





'''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  lowerCamelCase__				:


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=2   ,     UpperCAmelCase__=3_2   ,     UpperCAmelCase__=1_6   ,     UpperCAmelCase__=3   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=3_2   ,     UpperCAmelCase__=4   ,     UpperCAmelCase__=[0, 1, 2, 3]   ,     UpperCAmelCase__=4   ,     UpperCAmelCase__=3_7   ,     UpperCAmelCase__="gelu"   ,     UpperCAmelCase__=0.1   ,     UpperCAmelCase__=0.1   ,     UpperCAmelCase__=0.0_2   ,     UpperCAmelCase__=3   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=None   ,     )       ->				int:
					_A :				List[Any]				=  parent
					_A :				Tuple				=  batch_size
					_A :				int				=  image_size
					_A :				Optional[int]				=  patch_size
					_A :				Tuple				=  num_channels
					_A :				Optional[int]				=  is_training
					_A :				Optional[int]				=  use_labels
					_A :				str				=  hidden_size
					_A :				Any				=  num_hidden_layers
					_A :				int				=  backbone_out_indices
					_A :				Any				=  num_attention_heads
					_A :				Any				=  intermediate_size
					_A :				Union[str, Any]				=  hidden_act
					_A :				Optional[Any]				=  hidden_dropout_prob
					_A :				Dict				=  attention_probs_dropout_prob
					_A :				Optional[Any]				=  initializer_range
					_A :				Optional[int]				=  num_labels
					_A :				List[Any]				=  scope
					_A :				List[Any]				=  is_hybrid
					# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
					_A :				int				=  (image_size // patch_size) ** 2
					_A :				List[str]				=  num_patches + 1




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A :				List[Any]				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

					_A :				Optional[Any]				=  None
					if self.use_labels:
									_A :				Union[str, Any]				=  ids_tensor([self.batch_size, self.image_size, self.image_size]   ,     self.num_labels			)

					_A :				Optional[int]				=  self.get_config()

					return config, pixel_values, labels




	def 				_lowerCamelCase				(					self			)       ->				Any:
					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=UpperCAmelCase__   ,     initializer_range=self.initializer_range   ,     is_hybrid=self.is_hybrid   ,     )




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				int:
					_A :				Dict				=  DPTModel(config=UpperCAmelCase__			)
					model.to(UpperCAmelCase__			)
					model.eval()
					_A :				Optional[Any]				=  model(UpperCAmelCase__			)
					self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				int:
					_A :				Tuple				=  self.num_labels
					_A :				List[str]				=  DPTForDepthEstimation(UpperCAmelCase__			)
					model.to(UpperCAmelCase__			)
					model.eval()
					_A :				str				=  model(UpperCAmelCase__			)
					self.parent.assertEqual(result.predicted_depth.shape   ,     (self.batch_size, self.image_size, self.image_size)			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[Any]:
					_A :				Optional[Any]				=  self.num_labels
					_A :				Any				=  DPTForSemanticSegmentation(UpperCAmelCase__			)
					model.to(UpperCAmelCase__			)
					model.eval()
					_A :				List[str]				=  model(UpperCAmelCase__   ,     labels=UpperCAmelCase__			)
					self.parent.assertEqual(
					    result.logits.shape   ,     (self.batch_size, self.num_labels, self.image_size, self.image_size)			)




	def 				_lowerCamelCase				(					self			)       ->				Any:
					_A :				List[Any]				=  self.prepare_config_and_inputs()
					_A	,       _A	,       _A :				Dict				=  config_and_inputs
					_A :				Optional[int]				=  {'''pixel_values''': pixel_values}
					return config, inputs_dict



@require_torch
class  lowerCamelCase__				(						snake_case_  ,							snake_case_  ,							unittest.TestCase						):


	"""simple docstring"""





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

	__magic_name__							   =		False
	__magic_name__							   =		False
	__magic_name__							   =		False




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A :				Dict				=  DPTModelTester(self			)
					_A :				str				=  ConfigTester(self   ,     config_class=UpperCAmelCase__   ,     has_text_modality=UpperCAmelCase__   ,     hidden_size=3_7			)




	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					self.config_tester.run_common_tests()




	@unittest.skip(reason='''DPT does not use inputs_embeds'''			)
	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					pass




	def 				_lowerCamelCase				(					self			)       ->				Optional[Any]:
					_A	,       _A :				Tuple				=  self.model_tester.prepare_config_and_inputs_for_common()

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




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A	,       _A :				Union[str, Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									_A :				Tuple				=  model_class(UpperCAmelCase__			)
									_A :				Union[str, Any]				=  inspect.signature(model.forward			)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_A :				str				=  [*signature.parameters.keys()]

									_A :				Union[str, Any]				=  ['''pixel_values''']
									self.assertListEqual(arg_names[:1]   ,     UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				Any:
					_A :				Tuple				=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				Tuple:
					_A :				Union[str, Any]				=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				str:
					_A :				Dict				=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				int:
					for model_class in self.all_model_classes:
									if model_class.__name__ == "DPTForDepthEstimation":
													continue

