



"""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			(					lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_snake_case						,    """hidden_sizes"""		)		)
									self.parent.assertTrue(hasattr(_snake_case						,    """num_attention_heads"""		)		)
									self.parent.assertTrue(hasattr(_snake_case						,    """num_encoder_blocks"""		)		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case						,    _snake_case=13						,    _snake_case=64						,    _snake_case=3						,    _snake_case=4						,    _snake_case=[2, 2, 2, 2]						,    _snake_case=[8, 4, 2, 1]						,    _snake_case=[16, 32, 64, 128]						,    _snake_case=[1, 4, 8, 16]						,    _snake_case=[1, 2, 4, 8]						,    _snake_case=True						,    _snake_case=True						,    _snake_case="gelu"						,    _snake_case=0.1						,    _snake_case=0.1						,    _snake_case=0.02						,    _snake_case=16						,    _snake_case=3						,    _snake_case=None						,    ):



									"""simple docstring"""





									_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 							snake_case       (							self		):



									"""simple docstring"""





									_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 							snake_case       (							self		):



									"""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 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   GLPNModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									_lowerCAmelCase      =   model(_snake_case		)
									_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 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.num_labels
									_lowerCAmelCase      =   GLPNForDepthEstimation(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									_lowerCAmelCase      =   model(_snake_case		)
									self.parent.assertEqual(result.predicted_depth.shape						,    (self.batch_size, self.image_size, self.image_size)		)
									_lowerCAmelCase      =   model(_snake_case						,    labels=_snake_case		)
									self.parent.assertEqual(result.predicted_depth.shape						,    (self.batch_size, self.image_size, self.image_size)		)




				def 							snake_case       (							self		):



									"""simple docstring"""





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





@require_torch
class  __lowerCAmelCase			(					lowerCamelCase__	,						lowerCamelCase__	,						unittest.TestCase	):
				__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 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   GLPNModelTester(self		)
									_lowerCAmelCase      =   GLPNConfigTester(self						,    config_class=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_snake_case		)




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



									"""simple docstring"""





									pass




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



									"""simple docstring"""





									pass




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase     ,     _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														_lowerCAmelCase      =   model_class(_snake_case		)
														_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]						,    _snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_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(_snake_case		)
														model.to(_snake_case		)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase      =   model(**self._prepare_for_class(_snake_case						,    _snake_case		)		)
														_lowerCAmelCase      =   outputs.attentions

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

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

														self.assertEqual(len(_snake_case		)						,    _snake_case		)

														# 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(_snake_case		)

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

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

														_lowerCAmelCase      =   outputs.attentions

														self.assertEqual(len(_snake_case		)						,    _snake_case		)
														# 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 							snake_case       (							self		):



									"""simple docstring"""





									def check_hidden_states_output(_snake_case						,    _snake_case						,    _snake_case		):
														_lowerCAmelCase      =   model_class(_snake_case		)
														model.to(_snake_case		)
														model.eval()

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

														_lowerCAmelCase      =   outputs.hidden_states

														_lowerCAmelCase      =   self.model_tester.num_encoder_blocks
														self.assertEqual(len(_snake_case		)						,    _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,
														    ]						,    )

									_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(_snake_case						,    _snake_case						,    _snake_case		)

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

														check_hidden_states_output(_snake_case						,    _snake_case						,    _snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





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




				@slow
				def 							snake_case       (							self		):



									"""simple docstring"""





									for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase      =   GLPNModel.from_pretrained(_snake_case		)
														self.assertIsNotNone(_snake_case		)


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

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





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




				@slow
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
									_lowerCAmelCase      =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_snake_case		)

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

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

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

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

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








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image





class  __lowerCAmelCase			(					unittest.TestCase	,						lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   load_tool("""image-question-answering"""		)
									self.tool.setup()
									_lowerCAmelCase      =   load_tool("""image-question-answering"""						,    remote=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)



