

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     (		UpperCAmelCase ):


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			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     :


						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, 128]  ,    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  ,    ):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

										_UpperCAmelCase				:						Union[str, Any]       						=			self.get_config()
										return config, pixel_values, labels


						def 				_UpperCAmelCase (				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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			GLPNModel(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						Union[str, Any]       						=			model(A_		)
										_UpperCAmelCase				:						List[str]       						=			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  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			self.num_labels
										_UpperCAmelCase				:						Dict       						=			GLPNForDepthEstimation(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						Dict       						=			model(A_		)
										self.parent.assertEqual(result.predicted_depth.shape  ,    (self.batch_size, self.image_size, self.image_size)		)
										_UpperCAmelCase				:						Tuple       						=			model(A_  ,    labels=A_		)
										self.parent.assertEqual(result.predicted_depth.shape  ,    (self.batch_size, self.image_size, self.image_size)		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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







@require_torch
class  a     (		UpperCAmelCase							,       UpperCAmelCase							,       unittest.TestCase ):
						_lowercase			  =	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
						_lowercase			  =	(
						    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
						)

						_lowercase			  =	False
						_lowercase			  =	False
						_lowercase			  =	False


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			GLPNModelTester(self		)
										_UpperCAmelCase				:						str       						=			GLPNConfigTester(self  ,    config_class=A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


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







										'''simple docstring'''


										pass


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

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

														_UpperCAmelCase				:						int       						=			["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase				:						Tuple       						=			True

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

														_UpperCAmelCase				:						str       						=			sum(self.model_tester.depths		)
														self.assertEqual(len(A_		)  ,    A_		)

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

														self.assertEqual(len(A_		)  ,    A_		)

														# verify the first attentions (first block, first layer)
														_UpperCAmelCase				:						List[str]       						=			(self.model_tester.image_size // 4) ** 2
														_UpperCAmelCase				:						Union[str, 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				:						Dict       						=			(self.model_tester.image_size // 32) ** 2
														_UpperCAmelCase				:						Optional[Any]       						=			(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(A_		)

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

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

														_UpperCAmelCase				:						Optional[Any]       						=			outputs.attentions

														self.assertEqual(len(A_		)  ,    A_		)
														# verify the first attentions (first block, first layer)
														_UpperCAmelCase				:						int       						=			(self.model_tester.image_size // 4) ** 2
														_UpperCAmelCase				:						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		):







										'''simple docstring'''


										def check_hidden_states_output(A_  ,    A_  ,    A_		):
														_UpperCAmelCase				:						Union[str, Any]       						=			model_class(A_		)
														model.to(A_		)
														model.eval()

														with torch.no_grad():
																		_UpperCAmelCase				:						Optional[Any]       						=			model(**self._prepare_for_class(A_  ,    A_		)		)

														_UpperCAmelCase				:						Any       						=			outputs.hidden_states

														_UpperCAmelCase				:						Tuple       						=			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				:						List[Any]       						=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase				:						str       						=			True
														check_hidden_states_output(A_  ,    A_  ,    A_		)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_UpperCAmelCase				:						Optional[int]       						=			True

														check_hidden_states_output(A_  ,    A_  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										if not self.model_tester.is_training:
														return

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





						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase				:						Optional[Any]       						=			GLPNModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)







def 							__SCREAMING_SNAKE_CASE							( )      ->				int:
				_UpperCAmelCase				:						List[Any]       						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
				return image





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


						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
										_UpperCAmelCase				:						Dict       						=			GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(A_		)

										_UpperCAmelCase				:						List[str]       						=			prepare_img()
										_UpperCAmelCase				:						int       						=			image_processor(images=A_  ,    return_tensors="pt"		).to(A_		)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase				:						List[Any]       						=			model(**A_		)

										# verify the predicted depth
										_UpperCAmelCase				:						Optional[Any]       						=			torch.Size([1, 480, 640]		)
										self.assertEqual(outputs.predicted_depth.shape  ,    A_		)

										_UpperCAmelCase				:						Tuple       						=			torch.tensor(
										    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]		).to(A_		)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