									_A	,       _A :				Optional[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()
									_A :				List[str]				=  True

									if model_class in get_values(UpperCAmelCase__			):
													continue

									_A :				Any				=  model_class(UpperCAmelCase__			)
									model.to(UpperCAmelCase__			)
									model.train()
									_A :				Optional[int]				=  self._prepare_for_class(UpperCAmelCase__   ,     UpperCAmelCase__   ,     return_labels=UpperCAmelCase__			)
									_A :				Dict				=  model(**UpperCAmelCase__			).loss
									loss.backward()




	def 				_lowerCamelCase				(					self			)       ->				Tuple:
					for model_class in self.all_model_classes:
									if model_class.__name__ == "DPTForDepthEstimation":
													continue

									_A	,       _A :				Union[str, Any]				=  self.model_tester.prepare_config_and_inputs_for_common()
									_A :				int				=  False
									_A :				Tuple				=  True

									if model_class in get_values(UpperCAmelCase__			) or not model_class.supports_gradient_checkpointing:
													continue
									_A :				List[Any]				=  model_class(UpperCAmelCase__			)
									model.to(UpperCAmelCase__			)
									model.gradient_checkpointing_enable()
									model.train()
									_A :				Optional[Any]				=  self._prepare_for_class(UpperCAmelCase__   ,     UpperCAmelCase__   ,     return_labels=UpperCAmelCase__			)
									_A :				Optional[int]				=  model(**UpperCAmelCase__			).loss
									loss.backward()




	def 				_lowerCamelCase				(					self			)       ->				Union[str, Any]:
					_A	,       _A :				Optional[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

					_A :				Union[str, Any]				=  _config_zero_init(UpperCAmelCase__			)
					for model_class in self.all_model_classes:
									_A :				Tuple				=  model_class(config=UpperCAmelCase__			)
									# Skip the check for the backbone
									_A :				Optional[Any]				=  []
									for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																	_A :				Union[str, Any]				=  [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 				_lowerCamelCase				(					self			)       ->				Dict:
					pass




	@slow
	def 				_lowerCamelCase				(					self			)       ->				str:
					for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_A :				Dict				=  DPTModel.from_pretrained(UpperCAmelCase__			)
									self.assertIsNotNone(UpperCAmelCase__			)





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Union[str, Any]				=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image




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


	"""simple docstring"""





	def 				_lowerCamelCase				(					self			)       ->				Dict:
					_A :				Optional[int]				=  DPTImageProcessor.from_pretrained('''Intel/dpt-large'''			)
					_A :				Optional[int]				=  DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''			).to(UpperCAmelCase__			)

					_A :				List[str]				=  prepare_img()
					_A :				Union[str, Any]				=  image_processor(images=UpperCAmelCase__   ,     return_tensors='''pt'''			).to(UpperCAmelCase__			)

					# forward pass
					with torch.no_grad():
									_A :				Union[str, Any]				=  model(**UpperCAmelCase__			)
									_A :				str				=  outputs.predicted_depth

					# verify the predicted depth
					_A :				str				=  torch.Size((1, 3_8_4, 3_8_4)			)
					self.assertEqual(predicted_depth.shape   ,     UpperCAmelCase__			)

					_A :				int				=  torch.tensor(
					    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]			).to(UpperCAmelCase__			)

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




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A :				int				=  DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''			)
					_A :				List[str]				=  DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''			).to(UpperCAmelCase__			)

					_A :				Optional[Any]				=  prepare_img()
					_A :				List[Any]				=  image_processor(images=UpperCAmelCase__   ,     return_tensors='''pt'''			).to(UpperCAmelCase__			)

					# forward pass
					with torch.no_grad():
									_A :				Any				=  model(**UpperCAmelCase__			)

					# verify the logits
					_A :				List[str]				=  torch.Size((1, 1_5_0, 4_8_0, 4_8_0)			)
					self.assertEqual(outputs.logits.shape   ,     UpperCAmelCase__			)

					_A :				int				=  torch.tensor(
					    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]			).to(UpperCAmelCase__			)

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




	def 				_lowerCamelCase				(					self			)       ->				str:
					_A :				str				=  DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''			)
					_A :				str				=  DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''			).to(UpperCAmelCase__			)

					_A :				Tuple				=  prepare_img()
					_A :				Any				=  image_processor(images=UpperCAmelCase__   ,     return_tensors='''pt'''			).to(UpperCAmelCase__			)

					# forward pass
					with torch.no_grad():
									_A :				Dict				=  model(**UpperCAmelCase__			)

					_A :				List[Any]				=  outputs.logits.detach().cpu()

					_A :				List[Any]				=  image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase__   ,     target_sizes=[(5_0_0, 3_0_0)]			)
					_A :				Union[str, Any]				=  torch.Size((5_0_0, 3_0_0)			)
					self.assertEqual(segmentation[0].shape   ,     UpperCAmelCase__			)

					_A :				Optional[Any]				=  image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase__			)
					_A :				int				=  torch.Size((4_8_0, 4_8_0)			)
					self.assertEqual(segmentation[0].shape   ,     UpperCAmelCase__			)







'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
