






"""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							:	Tuple						,			a_							:	int						,			a_							:	List[Any]=2						,			a_							:	List[str]=32						,			a_							:	Union[str, Any]=16						,			a_							:	Optional[Any]=3						,			a_							:	List[str]=True						,			a_							:	Dict=True						,			a_							:	Tuple=32						,			a_							:	List[str]=4						,			a_							:	Union[str, Any]=[0, 1, 2, 3]						,			a_							:	List[str]=4						,			a_							:	int=37						,			a_							:	Optional[int]="gelu"						,			a_							:	int=0.1						,			a_							:	List[str]=0.1						,			a_							:	Union[str, Any]=0.02						,			a_							:	str=3						,			a_							:	Any=[1, 3_84, 24, 24]						,			a_							:	Optional[int]=True						,			a_							:	List[Any]=None						,			):
				lowerCAmelCase_   :							Optional[Any]									=			parent
				lowerCAmelCase_   :							Optional[Any]									=			batch_size
				lowerCAmelCase_   :							int									=			image_size
				lowerCAmelCase_   :							int									=			patch_size
				lowerCAmelCase_   :							List[str]									=			num_channels
				lowerCAmelCase_   :							Optional[Any]									=			is_training
				lowerCAmelCase_   :							Any									=			use_labels
				lowerCAmelCase_   :							str									=			hidden_size
				lowerCAmelCase_   :							Dict									=			num_hidden_layers
				lowerCAmelCase_   :							Any									=			backbone_out_indices
				lowerCAmelCase_   :							Optional[int]									=			num_attention_heads
				lowerCAmelCase_   :							Union[str, Any]									=			intermediate_size
				lowerCAmelCase_   :							int									=			hidden_act
				lowerCAmelCase_   :							Tuple									=			hidden_dropout_prob
				lowerCAmelCase_   :							Optional[Any]									=			attention_probs_dropout_prob
				lowerCAmelCase_   :							Optional[int]									=			initializer_range
				lowerCAmelCase_   :							Union[str, Any]									=			num_labels
				lowerCAmelCase_   :							str									=			backbone_featmap_shape
				lowerCAmelCase_   :							Tuple									=			scope
				lowerCAmelCase_   :							Tuple									=			is_hybrid
				# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
				lowerCAmelCase_   :							Any									=			(image_size // patch_size) ** 2
				lowerCAmelCase_   :							Tuple									=			num_patches + 1





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Tuple									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

				lowerCAmelCase_   :							List[Any]									=			self.get_config()

				return config, pixel_values, labels





		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Union[str, Any]									=			{
				    "global_padding": "same",
				    "layer_type": "bottleneck",
				    "depths": [3, 4, 9],
				    "out_features": ["stage1", "stage2", "stage3"],
				    "embedding_dynamic_padding": True,
				    "hidden_sizes": [96, 1_92, 3_84, 7_68],
				    "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    lowerCamelCase       (   self							:	List[str]						,			a_							:	Dict						,			a_							:	str						,			a_							:	List[Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			DPTModel(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Optional[Any]									=			model(a_	)
				self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	str						,			a_							:	str						,			a_							:	Union[str, Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			self.num_labels
				lowerCAmelCase_   :							List[Any]									=			DPTForDepthEstimation(a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							List[Any]									=			model(a_	)
				self.parent.assertEqual(result.predicted_depth.shape						,			(self.batch_size, self.image_size, self.image_size)	)





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	int						,			a_							:	Any						,			a_							:	Optional[int]	):
				lowerCAmelCase_   :							int									=			self.num_labels
				lowerCAmelCase_   :							List[Any]									=			DPTForSemanticSegmentation(a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Optional[Any]									=			model(a_						,			labels=a_	)
				self.parent.assertEqual(
				    result.logits.shape						,			(self.batch_size, self.num_labels, self.image_size, self.image_size)	)





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			self.prepare_config_and_inputs()
				lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_   :							Optional[Any]									=			config_and_inputs
				lowerCAmelCase_   :							List[str]									=			{"pixel_values": pixel_values}
				return config, inputs_dict







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





		'''simple docstring'''






		a_	:				Optional[Any]            =							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		a_	:				Union[str, Any]            =							(
		    {
		        """depth-estimation""": DPTForDepthEstimation,
		        """feature-extraction""": DPTModel,
		        """image-segmentation""": DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		a_	:				List[str]            =							False
		a_	:				Union[str, Any]            =							False
		a_	:				Dict            =							False





