


import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Optional[int]:







 """simple docstring"""


 _lowercase ,       _lowercase ,       _lowercase				 =pt_weights[F"{prefix}.in_proj_weight"].chunk(3	,		dim=0		)
 _lowercase ,       _lowercase ,       _lowercase				 =pt_weights[F"{prefix}.in_proj_bias"].chunk(3	,		dim=0		)

 _lowercase				 =pt_weights[F"{prefix}.out_proj.weight"]
 _lowercase				 =pt_weights[F"{prefix}.out_proj.bias"]

 _lowercase				 =q_proj
 _lowercase				 =q_proj_bias

 _lowercase				 =k_proj
 _lowercase				 =k_proj_bias

 _lowercase				 =v_proj
 _lowercase				 =v_proj_bias

 _lowercase				 =out_proj_weights
 _lowercase				 =out_proj_bias
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Tuple:







 """simple docstring"""


 copy_linear(hf_mlp.fca	,		__snake_case	,		F"{prefix}.c_fc"		)
 copy_linear(hf_mlp.fca	,		__snake_case	,		F"{prefix}.c_proj"		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Optional[int]:







 """simple docstring"""


 _lowercase				 =pt_weights[F"{prefix}.weight"].data
 _lowercase				 =pt_weights[F"{prefix}.bias"].data
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Dict:







 """simple docstring"""


 copy_linear(hf_layer.layer_norma	,		__snake_case	,		F"{prefix}.ln_1"		)
 copy_linear(hf_layer.layer_norma	,		__snake_case	,		F"{prefix}.ln_2"		)

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

 # copy attn
 copy_attn_layer(hf_layer.self_attn	,		__snake_case	,		F"{prefix}.attn"		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Tuple:







 """simple docstring"""


 for layer_id, hf_layer in enumerate(__snake_case		):
  copy_layer(__snake_case	,		__snake_case	,		F"{prefix}.{layer_id}"		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 _lowercase				 =pt_weights['''text_projection'''].data.T

 # copy text encoder
 for name, param in hf_model.text_model.named_parameters():
  _lowercase				 =pt_weights[F"bert.{name}"].data
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     Dict:







 """simple docstring"""


 _lowercase				 =pt_weights['''visual.proj'''].data.T

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

 # copy embeddings
 _lowercase				 =pt_weights['''visual.conv1.weight'''].data
 _lowercase				 =pt_weights['''visual.class_embedding'''].data
 _lowercase				 =pt_weights['''visual.positional_embedding'''].data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers	,		__snake_case	,		'''visual.transformer.resblocks'''		)
@torch.no_grad()
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case=None		)	->     List[str]:







 """simple docstring"""


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

 _lowercase				 =ChineseCLIPModel(__snake_case		).eval()

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

 copy_text_model_and_projection(__snake_case	,		__snake_case		)
 copy_vision_model_and_projection(__snake_case	,		__snake_case		)
 _lowercase				 =pt_weights['''logit_scale'''].data

 hf_model.save_pretrained(__snake_case		)


if __name__ == "__main__":
  UpperCAmelCase__        =				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.'''
  )
  UpperCAmelCase__        =				parser.parse_args()

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





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

      self.register_modules(unet=UpperCAmelCase						,	scheduler=UpperCAmelCase			)





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

      self.scheduler.set_timesteps(UpperCAmelCase			)
      for t in self.progress_bar(self.scheduler.timesteps			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

      _lowercase				 =(image / 2 + 0.5).clamp(0						,	1			)
      _lowercase				 =image.cpu().permute(0						,	2						,	3						,	1			).numpy()
      if output_type == "pil":
       _lowercase				 =self.numpy_to_pil(UpperCAmelCase			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

