
"""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      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       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      lowerCAmelCase_						:


				"""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__=0.02       ,	SCREAMING_SNAKE_CASE__=16       ,	SCREAMING_SNAKE_CASE__=3       ,	SCREAMING_SNAKE_CASE__=None       ,	) ->		Union[str, Any]:



											"""simple docstring"""




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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.get_config()
											return config, pixel_values, labels



				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       GLPNModel(config=SCREAMING_SNAKE_CASE__   )
											model.to(SCREAMING_SNAKE_CASE__   )
											model.eval()
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       model(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, 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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       self.num_labels
											SCREAMING_SNAKE_CASE__						:	str									=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE__   )
											model.to(SCREAMING_SNAKE_CASE__   )
											model.eval()
											SCREAMING_SNAKE_CASE__						:	Any									=       model(SCREAMING_SNAKE_CASE__   )
											self.parent.assertEqual(result.predicted_depth.shape       ,	(self.batch_size, self.image_size, self.image_size)   )
											SCREAMING_SNAKE_CASE__						:	int									=       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      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       config_and_inputs
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       {"""pixel_values""": pixel_values}
											return config, inputs_dict






@require_torch
class      lowerCAmelCase_						(a__			, a__			, unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase				:						str 	=       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
				__UpperCamelCase				:						Dict 	=       (
				    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
				)

				__UpperCamelCase				:						int 	=       False
				__UpperCamelCase				:						Optional[Any] 	=       False
				__UpperCamelCase				:						Union[str, Any] 	=       False



				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       GLPNModelTester(self   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       GLPNConfigTester(self       ,	config_class=SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											self.config_tester.run_common_tests()



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       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      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




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

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

																		SCREAMING_SNAKE_CASE__						:	str									=       ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]       ,	SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       True

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

																		SCREAMING_SNAKE_CASE__						:	str									=       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"]
																		SCREAMING_SNAKE_CASE__						:	int									=       True
																		SCREAMING_SNAKE_CASE__						:	int									=       model_class(SCREAMING_SNAKE_CASE__   )
																		model.to(SCREAMING_SNAKE_CASE__   )
																		model.eval()
																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE__						:	Any									=       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )   )
																		SCREAMING_SNAKE_CASE__						:	Dict									=       outputs.attentions

																		self.assertEqual(len(SCREAMING_SNAKE_CASE__   )       ,	SCREAMING_SNAKE_CASE__   )

																		# verify the first attentions (first block, first layer)
																		SCREAMING_SNAKE_CASE__						:	Dict									=       (self.model_tester.image_size // 4) ** 2
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       (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)
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       (self.model_tester.image_size // 32) ** 2
																		SCREAMING_SNAKE_CASE__						:	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]       ,	)
																		SCREAMING_SNAKE_CASE__						:	str									=       len(SCREAMING_SNAKE_CASE__   )

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

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

																		SCREAMING_SNAKE_CASE__						:	int									=       outputs.attentions

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



											"""simple docstring"""




											def check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       model_class(SCREAMING_SNAKE_CASE__   )
																		model.to(SCREAMING_SNAKE_CASE__   )
																		model.eval()

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

																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       outputs.hidden_states

																		SCREAMING_SNAKE_CASE__						:	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,
																		    ]       ,	)

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

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:	str									=       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"]
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       True

																		check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											if not self.model_tester.is_training:
																		return

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



				@slow
				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




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






def lowercase_  (       ):
							SCREAMING_SNAKE_CASE__						:	str									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
							return image



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


				"""simple docstring"""



				@slow
				def      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
											SCREAMING_SNAKE_CASE__						:	Dict									=       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Tuple									=       prepare_img()
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       image_processor(images=SCREAMING_SNAKE_CASE__       ,	return_tensors="""pt"""   ).to(SCREAMING_SNAKE_CASE__   )

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       model(**SCREAMING_SNAKE_CASE__   )

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

											SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
											    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]   ).to(SCREAMING_SNAKE_CASE__   )

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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				:   Optional[Any]   							=			{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Tuple   							=			[
					    'UMT5EncoderModel',
					    'UMT5ForConditionalGeneration',
					    'UMT5ForQuestionAnswering',
					    'UMT5Model',
					    'UMT5PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_umta import UMTaConfig, UMTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_umta import (
										    UMTaEncoderModel,
										    UMTaForConditionalGeneration,
										    UMTaForQuestionAnswering,
										    UMTaModel,
										    UMTaPreTrainedModel,
										)
else:
					import sys

					UpperCAmelCase__				:   Optional[Any]   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