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							str									=			DPTModelTester(self	)
				lowerCAmelCase_   :							Any									=			ConfigTester(self						,			config_class=a_						,			has_text_modality=a_						,			hidden_size=37	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				self.config_tester.run_common_tests()





		@unittest.skip(reason="DPT does not use inputs_embeds"	)
		def    lowerCamelCase       (   self							:	Any	):
				pass





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Dict									=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowerCAmelCase_   :							Any									=			model_class(a_	)
						self.assertIsInstance(model.get_input_embeddings()						,			(nn.Module)	)
						lowerCAmelCase_   :							Optional[Any]									=			model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(a_						,			nn.Linear	)	)





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Tuple									=			self.model_tester.prepare_config_and_inputs_for_common()

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

						lowerCAmelCase_   :							Union[str, Any]									=			["pixel_values"]
						self.assertListEqual(arg_names[:1]						,			a_	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							str									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a_	)





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Dict									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*a_	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Dict									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*a_	)





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

						lowerCAmelCase_				,	lowerCAmelCase_   :							List[Any]									=			self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase_   :							Any									=			True

						if model_class in get_values(a_	):
								continue

						lowerCAmelCase_   :							List[str]									=			model_class(a_	)
						model.to(a_	)
						model.train()
						lowerCAmelCase_   :							Optional[Any]									=			self._prepare_for_class(a_						,			a_						,			return_labels=a_	)
						lowerCAmelCase_   :							Optional[Any]									=			model(**a_	).loss
						loss.backward()





		def    lowerCamelCase       (   self							:	List[Any]	):
				for model_class in self.all_model_classes:
						if model_class.__name__ == "DPTForDepthEstimation":
								continue

						lowerCAmelCase_				,	lowerCAmelCase_   :							str									=			self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase_   :							Tuple									=			False
						lowerCAmelCase_   :							int									=			True

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





		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Dict									=			self.model_tester.prepare_config_and_inputs_for_common()

				lowerCAmelCase_   :							Dict									=			_config_zero_init(a_	)
				for model_class in self.all_model_classes:
						lowerCAmelCase_   :							Optional[Any]									=			model_class(config=a_	)
						# Skip the check for the backbone
						lowerCAmelCase_   :							List[str]									=			[]
						for name, module in model.named_modules():
								if module.__class__.__name__ == "DPTViTHybridEmbeddings":
										lowerCAmelCase_   :							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							:	Optional[Any]	):
				for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
						lowerCAmelCase_   :							str									=			DPTModel.from_pretrained(a_	)
						self.assertIsNotNone(a_	)





		def    lowerCamelCase       (   self							:	Dict	):
				# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
				lowerCAmelCase_				,	lowerCAmelCase_   :							Any									=			self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase_   :							Union[str, Any]									=			"add"
				with self.assertRaises(a_	):
						lowerCAmelCase_   :							Dict									=			DPTForDepthEstimation(a_	)







def 		__lowerCamelCase	(  )						->    str:

		"""simple docstring"""







		lowerCAmelCase_   :							Dict									=			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							:	int	):
				lowerCAmelCase_   :							Dict									=			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
				lowerCAmelCase_   :							Tuple									=			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(a_	)

				lowerCAmelCase_   :							Optional[int]									=			prepare_img()
				lowerCAmelCase_   :							List[str]									=			image_processor(images=a_						,			return_tensors="pt"	).to(a_	)

				# forward pass
				with torch.no_grad():
						lowerCAmelCase_   :							Optional[Any]									=			model(**a_	)
						lowerCAmelCase_   :							Optional[int]									=			outputs.predicted_depth

				# verify the predicted depth
				lowerCAmelCase_   :							Union[str, Any]									=			torch.Size((1, 3_84, 3_84)	)
				self.assertEqual(predicted_depth.shape						,			a_	)

				lowerCAmelCase_   :							Optional[Any]									=			torch.tensor(
				    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]	).to(a_	)

				self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00						,			a_						,			atol=1e-4	)	)








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowercase  							=					{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					["GPTNeoXTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTNeoXForCausalLM",
	    "GPTNeoXForQuestionAnswering",
	    "GPTNeoXForSequenceClassification",
	    "GPTNeoXForTokenClassification",
	    "GPTNeoXLayer",
	    "GPTNeoXModel",
	    "GPTNeoXPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





