

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

lowerCamelCase 	=   {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

lowerCamelCase 	=   {
    '''Salesforce/codegen-350M-mono''': 2048,
}




@lru_cache()
def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =(
      list(range(ord('''!'''				)      , ord('''~'''				) + 1				)				) + list(range(ord('''¡'''				)      , ord('''¬'''				) + 1				)				) + list(range(ord('''®'''				)      , ord('''ÿ'''				) + 1				)				)
  )
  a__    =bs[:]
  a__    =0
  for b in range(2**8				):
    if b not in bs:
      bs.append(_A				)
      cs.append(2**8 + n				)
      n += 1
  a__    =[chr(_A				) for n in cs]
  return dict(zip(_A      , _A				)				)




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  a__    =set()
  a__    =word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)				)
    a__    =char
  return pairs







class 		__magic_name__     (				lowerCAmelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      List[str]        =  VOCAB_FILES_NAMES
   lowerCamelCase__					:      List[str]        =  PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__					:      Any        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__					:      Any        =  ['input_ids', 'attention_mask']



   def __init__(							self,       lowercase_,       lowercase_,       lowercase_="replace",       lowercase_="<|endoftext|>",       lowercase_="<|endoftext|>",       lowercase_="<|endoftext|>",       lowercase_=None,       lowercase_=False,       lowercase_=False,       **lowercase_,       )				->       Optional[Any]:






     """simple docstring"""






     a__    =AddedToken(lowerCAmelCase__,       lstrip=lowerCAmelCase__,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__,       lowerCAmelCase__			) else bos_token
     a__    =AddedToken(lowerCAmelCase__,       lstrip=lowerCAmelCase__,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__,       lowerCAmelCase__			) else eos_token
     a__    =AddedToken(lowerCAmelCase__,       lstrip=lowerCAmelCase__,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__,       lowerCAmelCase__			) else unk_token
     a__    =AddedToken(lowerCAmelCase__,       lstrip=lowerCAmelCase__,       rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__,       lowerCAmelCase__			) else pad_token
     super().__init__(
         errors=lowerCAmelCase__,       unk_token=lowerCAmelCase__,       bos_token=lowerCAmelCase__,       eos_token=lowerCAmelCase__,       pad_token=lowerCAmelCase__,       add_prefix_space=lowerCAmelCase__,       add_bos_token=lowerCAmelCase__,       **lowerCAmelCase__,       )
     a__    =add_bos_token

     with open(lowerCAmelCase__,       encoding='''utf-8'''			) as vocab_handle:
       a__    =json.load(lowerCAmelCase__			)
     a__    ={v: k for k, v in self.encoder.items()}
     a__    =errors  # how to handle errors in decoding
     a__    =bytes_to_unicode()
     a__    ={v: k for k, v in self.byte_encoder.items()}
     with open(lowerCAmelCase__,       encoding='''utf-8'''			) as merges_handle:
       a__    =merges_handle.read().split('''\n'''			)[1:-1]
     a__    =[tuple(merge.split()			) for merge in bpe_merges]
     a__    =dict(zip(lowerCAmelCase__,       range(len(lowerCAmelCase__			)			)			)			)
     a__    ={}
     a__    =add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     a__    =re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''			)



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






     """simple docstring"""






     return len(self.encoder			)



   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     return dict(self.encoder,       **self.added_tokens_encoder			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Tuple:






     """simple docstring"""






     if token in self.cache:
       return self.cache[token]
     a__    =tuple(lowerCAmelCase__			)
     a__    =get_pairs(lowerCAmelCase__			)

     if not pairs:
       return token

     while True:
       a__    =min(lowerCAmelCase__,       key=lambda lowercase_			: self.bpe_ranks.get(lowerCAmelCase__,       float('''inf'''			)			)			)
       if bigram not in self.bpe_ranks:
         break
       a__, a__    =bigram
       a__    =[]
       a__    =0
       while i < len(lowerCAmelCase__			):
         try:
           a__    =word.index(lowerCAmelCase__,       lowerCAmelCase__			)
         except ValueError:
           new_word.extend(word[i:]			)
           break
         else:
           new_word.extend(word[i:j]			)
           a__    =j

         if word[i] == first and i < len(lowerCAmelCase__			) - 1 and word[i + 1] == second:
           new_word.append(first + second			)
           i += 2
         else:
           new_word.append(word[i]			)
           i += 1
       a__    =tuple(lowerCAmelCase__			)
       a__    =new_word
       if len(lowerCAmelCase__			) == 1:
         break
       else:
         a__    =get_pairs(lowerCAmelCase__			)
     a__    =''' '''.join(lowerCAmelCase__			)
     a__    =word
     return word



