



"""simple docstring"""






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			pt_attn_layer.in_proj_weight.chunk(3       ,							dim=0    )
  _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			pt_attn_layer.in_proj_bias.chunk(3       ,							dim=0    )

  _SCREAMING_SNAKE_CASE        =			pt_attn_layer.out_proj.weight
  _SCREAMING_SNAKE_CASE        =			pt_attn_layer.out_proj.bias

  _SCREAMING_SNAKE_CASE        =			q_proj
  _SCREAMING_SNAKE_CASE        =			q_proj_bias

  _SCREAMING_SNAKE_CASE        =			k_proj
  _SCREAMING_SNAKE_CASE        =			k_proj_bias

  _SCREAMING_SNAKE_CASE        =			v_proj
  _SCREAMING_SNAKE_CASE        =			v_proj_bias

  _SCREAMING_SNAKE_CASE        =			out_proj_weights
  _SCREAMING_SNAKE_CASE        =			out_proj_bias


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  copy_linear(hf_mlp.fca       ,							pt_mlp.c_fc    )
  copy_linear(hf_mlp.fca       ,							pt_mlp.c_proj    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_linear.weight
  _SCREAMING_SNAKE_CASE        =			pt_linear.bias


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  copy_linear(hf_layer.layer_norma       ,							pt_layer.ln_a    )
  copy_linear(hf_layer.layer_norma       ,							pt_layer.ln_a    )

  # copy MLP
  copy_mlp(hf_layer.mlp       ,							pt_layer.mlp    )

  # copy attn
  copy_attn_layer(hf_layer.self_attn       ,							pt_layer.attn    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  for hf_layer, pt_layer in zip(_A       ,							_A    ):
    copy_layer(_A       ,							_A    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_model.token_embedding.weight
  _SCREAMING_SNAKE_CASE        =			pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm       ,							pt_model.ln_final    )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers       ,							pt_model.transformer.resblocks    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model       ,							_A    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			pt_model.visual.proj.data.T

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

  # copy embeds
  _SCREAMING_SNAKE_CASE        =			pt_model.visual.conva.weight.data
  _SCREAMING_SNAKE_CASE        =			pt_model.visual.class_embedding
  _SCREAMING_SNAKE_CASE        =			pt_model.visual.positional_embedding.data

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


@torch.no_grad()
def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None    ):


  '''simple docstring'''


  if config_path is not None:
    _SCREAMING_SNAKE_CASE        =			CLIPConfig.from_pretrained(_A    )
  else:
    _SCREAMING_SNAKE_CASE        =			CLIPConfig(projection_dim=512       ,							text_config={}       ,							vision_config={}    )

  _SCREAMING_SNAKE_CASE        =			CLIPModel(_A    ).eval()

  _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			load(_A       ,							device='''cpu'''       ,							jit=_A    )
  _SCREAMING_SNAKE_CASE        =			pt_model.eval()

  copy_text_model_and_projection(_A       ,							_A    )
  copy_vison_model_and_projection(_A       ,							_A    )
  _SCREAMING_SNAKE_CASE        =			pt_model.logit_scale

  _SCREAMING_SNAKE_CASE        =			torch.arange(0       ,							77    ).unsqueeze(0    )
  _SCREAMING_SNAKE_CASE        =			torch.randn(1       ,							3       ,							224       ,							224    )

  _SCREAMING_SNAKE_CASE        =			hf_model(input_ids=_A       ,							pixel_values=_A       ,							return_dict=_A    )
  _SCREAMING_SNAKE_CASE        =			hf_outputs.logits_per_image
  _SCREAMING_SNAKE_CASE        =			hf_outputs.logits_per_text
  _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			pt_model(_A       ,							_A    )

  assert torch.allclose(_A       ,							_A       ,							atol=1e-3    )
  assert torch.allclose(_A       ,							_A       ,							atol=1e-3    )

  hf_model.save_pretrained(_A    )


if __name__ == "__main__":
    lowerCamelCase				:     List[Any]								=  argparse.ArgumentParser()
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    lowerCamelCase				:     List[str]								=  parser.parse_args()

    convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






from __future__ import annotations

import sys
class 							__lowercase   :



     def __init__(  self   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							int       ):
       a_		    =  letter
       a_		    =  freq
       a_		    =  {}



     def __repr__(  self   :							List[Any]       ):
       return f"{self.letter}:{self.freq}"



class 							__lowercase   :



     def __init__(  self   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							Letter | TreeNode      ,		lowercase__   :							Letter | TreeNode       ):
       a_		    =  freq
       a_		    =  left
       a_		    =  right






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  {}
  with open(_A			) as f:
    while True:
      a_		    =  f.read(1			)
      if not c:
        break
      a_		    =  chars[c] + 1 if c in chars else 1
  return sorted((Letter(_A      ,      _A			) for c, f in chars.items())      ,      key=lambda _A			: x.freq			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  letters  # type: ignore
  while len(_A			) > 1:
    a_		    =  response.pop(0			)
    a_		    =  response.pop(0			)
    a_		    =  left.freq + right.freq
    a_		    =  TreeNode(_A      ,      _A      ,      _A			)
    response.append(_A			)
    response.sort(key=lambda _A			: x.freq			)
  return response[0]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if isinstance(_A      ,      _A			):
    a_		    =  bitstring
    return [root]
  a_		    =  root  # type: ignore
  a_		    =  []
  letters += traverse_tree(treenode.left      ,      bitstring + '''0'''			)
  letters += traverse_tree(treenode.right      ,      bitstring + '''1'''			)
  return letters






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  parse_file(_A			)
  a_		    =  build_tree(_A			)
  a_		    =  {
      k: v for letter in traverse_tree(_A      ,      ''''''			) for k, v in letter.bitstring.items()
  }
  print(f"Huffman Coding  of {file_path}: "			)
  with open(_A			) as f:
    while True:
      a_		    =  f.read(1			)
      if not c:
        break
      print(letters[c]      ,      end=''' '''			)
  print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])
