

"""simple docstring"""
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel







def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					int     , lowerCamelCase							:					List[str]   )						->     List[Any]:



       """simple docstring"""







       __magic_name__       ,							__magic_name__       ,							__magic_name__			:		str        =     pt_weights[f"""{prefix}.in_proj_weight"""].chunk(3     , dim=0   )
       __magic_name__       ,							__magic_name__       ,							__magic_name__			:		List[Any]        =     pt_weights[f"""{prefix}.in_proj_bias"""].chunk(3     , dim=0   )

       __magic_name__			:		Optional[int]        =     pt_weights[f"""{prefix}.out_proj.weight"""]
       __magic_name__			:		List[str]        =     pt_weights[f"""{prefix}.out_proj.bias"""]

       __magic_name__			:		str        =     q_proj
       __magic_name__			:		Optional[int]        =     q_proj_bias

       __magic_name__			:		List[str]        =     k_proj
       __magic_name__			:		Dict        =     k_proj_bias

       __magic_name__			:		List[str]        =     v_proj
       __magic_name__			:		int        =     v_proj_bias

       __magic_name__			:		Optional[int]        =     out_proj_weights
       __magic_name__			:		List[Any]        =     out_proj_bias







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					int     , lowerCamelCase							:					List[str]   )						->     List[Any]:



       """simple docstring"""







       copy_linear(hf_mlp.fca     , lowerCamelCase     , f"""{prefix}.c_fc"""   )
       copy_linear(hf_mlp.fca     , lowerCamelCase     , f"""{prefix}.c_proj"""   )







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					List[Any]   )						->     List[str]:



       """simple docstring"""







       __magic_name__			:		Optional[Any]        =     pt_weights[f"""{prefix}.weight"""].data
       __magic_name__			:		List[str]        =     pt_weights[f"""{prefix}.bias"""].data







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					Optional[Any]   )						->     int:



       """simple docstring"""







       copy_linear(hf_layer.layer_norma     , lowerCamelCase     , f"""{prefix}.ln_1"""   )
       copy_linear(hf_layer.layer_norma     , lowerCamelCase     , f"""{prefix}.ln_2"""   )

       # copy MLP
       copy_mlp(hf_layer.mlp     , lowerCamelCase     , f"""{prefix}.mlp"""   )

       # copy attn
       copy_attn_layer(hf_layer.self_attn     , lowerCamelCase     , f"""{prefix}.attn"""   )







def       UpperCamelCase_					(     lowerCamelCase							:					List[Any]     , lowerCamelCase							:					List[str]     , lowerCamelCase							:					Tuple   )						->     List[str]:



       """simple docstring"""







       for layer_id, hf_layer in enumerate(lowerCamelCase   ):
              copy_layer(lowerCamelCase     , lowerCamelCase     , f"""{prefix}.{layer_id}"""   )







def       UpperCamelCase_					(     lowerCamelCase							:					Any     , lowerCamelCase							:					Union[str, Any]   )						->     Union[str, Any]:



       """simple docstring"""







       __magic_name__			:		Optional[Any]        =     pt_weights['''text_projection'''].data.T

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







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple     , lowerCamelCase							:					List[Any]   )						->     List[str]:



       """simple docstring"""







       __magic_name__			:		int        =     pt_weights['''visual.proj'''].data.T

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

       # copy embeddings
       __magic_name__			:		int        =     pt_weights['''visual.conv1.weight'''].data
       __magic_name__			:		Union[str, Any]        =     pt_weights['''visual.class_embedding'''].data
       __magic_name__			:		List[str]        =     pt_weights['''visual.positional_embedding'''].data

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







@torch.no_grad()
def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					Any     , lowerCamelCase							:					Any=None   )						->     List[str]:



       """simple docstring"""







       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       __magic_name__			:		Optional[int]        =     ChineseCLIPConfig.from_pretrained(lowerCamelCase   )

       __magic_name__			:		List[str]        =     ChineseCLIPModel(lowerCamelCase   ).eval()

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

       copy_text_model_and_projection(lowerCamelCase     , lowerCamelCase   )
       copy_vision_model_and_projection(lowerCamelCase     , lowerCamelCase   )
       __magic_name__			:		List[Any]        =     pt_weights['''logit_scale'''].data

       hf_model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						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."""
 )
 A									=						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"""
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os

class        _UpperCamelCase	:


  """simple docstring"""





  snake_case_        =    'block_{0}.dat'



  def __init__( self					: int       ,       snake_case					: int      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     filename
         __magic_name__			:		Any        =     []