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_=None			)				->       List[str]:






     """simple docstring"""






     if self.add_bos_token:
       a__    =[self.bos_token_id]
     else:
       a__    =[]

     a__    =bos_token_ids + token_ids_a

     if token_ids_a is None:
       return output

     return output + bos_token_ids + token_ids_a



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






     """simple docstring"""






     a__    =[]
     for token in re.findall(self.pat,       lowerCAmelCase__			):
       a__    =''''''.join(
           self.byte_encoder[b] for b in token.encode('''utf-8'''			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__			).split(''' '''			)			)
     return bpe_tokens



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       str:






     """simple docstring"""






     return self.encoder.get(lowerCAmelCase__,       self.encoder.get(self.unk_token			)			)



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






     """simple docstring"""






     return self.decoder.get(lowerCAmelCase__			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Any:






     """simple docstring"""






     a__    =''''''.join(lowerCAmelCase__			)
     a__    =bytearray([self.byte_decoder[c] for c in text]			).decode('''utf-8''',       errors=self.errors			)
     return text



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None			)				->       Tuple[str]:






     """simple docstring"""






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

     with open(lowerCAmelCase__,       '''w''',       encoding='''utf-8'''			) as f:
       f.write(json.dumps(self.encoder,       indent=2,       sort_keys=lowerCAmelCase__,       ensure_ascii=lowerCAmelCase__			) + '''\n'''			)

     a__    =0
     with open(lowerCAmelCase__,       '''w''',       encoding='''utf-8'''			) as writer:
       writer.write('''#version: 0.2\n'''			)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),       key=lambda lowercase_			: kv[1]			):
         if index != token_index:
           logger.warning(
               F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
               ''' Please check that the tokenizer is not corrupted!'''			)
           a__    =token_index
         writer.write(''' '''.join(lowerCAmelCase__			) + '''\n'''			)
         index += 1

     return vocab_file, merge_file



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_=False,       **lowercase_			)				->       List[Any]:






     """simple docstring"""






     a__    =kwargs.pop('''add_prefix_space''',       self.add_prefix_space			)
     if is_split_into_words or add_prefix_space:
       a__    =''' ''' + text
     return (text, kwargs)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = False,       lowercase_ = None,       lowercase_ = None,       **lowercase_,       )				->       str:






     """simple docstring"""






     a__    =super()._decode(
         token_ids=lowerCAmelCase__,       skip_special_tokens=lowerCAmelCase__,       clean_up_tokenization_spaces=lowerCAmelCase__,       **lowerCAmelCase__,       )

     if truncate_before_pattern is not None and len(lowerCAmelCase__			) > 0:
       a__    =self.truncate(lowerCAmelCase__,       lowerCAmelCase__			)

     return decoded_text



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






     """simple docstring"""






     def find_re(lowercase_,       lowercase_,       lowercase_			):
       a__    =pattern.search(lowerCAmelCase__,       lowerCAmelCase__			)
       return m.start() if m else -1

     a__    =[re.compile(lowerCAmelCase__,       re.MULTILINE			) for pattern in truncate_before_pattern]

     a__    =list(re.finditer('''^print''',       lowerCAmelCase__,       re.MULTILINE			)			)

     if len(lowerCAmelCase__			) > 1:
       a__    =completion[: prints[1].start()]

     a__    =list(re.finditer('''^def''',       lowerCAmelCase__,       re.MULTILINE			)			)

     if len(lowerCAmelCase__			) > 1:
       a__    =completion[: defs[1].start()]

     a__    =0

     a__    =[
         pos for pos in [find_re(lowerCAmelCase__,       lowerCAmelCase__,       lowerCAmelCase__			) for terminal in terminals] if pos != -1
     ]

     if len(lowerCAmelCase__			) > 0:
       return completion[: min(lowerCAmelCase__			)]
     else:
       return completion

"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # copy layer norms
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     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__":
    __UpperCamelCase										=     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''')
    __UpperCamelCase										=     parser.parse_args()

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

