

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 __A    	=			None


__A    	=			logging.get_logger(__name__)

__A    	=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A    	=			{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

__A    	=			{
    '''facebook/xglm-564M''': 20_48,
}



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	str		   =       VOCAB_FILES_NAMES
      UpperCamelCase_    :	Optional[Any]		   =       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_    :	Tuple		   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_    :	Optional[int]		   =       ["input_ids", "attention_mask"]
      UpperCamelCase_    :	Dict		   =       XGLMTokenizer



      def __init__(    self   :       Optional[Any]    ,      A__   :       List[Any]=None    ,      A__   :       int=None    ,      A__   :       Union[str, Any]="<s>"    ,      A__   :       List[str]="</s>"    ,      A__   :       Optional[int]="</s>"    ,      A__   :       List[Any]="<s>"    ,      A__   :       List[Any]="<unk>"    ,      A__   :       List[Any]="<pad>"    ,      **A__   :       List[str]    ,      )					->  Tuple:
        # Compatibility with the original tokenizer
        _snake_case		=							7
        _snake_case		=							[f"""<madeupword{i}>""" for i in range(self.num_madeup_words	)]

        _snake_case		=							kwargs.get('''additional_special_tokens'''    ,      []	)
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            A__    ,      tokenizer_file=A__    ,      bos_token=A__    ,      eos_token=A__    ,      sep_token=A__    ,      cls_token=A__    ,      unk_token=A__    ,      pad_token=A__    ,      **A__    ,      )

        _snake_case		=							vocab_file
        _snake_case		=							False if not self.vocab_file else True



      def 		UpperCamelCase_	(    self   :       Tuple    ,      A__   :       List[int]    ,      A__   :       Optional[List[int]] = None	)					->  List[int]:

        if token_ids_a is None:
          return [self.sep_token_id] + token_ids_a
        _snake_case		=							[self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a



      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       List[int]    ,      A__   :       Optional[List[int]] = None	)					->  List[int]:
        _snake_case		=							[self.sep_token_id]

        if token_ids_a is None:
          return len(sep + token_ids_a	) * [0]
        return len(sep + token_ids_a + sep + sep + token_ids_a	) * [0]







      def 		UpperCamelCase_	(    self   :       str    ,      A__   :       str    ,      A__   :       Optional[str] = None	)					->  Tuple[str]:
        if not self.can_save_slow_tokenizer:
          raise ValueError(
              '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
              '''tokenizer.'''	)

        if not os.path.isdir(A__	):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""	)
          return
        _snake_case		=							os.path.join(
            A__    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

        if os.path.abspath(self.vocab_file	) != os.path.abspath(A__	):
          copyfile(self.vocab_file    ,      A__	)

        return (out_vocab_file,)




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






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

  _snake_case		=							pt_attn_layer.out_proj.weight
  _snake_case		=							pt_attn_layer.out_proj.bias

  _snake_case		=							q_proj
  _snake_case		=							q_proj_bias

  _snake_case		=							k_proj
  _snake_case		=							k_proj_bias

  _snake_case		=							v_proj
  _snake_case		=							v_proj_bias

  _snake_case		=							out_proj_weights
  _snake_case		=							out_proj_bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=							pt_model.token_embedding.weight
  _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    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _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
  _snake_case		=							pt_model.visual.conva.weight.data
  _snake_case		=							pt_model.visual.class_embedding
  _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    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






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

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

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

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							pt_model(_UpperCamelCase				, _UpperCamelCase  )

  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )
  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )

  hf_model.save_pretrained(_UpperCamelCase  )


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

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

