






"""simple docstring"""







import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
	from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import DPTImageProcessor







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Tuple				,		__A:     Dict				,		__A:     List[Any]=2				,		__A:     List[str]=32				,		__A:     Optional[int]=16				,		__A:     Union[str, Any]=3				,		__A:     int=True				,		__A:     Optional[Any]=True				,		__A:     Optional[int]=32				,		__A:     Dict=4				,		__A:     Any=[0, 1, 2, 3]				,		__A:     Any=4				,		__A:     Union[str, Any]=37				,		__A:     Dict="gelu"				,		__A:     Tuple=0.1				,		__A:     Dict=0.1				,		__A:     Optional[int]=0.0_2				,		__A:     str=3				,		__A:     Dict=[1, 384, 24, 24]				,		__A:     int=True				,		__A:     List[str]=None				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  is_training
												a__          =  use_labels
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  backbone_out_indices
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  num_labels
												a__          =  backbone_featmap_shape
												a__          =  scope
												a__          =  is_hybrid
												# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
												a__          =  (image_size // patch_size) ** 2
												a__          =  num_patches + 1




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

												a__          =  self.get_config()

												return config, pixel_values, labels




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  {
												    '''global_padding''': '''same''',
												    '''layer_type''': '''bottleneck''',
												    '''depths''': [3, 4, 9],
												    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
												    '''embedding_dynamic_padding''': True,
												    '''hidden_sizes''': [96, 192, 384, 768],
												    '''num_groups''': 2,
												}

												return DPTConfig(
												    image_size=self.image_size				,		patch_size=self.patch_size				,		num_channels=self.num_channels				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		backbone_out_indices=self.backbone_out_indices				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		is_hybrid=self.is_hybrid				,		backbone_config=__A				,		backbone_featmap_shape=self.backbone_featmap_shape				,		)




							def  lowercase		(   self:     List[str]				,		__A:     Optional[int]				,		__A:     List[Any]				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  DPTModel(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     int				,		__A:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  DPTForDepthEstimation(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A    )
												self.parent.assertEqual(result.predicted_depth.shape				,		(self.batch_size, self.image_size, self.image_size)    )




							def  lowercase		(   self:     Tuple				,		__A:     List[Any]				,		__A:     int				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  DPTForSemanticSegmentation(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		labels=__A    )
												self.parent.assertEqual(
												    result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size, self.image_size)    )





							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__          =  config_and_inputs
												a__          =  {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class        _SCREAMING_SNAKE_CASE						(  UpperCamelCase_       , UpperCamelCase_       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =	(
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							_SCREAMING_SNAKE_CASE    =	False
							_SCREAMING_SNAKE_CASE    =	False
							_SCREAMING_SNAKE_CASE    =	False




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  DPTModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		has_text_modality=__A				,		hidden_size=37    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason='''DPT does not use inputs_embeds'''    )
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a__          =  model_class(__A    )
																	self.assertIsInstance(model.get_input_embeddings()				,		(nn.Module)    )
																	a__          =  model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__A				,		nn.Linear    )    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_common()

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

																	a__          =  ['''pixel_values''']
																	self.assertListEqual(arg_names[:1]				,		__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*__A    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																	a__          =  self.model_tester.prepare_config_and_inputs_for_common()
																	a__          =  True

																	if model_class in get_values(__A    ):
																						continue

																	a__          =  model_class(__A    )
																	model.to(__A    )
																	model.train()
																	a__          =  self._prepare_for_class(__A				,		__A				,		return_labels=__A    )
																	a__          =  model(**__A    ).loss
																	loss.backward()




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																	a__          =  self.model_tester.prepare_config_and_inputs_for_common()
																	a__          =  False
																	a__          =  True

																	if model_class in get_values(__A    ) or not model_class.supports_gradient_checkpointing:
																						continue
																	a__          =  model_class(__A    )
																	model.to(__A    )
																	model.gradient_checkpointing_enable()
																	model.train()
																	a__          =  self._prepare_for_class(__A				,		__A				,		return_labels=__A    )
																	a__          =  model(**__A    ).loss
																	loss.backward()




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												a__          =  _config_zero_init(__A    )
												for model_class in self.all_model_classes:
																	a__          =  model_class(config=__A    )
																	# Skip the check for the backbone
																	a__          =  []
																	for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																											a__          =  [F'{name}.{key}' for key in module.state_dict().keys()]
																											break

																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											if name in backbone_params:
																																continue
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()				,		[0.0, 1.0]				,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,		)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass




							@slow
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																	a__          =  DPTModel.from_pretrained(__A    )
																	self.assertIsNotNone(__A    )





							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_common()
												a__          =  '''add'''
												with self.assertRaises(__A    ):
																	a__          =  DPTForDepthEstimation(__A    )





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
					return image







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





							"""simple docstring"""





							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''    )
												a__          =  DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''    ).to(__A    )

												a__          =  prepare_img()
												a__          =  image_processor(images=__A				,		return_tensors='''pt'''    ).to(__A    )

												# forward pass
												with torch.no_grad():
																	a__          =  model(**__A    )
																	a__          =  outputs.predicted_depth

												# verify the predicted depth
												a__          =  torch.Size((1, 384, 384)    )
												self.assertEqual(predicted_depth.shape				,		__A    )

												a__          =  torch.tensor(
												    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]    ).to(__A    )

												self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100				,		__A				,		atol=1e-4    )    )










"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_attention_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  num_choices




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  None
												if self.use_attention_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

												a__          =  None
												if self.use_token_type_ids:
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size    )

												a__          =  RobertaPreLayerNormConfig(
												    vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)

												return config, input_ids, token_type_ids, attention_mask




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

							_SCREAMING_SNAKE_CASE    =(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	a__          =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )
																	a__          =  model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__A    )







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


