
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        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Any							,			snake_case__				:			List[Any]							,			snake_case__				:			str=2							,			snake_case__				:			str=32							,			snake_case__				:			List[str]=16							,			snake_case__				:			List[Any]=3							,			snake_case__				:			Dict=True							,			snake_case__				:			Optional[Any]=True							,			snake_case__				:			int=32							,			snake_case__				:			Optional[int]=4							,			snake_case__				:			List[str]=[0, 1, 2, 3]							,			snake_case__				:			Optional[int]=4							,			snake_case__				:			Any=37							,			snake_case__				:			Union[str, Any]="gelu"							,			snake_case__				:			int=0.1							,			snake_case__				:			Optional[Any]=0.1							,			snake_case__				:			int=0.02							,			snake_case__				:			int=3							,			snake_case__				:			str=False							,			snake_case__				:			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				=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        _a					(      self				:			Dict ):
			"""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        _a					(      self				:			str ):
			"""simple docstring"""





			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=snake_case__							,			initializer_range=self.initializer_range							,			is_hybrid=self.is_hybrid							,			)







	def        _a					(      self				:			Tuple							,			snake_case__				:			Optional[Any]							,			snake_case__				:			int							,			snake_case__				:			Dict ):
			"""simple docstring"""





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







	def        _a					(      self				:			int							,			snake_case__				:			List[str]							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





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







	def        _a					(      self				:			Any							,			snake_case__				:			Optional[int]							,			snake_case__				:			Tuple							,			snake_case__				:			Optional[Any] ):
			"""simple docstring"""





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







	def        _a					(      self				:			Optional[int] ):
			"""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        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__						,						unittest.TestCase	):

	"""simple docstring"""

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

	_A  =		False
	_A  =		False
	_A  =		False







	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			A				=DPTModelTester(self )
			A				=ConfigTester(self							,			config_class=snake_case__							,			has_text_modality=snake_case__							,			hidden_size=37 )







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			self.config_tester.run_common_tests()







	@unittest.skip(reason="DPT does not use inputs_embeds" )
	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			pass







	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			A			,    A				=self.model_tester.prepare_config_and_inputs_for_common()

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







	def        _a					(      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(snake_case__ )
					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]							,			snake_case__ )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			A				=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*snake_case__ )







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			A				=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*snake_case__ )







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			A				=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__ )







	def        _a					(      self				:			Any ):
			"""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(snake_case__ ):
							continue

					A				=model_class(snake_case__ )
					model.to(snake_case__ )
					model.train()
					A				=self._prepare_for_class(snake_case__							,			snake_case__							,			return_labels=snake_case__ )
					A				=model(**snake_case__ ).loss
					loss.backward()







	def        _a					(      self				:			Optional[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				=False
					A				=True

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







	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			A			,    A				=self.model_tester.prepare_config_and_inputs_for_common()

			A				=_config_zero_init(snake_case__ )
			for model_class in self.all_model_classes:
					A				=model_class(config=snake_case__ )
					# 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        _a					(      self				:			int ):
			"""simple docstring"""





			pass







	@slow
	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





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


def    UpperCamelCase_ (	)     ->Any:
		A				=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
		return image






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

	"""simple docstring"""

	def        _a					(      self				:			Any ):
			"""simple docstring"""





			A				=DPTImageProcessor.from_pretrained("Intel/dpt-large" )
			A				=DPTForDepthEstimation.from_pretrained("Intel/dpt-large" ).to(snake_case__ )

			A				=prepare_img()
			A				=image_processor(images=snake_case__							,			return_tensors="pt" ).to(snake_case__ )

			# forward pass
			with torch.no_grad():
					A				=model(**snake_case__ )
					A				=outputs.predicted_depth

			# verify the predicted depth
			A				=torch.Size((1, 3_84, 3_84) )
			self.assertEqual(predicted_depth.shape							,			snake_case__ )

			A				=torch.tensor(
			    [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] ).to(snake_case__ )

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







	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			A				=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
			A				=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(snake_case__ )

			A				=prepare_img()
			A				=image_processor(images=snake_case__							,			return_tensors="pt" ).to(snake_case__ )

			# forward pass
			with torch.no_grad():
					A				=model(**snake_case__ )

			# verify the logits
			A				=torch.Size((1, 1_50, 4_80, 4_80) )
			self.assertEqual(outputs.logits.shape							,			snake_case__ )

			A				=torch.tensor(
			    [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] ).to(snake_case__ )

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







	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			A				=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
			A				=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(snake_case__ )

			A				=prepare_img()
			A				=image_processor(images=snake_case__							,			return_tensors="pt" ).to(snake_case__ )

			# forward pass
			with torch.no_grad():
					A				=model(**snake_case__ )

			A				=outputs.logits.detach().cpu()

			A				=image_processor.post_process_semantic_segmentation(outputs=snake_case__							,			target_sizes=[(5_00, 3_00)] )
			A				=torch.Size((5_00, 3_00) )
			self.assertEqual(segmentation[0].shape							,			snake_case__ )

			A				=image_processor.post_process_semantic_segmentation(outputs=snake_case__ )
			A				=torch.Size((4_80, 4_80) )
			self.assertEqual(segmentation[0].shape							,			snake_case__ )






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]  ,				a_  ,				a_		)
								module_index += 1

				if hasattr(a_  ,				"downsamplers"		) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down  ,				a_  ,				a_		)
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				a_  ,				a_		)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				a_  ,				a_		)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				a_  ,				a_		)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]  ,				a_  ,				a_		)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(a_  ,				"resnet_up"		) and block.resnet_up is not None:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
	    type=str,
	    required=False,
	    help="""Path to the checkpoint to convert.""",
	)

	parser.add_argument(
	    """--config_file""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
	    type=str,
	    required=False,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument(
	    """--dump_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
	    type=str,
	    required=False,
	    help="""Path to the output model.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

	with open(args.config_file) as f:
		__a							  =						json.loads(f.read())

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	__a							  =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__a							  =						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		__a							  =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)




