

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     a__  :
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=2					,					lowercase=32					,					lowercase=16					,					lowercase=3					,					lowercase=True					,					lowercase=True					,					lowercase=32					,					lowercase=4					,					lowercase=[0, 1, 2, 3]					,					lowercase=4					,					lowercase=37					,					lowercase="gelu"					,					lowercase=0.1					,					lowercase=0.1					,					lowercase=0.02					,					lowercase=3					,					lowercase=[1, 384, 24, 24]					,					lowercase=True					,					lowercase=None					,					)     ->  Union[str, Any]:
							'''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    UpperCamelCase						(			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    UpperCamelCase						(			self				)     ->  Dict:
							'''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=lowercase					,					initializer_range=self.initializer_range					,					is_hybrid=self.is_hybrid					,					backbone_config=lowercase					,					backbone_featmap_shape=self.backbone_featmap_shape					,					)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  int:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






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

@require_torch
class     a__  (    snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







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

			__lowerCamelCase       				=				False
			__lowerCamelCase       				=				False
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	DPTModelTester(self				)
							A__						=	ConfigTester(self					,					config_class=lowercase					,					has_text_modality=lowercase					,					hidden_size=37				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			@unittest.skip(reason="DPT does not use inputs_embeds"				)
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__			, A__						=	self.model_tester.prepare_config_and_inputs_for_common()

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


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__			, A__						=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											A__						=	model_class(lowercase				)
											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]					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowercase				)


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase				)


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






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

											A__			, A__						=	self.model_tester.prepare_config_and_inputs_for_common()
											A__						=	True

											if model_class in get_values(lowercase				):
															continue

											A__						=	model_class(lowercase				)
											model.to(lowercase				)
											model.train()
											A__						=	self._prepare_for_class(lowercase					,					lowercase					,					return_labels=lowercase				)
											A__						=	model(**lowercase				).loss
											loss.backward()


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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

											A__			, A__						=	self.model_tester.prepare_config_and_inputs_for_common()
											A__						=	False
											A__						=	True

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


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__			, A__						=	self.model_tester.prepare_config_and_inputs_for_common()

							A__						=	_config_zero_init(lowercase				)
							for model_class in self.all_model_classes:
											A__						=	model_class(config=lowercase				)
											# 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    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							pass


			@slow
			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__			, A__						=	self.model_tester.prepare_config_and_inputs_for_common()
							A__						=	"add"
							with self.assertRaises(lowercase				):
											A__						=	DPTForDepthEstimation(lowercase				)



def     lowerCAmelCase__							(		)	->					List[str]:




				'''simple docstring'''





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





@require_torch
@require_vision
@slow
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"				)
							A__						=	DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"				).to(lowercase				)

							A__						=	prepare_img()
							A__						=	image_processor(images=lowercase					,					return_tensors="pt"				).to(lowercase				)

							# forward pass
							with torch.no_grad():
											A__						=	model(**lowercase				)
											A__						=	outputs.predicted_depth

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

							A__						=	torch.tensor(
							    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]				).to(lowercase				)

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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      int					 =     {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[Any]					 =     ["MBartTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Dict					 =     ["MBartTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
         "MBartForConditionalGeneration",
         "MBartForQuestionAnswering",
         "MBartForSequenceClassification",
         "MBartModel",
         "MBartPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
         "FlaxMBartForSequenceClassification",
         "FlaxMBartModel",
         "FlaxMBartPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