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: Any      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     self.BLOCK_FILENAME_FORMAT.format(snake_case      )
         with open(snake_case       ,       '''w'''      ) as file:
                file.write(snake_case      )
         self.block_filenames.append(snake_case      )



  def 							_UpperCAmelCase      ( self					: str      )	->			List[str]:


         '''simple docstring'''





         return self.block_filenames



  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: Dict       ,       snake_case					: int=None      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     0
         with open(self.filename      ) as file:
                while True:
                       __magic_name__			:		Tuple        =     file.readlines(snake_case      )

                       if lines == []:
                              break

                       if sort_key is None:
                              lines.sort()
                       else:
                              lines.sort(key=snake_case      )

                       self.write_block(''''''.join(snake_case      )       ,       snake_case      )
                       i += 1




  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Optional[int]:


         '''simple docstring'''





         map(os.remove       ,       self.block_filenames      )

class        _UpperCamelCase	:


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Dict      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Any        =     -1
         __magic_name__			:		Union[str, Any]        =     None

         for i in range(len(snake_case      )      ):
                if min_str is None or choices[i] < min_str:
                       __magic_name__			:		Dict        =     i

         return min_index

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: Optional[int]      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Dict        =     files
         __magic_name__			:		List[str]        =     set()
         __magic_name__			:		Any        =     len(snake_case      )
         __magic_name__			:		int        =     {i: None for i in range(self.num_buffers      )}



  def 							_UpperCAmelCase      ( self					: List[str]      )	->			Optional[Any]:


         '''simple docstring'''





         return {
             i: self.buffers[i] for i in range(self.num_buffers      ) if i not in self.empty
         }



  def 							_UpperCAmelCase      ( self					: int      )	->			int:


         '''simple docstring'''





         for i in range(self.num_buffers      ):
                if self.buffers[i] is None and i not in self.empty:
                       __magic_name__			:		int        =     self.files[i].readline()

                       if self.buffers[i] == "":
                              self.empty.add(snake_case      )
                              self.files[i].close()

         if len(self.empty      ) == self.num_buffers:
                return False

         return True




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Optional[Any]      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.buffers[index]
         __magic_name__			:		Dict        =     None

         return value

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: Any       ,       snake_case					: str      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     merge_strategy



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: int       ,       snake_case					: Dict       ,       snake_case					: Dict      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     FilesArray(self.get_file_handles(snake_case       ,       snake_case      )      )
         with open(snake_case       ,       '''w'''       ,       snake_case      ) as outfile:
                while buffers.refresh():
                       __magic_name__			:		List[str]        =     self.merge_strategy.select(buffers.get_dict()      )
                       outfile.write(buffers.unshift(snake_case      )      )




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: List[str]       ,       snake_case					: Optional[int]      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		str        =     {}

         for i in range(len(snake_case      )      ):
                __magic_name__			:		Any        =     open(filenames[i]       ,       '''r'''       ,       snake_case      )  # noqa: UP015

         return files

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: Optional[Any]       ,       snake_case					: Union[str, Any]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		int        =     block_size



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Optional[int]       ,       snake_case					: Dict=None      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.get_number_blocks(snake_case       ,       self.block_size      )
         __magic_name__			:		str        =     FileSplitter(snake_case      )
         splitter.split(self.block_size       ,       snake_case      )

         __magic_name__			:		int        =     FileMerger(NWayMerge()      )
         __magic_name__			:		Any        =     self.block_size / (num_blocks + 1)
         merger.merge(splitter.get_block_filenames()       ,       filename + '''.out'''       ,       snake_case      )

         splitter.cleanup()




  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: List[str]       ,       snake_case					: Union[str, Any]      )	->			Tuple:


         '''simple docstring'''





         return (os.stat(snake_case      ).st_size / block_size) + 1







def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]   )						->     Union[str, Any]:



       """simple docstring"""







       if string[-1].lower() == "k":
              return int(string[:-1]   ) * 1024
       elif string[-1].lower() == "m":
              return int(string[:-1]   ) * 1024 * 1024
       elif string[-1].lower() == "g":
              return int(string[:-1]   ) * 1024 * 1024 * 1024
       else:
              return int(lowerCamelCase   )







def       UpperCamelCase_					(     )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     argparse.ArgumentParser()
       parser.add_argument(
           '''-m'''     , '''--mem'''     , help='''amount of memory to use for sorting'''     , default='''100M'''   )
       parser.add_argument(
           '''filename'''     , metavar='''<filename>'''     , nargs=1     , help='''name of file to sort'''   )
       __magic_name__			:		List[str]        =     parser.parse_args()

       __magic_name__			:		Any        =     ExternalSort(parse_memory(args.mem   )   )
       sorter.sort(args.filename[0]   )


if __name__ == "__main__":
 main()
