

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__(a_       ):
						"""simple docstring"""






						def  UpperCamelCase__				(						self						)    ->   Optional[Any]:
									a_	:	Union[str, Any]          =   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__:
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase=13     ,			_lowercase=64     ,			_lowercase=3     ,			_lowercase=4     ,			_lowercase=[2, 2, 2, 2]     ,			_lowercase=[8, 4, 2, 1]     ,			_lowercase=[16, 32, 64, 128]     ,			_lowercase=[1, 4, 8, 16]     ,			_lowercase=[1, 2, 4, 8]     ,			_lowercase=True     ,			_lowercase=True     ,			_lowercase="gelu"     ,			_lowercase=0.1     ,			_lowercase=0.1     ,			_lowercase=0.0_2     ,			_lowercase=16     ,			_lowercase=3     ,			_lowercase=None     ,			)    ->   List[str]:
									a_	:	Dict          =   parent
									a_	:	Dict          =   batch_size
									a_	:	Optional[Any]          =   image_size
									a_	:	List[str]          =   num_channels
									a_	:	str          =   num_encoder_blocks
									a_	:	Dict          =   sr_ratios
									a_	:	Tuple          =   depths
									a_	:	Optional[Any]          =   hidden_sizes
									a_	:	Optional[int]          =   downsampling_rates
									a_	:	int          =   num_attention_heads
									a_	:	Optional[int]          =   is_training
									a_	:	Optional[Any]          =   use_labels
									a_	:	Optional[Any]          =   hidden_act
									a_	:	Dict          =   hidden_dropout_prob
									a_	:	int          =   attention_probs_dropout_prob
									a_	:	Tuple          =   initializer_range
									a_	:	List[Any]          =   decoder_hidden_size
									a_	:	List[str]          =   num_labels
									a_	:	Tuple          =   scope





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									a_	:	int          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

									a_	:	Tuple          =   self.get_config()
									return config, pixel_values, labels





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





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	Any          =   self.prepare_config_and_inputs()
									a_       ,				a_       ,				a_	:	Any          =   config_and_inputs
									a_	:	Dict          =   {"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_torch
class 					A__(a_,     a_,     unittest.TestCase       ):
						"""simple docstring"""






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

						_A						:							Optional[Any]									= False
						_A						:							Optional[Any]									= False
						_A						:							str									= False





						def  UpperCamelCase__				(						self						)    ->   Any:
									a_	:	int          =   GLPNModelTester(self						)
									a_	:	Dict          =   GLPNConfigTester(self     ,			config_class=_lowercase						)





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





						def  UpperCamelCase__				(						self						)    ->   Optional[int]:
									a_	:	Any          =   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Dict:
									a_	:	Optional[Any]          =   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  UpperCamelCase__				(						self						)    ->   str:
									pass





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





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

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

												a_	:	Optional[Any]          =   ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]     ,			_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   str:
									a_       ,				a_	:	Optional[Any]          =   self.model_tester.prepare_config_and_inputs_for_common()
									a_	:	Any          =   True

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

												a_	:	int          =   sum(self.model_tester.depths						)
												self.assertEqual(len(_lowercase						)     ,			_lowercase						)

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

												self.assertEqual(len(_lowercase						)     ,			_lowercase						)

												# verify the first attentions (first block, first layer)
												a_	:	Optional[Any]          =   (self.model_tester.image_size // 4) ** 2
												a_	:	Tuple          =   (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_	:	str          =   (self.model_tester.image_size // 32) ** 2
												a_	:	Optional[int]          =   (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_	:	str          =   len(_lowercase						)

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

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

												a_	:	Optional[int]          =   outputs.attentions

												self.assertEqual(len(_lowercase						)     ,			_lowercase						)
												# verify the first attentions (first block, first layer)
												a_	:	int          =   (self.model_tester.image_size // 4) ** 2
												a_	:	Union[str, Any]          =   (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]:
									def check_hidden_states_output(_lowercase     ,			_lowercase     ,			_lowercase						):
												a_	:	str          =   model_class(_lowercase						)
												model.to(_lowercase						)
												model.eval()

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

												a_	:	Any          =   outputs.hidden_states

												a_	:	str          =   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,
												    ]     ,			)

									a_       ,				a_	:	Optional[int]          =   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												a_	:	Optional[Any]          =   True
												check_hidden_states_output(_lowercase     ,			_lowercase     ,			_lowercase						)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												a_	:	Tuple          =   True

												check_hidden_states_output(_lowercase     ,			_lowercase     ,			_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   Any:
									if not self.model_tester.is_training:
												return

									a_       ,				a_	:	Optional[int]          =   self.model_tester.prepare_config_and_inputs_for_common()
									a_	:	int          =   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":
															a_       ,				a_       ,				a_       ,				a_	:	str          =   inputs_dict["""pixel_values"""].shape
															a_	:	Union[str, Any]          =   torch.zeros(
															    [self.model_tester.batch_size, height, width]     ,			device=_lowercase						).long()
												a_	:	Optional[int]          =   model_class(_lowercase						)
												model.to(_lowercase						)
												model.train()
												a_	:	Any          =   self._prepare_for_class(_lowercase     ,			_lowercase     ,			return_labels=_lowercase						)
												a_	:	str          =   model(**_lowercase						).loss
												loss.backward()





						@slow
						def  UpperCamelCase__				(						self						)    ->   List[str]:
									for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a_	:	List[Any]          =   GLPNModel.from_pretrained(_lowercase						)
												self.assertIsNotNone(_lowercase						)







def 				_UpperCAmelCase							(	):

			'''simple docstring'''



			a_	:	List[Any]          =   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						)    ->   Dict:
									a_	:	Any          =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
									a_	:	Optional[int]          =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_lowercase						)

									a_	:	Dict          =   prepare_img()
									a_	:	int          =   image_processor(images=_lowercase     ,			return_tensors="""pt"""						).to(_lowercase						)

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

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

									a_	:	Union[str, 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(_lowercase						)

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





from dataclasses import dataclass
from typing import Callable, Optional

import datasets



@dataclass
class 					A__(datasets.BuilderConfig       ):
						"""simple docstring"""






						_A						:							Optional[Callable]									= None
						_A						:							Optional[dict]									= None
						_A						:							Optional[datasets.Features]									= None





						def  UpperCamelCase__				(						self						)    ->   Tuple:
									assert self.generator is not None, "generator must be specified"

									if self.gen_kwargs is None:
												a_	:	Tuple          =   {}







class 					A__(datasets.GeneratorBasedBuilder       ):
						"""simple docstring"""






						_A						:							Dict									= GeneratorConfig





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									return datasets.DatasetInfo(features=self.config.features						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[Any]:
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,			gen_kwargs=self.config.gen_kwargs						)]





						def  UpperCamelCase__				(						self     ,			**_lowercase						)    ->   Tuple:
									for idx, ex in enumerate(self.config.generator(**_lowercase						)						):
												yield idx, ex


