


'''simple docstring'''



import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Optional[int]     ):
					__a					,       __a					,       __a    : Optional[int]      					=						pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3		,   dim=0     )
					__a					,       __a					,       __a    : List[str]      					=						pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3		,   dim=0     )

					__a    : Union[str, Any]      					=						pt_weights[F"""{prefix}.out_proj.weight"""]
					__a    : str      					=						pt_weights[F"""{prefix}.out_proj.bias"""]

					__a    : Dict      					=						q_proj
					__a    : Optional[int]      					=						q_proj_bias

					__a    : List[Any]      					=						k_proj
					__a    : Union[str, Any]      					=						k_proj_bias

					__a    : Union[str, Any]      					=						v_proj
					__a    : Optional[Any]      					=						v_proj_bias

					__a    : Optional[int]      					=						out_proj_weights
					__a    : Tuple      					=						out_proj_bias






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Any     ):
					copy_linear(hf_mlp.fca		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.c_fc"""     )
					copy_linear(hf_mlp.fca		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.c_proj"""     )






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Any     ):
					__a    : Tuple      					=						pt_weights[F"""{prefix}.weight"""].data
					__a    : int      					=						pt_weights[F"""{prefix}.bias"""].data






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
					# copy layer norms
					copy_linear(hf_layer.layer_norma		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.ln_1"""     )
					copy_linear(hf_layer.layer_norma		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.ln_2"""     )

					# copy MLP
					copy_mlp(hf_layer.mlp		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.mlp"""     )

					# copy attn
					copy_attn_layer(hf_layer.self_attn		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.attn"""     )






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Optional[int]     ):
					for layer_id, hf_layer in enumerate(_SCREAMING_SNAKE_CASE     ):
										copy_layer(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   F"""{prefix}.{layer_id}"""     )






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]     ):
					# copy projection
					__a    : Optional[Any]      					=						pt_weights['text_projection'].data.T

					# copy text encoder
					for name, param in hf_model.text_model.named_parameters():
										__a    : str      					=						pt_weights[F"""bert.{name}"""].data






def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
					# copy projection
					__a    : Any      					=						pt_weights['visual.proj'].data.T

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

					# copy embeddings
					__a    : int      					=						pt_weights['visual.conv1.weight'].data
					__a    : Dict      					=						pt_weights['visual.class_embedding'].data
					__a    : List[Any]      					=						pt_weights['visual.positional_embedding'].data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers		,   _SCREAMING_SNAKE_CASE		,   'visual.transformer.resblocks'     )






@torch.no_grad()
def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
					assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
					__a    : int      					=						ChineseCLIPConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )

					__a    : int      					=						ChineseCLIPModel(_SCREAMING_SNAKE_CASE     ).eval()

					__a    : Any      					=						torch.load(_SCREAMING_SNAKE_CASE		,   map_location='cpu'     )['state_dict']
					__a    : Union[str, Any]      					=						{(name[7:] if name.startswith('module.'     ) else name): value for name, value in pt_weights.items()}

					copy_text_model_and_projection(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
					copy_vision_model_and_projection(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
					__a    : Any      					=						pt_weights['logit_scale'].data

					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
	__lowercase			:		Optional[int] =			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			:		Optional[int] =			parser.parse_args()

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







'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	A_			    =					(KDPMaAncestralDiscreteScheduler,)
	A_			    =					10




	def    __UpperCAmelCase							(     self		,       **__a							):

						'''simple docstring'''





						__a    : Dict      					=						{
						    'num_train_timesteps': 1100,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__a							)
						return config




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
											self.check_over_configs(beta_start=__a		,       beta_end=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : Dict      					=						self.scheduler_classes[0]
						__a    : Tuple      					=						self.get_scheduler_config()
						__a    : Optional[int]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a    : int      					=						torch.manual_seed(0							)

						__a    : Dict      					=						self.dummy_model()
						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
						__a    : int      					=						sample.to(__a							)

						for i, t in enumerate(scheduler.timesteps							):
											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : int      					=						model(__a		,       __a							)

											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : int      					=						output.prev_sample

						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 13849.3877							) < 1E-2
						assert abs(result_mean.item() - 18.0331							) < 5E-3




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : Dict      					=						self.scheduler_classes[0]
						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
						__a    : List[str]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a    : Dict      					=						self.dummy_model()
						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
						__a    : List[str]      					=						sample.to(__a							)

						__a    : Tuple      					=						torch.manual_seed(0							)

						for i, t in enumerate(scheduler.timesteps							):
											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : Optional[int]      					=						model(__a		,       __a							)

											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : Union[str, Any]      					=						output.prev_sample

						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 328.9970							) < 1E-2
						assert abs(result_mean.item() - 0.4284							) < 1E-3





	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : List[str]      					=						self.scheduler_classes[0]
						__a    : Union[str, Any]      					=						self.get_scheduler_config()
						__a    : List[Any]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
						__a    : Union[str, Any]      					=						torch.manual_seed(0							)

						__a    : List[str]      					=						self.dummy_model()
						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : str      					=						model(__a		,       __a							)

											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : Any      					=						output.prev_sample

						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
						__a    : str      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 13849.3818							) < 1E-1
						assert abs(result_mean.item() - 18.0331							) < 1E-3



