



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 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=2	, lowerCamelCase_=32	, lowerCamelCase_=16	, lowerCamelCase_=3	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=32	, lowerCamelCase_=4	, lowerCamelCase_=[0, 1, 2, 3]	, lowerCamelCase_=4	, lowerCamelCase_=37	, lowerCamelCase_="gelu"	, lowerCamelCase_=0.1	, lowerCamelCase_=0.1	, lowerCamelCase_=0.02	, lowerCamelCase_=3	, lowerCamelCase_=False	, lowerCamelCase_=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   UpperCamelCase_ (self	):
						"""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	):
						"""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=lowerCamelCase_	, initializer_range=self.initializer_range	, is_hybrid=self.is_hybrid	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""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 			_lowercase							(     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   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     DPTModelTester(self	)
						a        =     ConfigTester(self	, config_class=lowerCamelCase_	, has_text_modality=lowerCamelCase_	, hidden_size=37	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	@unittest.skip(reason="DPT does not use inputs_embeds"	)
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a        =     model_class(lowerCamelCase_	)
											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]	, lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""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(lowerCamelCase_	):
																continue

											a        =     model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.train()
											a        =     self._prepare_for_class(lowerCamelCase_	, lowerCamelCase_	, return_labels=lowerCamelCase_	)
											a        =     model(**lowerCamelCase_	).loss
											loss.backward()




	def   UpperCamelCase_ (self	):
						"""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(lowerCamelCase_	) or not model_class.supports_gradient_checkpointing:
																continue
											a        =     model_class(lowerCamelCase_	)
											model.to(lowerCamelCase_	)
											model.gradient_checkpointing_enable()
											model.train()
											a        =     self._prepare_for_class(lowerCamelCase_	, lowerCamelCase_	, return_labels=lowerCamelCase_	)
											a        =     model(**lowerCamelCase_	).loss
											loss.backward()




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a  ,       a        =     self.model_tester.prepare_config_and_inputs_for_common()

						a        =     _config_zero_init(lowerCamelCase_	)
						for model_class in self.all_model_classes:
											a        =     model_class(config=lowerCamelCase_	)
											# 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	):
						"""simple docstring"""

						pass




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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






def 					a(   )	-> Tuple:
					"""simple docstring"""





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







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





	"""simple docstring"""




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     DPTImageProcessor.from_pretrained("Intel/dpt-large"	)
						a        =     DPTForDepthEstimation.from_pretrained("Intel/dpt-large"	).to(lowerCamelCase_	)

						a        =     prepare_img()
						a        =     image_processor(images=lowerCamelCase_	, return_tensors="pt"	).to(lowerCamelCase_	)

						# forward pass
						with torch.no_grad():
											a        =     model(**lowerCamelCase_	)
											a        =     outputs.predicted_depth

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

						a        =     torch.tensor(
						    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	).to(lowerCamelCase_	)

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
						a        =     DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(lowerCamelCase_	)

						a        =     prepare_img()
						a        =     image_processor(images=lowerCamelCase_	, return_tensors="pt"	).to(lowerCamelCase_	)

						# forward pass
						with torch.no_grad():
											a        =     model(**lowerCamelCase_	)

						# verify the logits
						a        =     torch.Size((1, 150, 480, 480)	)
						self.assertEqual(outputs.logits.shape	, lowerCamelCase_	)

						a        =     torch.tensor(
						    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	).to(lowerCamelCase_	)

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
						a        =     DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(lowerCamelCase_	)

						a        =     prepare_img()
						a        =     image_processor(images=lowerCamelCase_	, return_tensors="pt"	).to(lowerCamelCase_	)

						# forward pass
						with torch.no_grad():
											a        =     model(**lowerCamelCase_	)

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

						a        =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_	, target_sizes=[(500, 300)]	)
						a        =     torch.Size((500, 300)	)
						self.assertEqual(segmentation[0].shape	, lowerCamelCase_	)

						a        =     image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_	)
						a        =     torch.Size((480, 480)	)
						self.assertEqual(segmentation[0].shape	, lowerCamelCase_	)





import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 					a(   A :					Union[str, Any]				,  A :					List[str]				,  A :					Any )	-> Tuple:
					"""simple docstring"""





