes:
								_UpperCamelCase				:str				    =  True
								_UpperCamelCase				:List[Any]				    =  False
								_UpperCamelCase				:Tuple				    =  True
								_UpperCamelCase				:Union[str, Any]				    =  model_class(SCREAMING_SNAKE_CASE__      )
								model.to(SCREAMING_SNAKE_CASE__      )
								model.eval()
								with torch.no_grad():
											_UpperCamelCase				:Dict				    =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )      )
								_UpperCamelCase				:Optional[Any]				    =  outputs.attentions

								_UpperCamelCase				:Any				    =  sum(self.model_tester.depths      )
								self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )

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

								self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )

								# verify the first attentions (first block, first layer)
								_UpperCamelCase				:str				    =  (self.model_tester.image_size // 4) ** 2
								_UpperCamelCase				:Optional[Any]				    =  (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				:List[str]				    =  (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				:Any				    =  len(SCREAMING_SNAKE_CASE__      )

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

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

								_UpperCamelCase				:Any				    =  outputs.attentions

								self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
								# verify the first attentions (first block, first layer)
								_UpperCamelCase				:str				    =  (self.model_tester.image_size // 4) ** 2
								_UpperCamelCase				:str				    =  (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      )			->  int:

					"""simple docstring"""



					def check_hidden_states_output(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ):
								_UpperCamelCase				:int				    =  model_class(SCREAMING_SNAKE_CASE__      )
								model.to(SCREAMING_SNAKE_CASE__      )
								model.eval()

								with torch.no_grad():
											_UpperCamelCase				:List[str]				    =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )      )

								_UpperCamelCase				:Any				    =  outputs.hidden_states

								_UpperCamelCase				:List[Any]				    =  self.model_tester.num_encoder_blocks
								self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_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				:Dict				    =  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								_UpperCamelCase				:Union[str, Any]				    =  True
								check_hidden_states_output(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								_UpperCamelCase				:Union[str, Any]				    =  True

								check_hidden_states_output(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					if not self.model_tester.is_training:
								return

					_UpperCamelCase    ,			_UpperCamelCase				:int				    =  self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCamelCase				:str				    =  True

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





		@slow
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_UpperCamelCase				:str				    =  GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE__      )
								self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )





def 							A_					(   )				->     str:
			_UpperCamelCase				:Tuple				    =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image






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

		"""simple docstring"""





		@slow
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
					_UpperCamelCase				:List[str]				    =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Union[str, Any]				    =  prepare_img()
					_UpperCamelCase				:Union[str, Any]				    =  image_processor(images=SCREAMING_SNAKE_CASE__				,							return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE__      )

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

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

					_UpperCamelCase				:List[Any]				    =  torch.tensor(
					    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      ).to(SCREAMING_SNAKE_CASE__      )

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





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available






@dataclass
class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