					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3				,  dim=0 )
					a  ,       a  ,       a        =     pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3				,  dim=0 )

					a        =     pt_weights[f'''{prefix}.out_proj.weight''']
					a        =     pt_weights[f'''{prefix}.out_proj.bias''']

					a        =     q_proj
					a        =     q_proj_bias

					a        =     k_proj
					a        =     k_proj_bias

					a        =     v_proj
					a        =     v_proj_bias

					a        =     out_proj_weights
					a        =     out_proj_bias






def 					a(   A :					List[str]				,  A :					List[Any]				,  A :					int )	-> List[Any]:
					"""simple docstring"""





					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_fc''' )
					copy_linear(hf_mlp.fca				,  A				,  f'''{prefix}.c_proj''' )






def 					a(   A :					Optional[Any]				,  A :					List[Any]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					a        =     pt_weights[f'''{prefix}.weight'''].data
					a        =     pt_weights[f'''{prefix}.bias'''].data






def 					a(   A :					int				,  A :					Optional[Any]				,  A :					List[Any] )	-> Union[str, Any]:
					"""simple docstring"""





					copy_linear(hf_layer.layer_norma				,  A				,  f'''{prefix}.ln_1''' )
					copy_linear(hf_layer.layer_norma				,  A				,  f'''{prefix}.ln_2''' )

					# copy MLP
					copy_mlp(hf_layer.mlp				,  A				,  f'''{prefix}.mlp''' )

					# copy attn
					copy_attn_layer(hf_layer.self_attn				,  A				,  f'''{prefix}.attn''' )






def 					a(   A :					str				,  A :					Optional[int]				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					for layer_id, hf_layer in enumerate(A ):
										copy_layer(A				,  A				,  f'''{prefix}.{layer_id}''' )






def 					a(   A :					Dict				,  A :					Union[str, Any] )	-> Optional[Any]:
					"""simple docstring"""





					a        =     pt_weights["text_projection"].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										a        =     pt_weights[f'''bert.{name}'''].data






def 					a(   A :					Optional[Any]				,  A :					Optional[Any] )	-> List[str]:
					"""simple docstring"""





					a        =     pt_weights["visual.proj"].data.T

					# copy layer norms
					copy_linear(hf_model.vision_model.pre_layrnorm				,  A				,  "visual.ln_pre" )
					copy_linear(hf_model.vision_model.post_layernorm				,  A				,  "visual.ln_post" )

					# copy embeddings
					a        =     pt_weights["visual.conv1.weight"].data
					a        =     pt_weights["visual.class_embedding"].data
					a        =     pt_weights["visual.positional_embedding"].data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers				,  A				,  "visual.transformer.resblocks" )






@torch.no_grad()
def 					a(   A :					str				,  A :					List[str]				,  A :					Union[str, Any]=None )	-> Tuple:
					"""simple docstring"""





					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					a        =     ChineseCLIPConfig.from_pretrained(A )

					a        =     ChineseCLIPModel(A ).eval()

					a        =     torch.load(A				,  map_location="cpu" )["state_dict"]
					a        =     {(name[7:] if name.startswith("module." ) else name): value for name, value in pt_weights.items()}

					copy_text_model_and_projection(A				,  A )
					copy_vision_model_and_projection(A				,  A )
					a        =     pt_weights["logit_scale"].data

					hf_model.save_pretrained(A )


if __name__ == "__main__":
				_lowercase:				List[Any]       =	argparse.ArgumentParser()
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    help="Path to the output folder storing converted hf PyTorch model.",
				)
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
				)
				parser.add_argument(
				    "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
				)
				_lowercase:				int       =	parser.parse_args()

				convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
				print("The conversion is finished!")
