

'''simple docstring'''

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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 lowercase__            =     None


lowercase__            =     logging.get_logger(__name__)

lowercase__            =     {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase__            =     {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__            =     {
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
lowercase__            =     ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      int    						=    VOCAB_FILES_NAMES
     UpperCAmelCase_		:      Dict    						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_		:      Optional[int]    						=    PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_		:      List[str]    						=    ["""input_ids""", """attention_mask"""]
     UpperCAmelCase_		:      Tuple    						=    MBartaaTokenizer

     UpperCAmelCase_		:      List[int]    						=    []
     UpperCAmelCase_		:      List[int]    						=    []


     def __init__(						self					:	Tuple	,     lowercase_					:	List[str]=None	,     lowercase_					:	Union[str, Any]=None	,     lowercase_					:	Tuple=None	,     lowercase_					:	int=None	,     lowercase_					:	int="</s>"	,     lowercase_					:	List[str]="</s>"	,     lowercase_					:	Union[str, Any]="<s>"	,     lowercase_					:	List[str]="<unk>"	,     lowercase_					:	Union[str, Any]="<pad>"	,     lowercase_					:	Dict="<mask>"	,     **lowercase_					:	List[Any]	,     )		->      str:
      # Mask token behave like a normal word, i.e. include the space before it
      UpperCAmelCase			:      str		   =					AddedToken(lowercase_	,     lstrip=lowercase_	,     rstrip=lowercase_			) if isinstance(lowercase_	,     lowercase_			) else mask_token

      UpperCAmelCase			:      Optional[int]		   =					kwargs.get('additional_special_tokens'	,     []			)
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          lowercase_	,     src_lang=lowercase_	,     tgt_lang=lowercase_	,     tokenizer_file=lowercase_	,     eos_token=lowercase_	,     sep_token=lowercase_	,     cls_token=lowercase_	,     unk_token=lowercase_	,     pad_token=lowercase_	,     mask_token=lowercase_	,     **lowercase_	,     )

      UpperCAmelCase			:      List[Any]		   =					vocab_file
      UpperCAmelCase			:      Union[str, Any]		   =					False if not self.vocab_file else True

      UpperCAmelCase			:      List[Any]		   =					{
          lang_code: self.convert_tokens_to_ids(lowercase_			) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      UpperCAmelCase			:      Any		   =					src_lang if src_lang is not None else 'en_XX'
      UpperCAmelCase			:      Any		   =					tgt_lang
      UpperCAmelCase			:      List[Any]		   =					self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang			)


     @property
     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      str:
      return self._src_lang


     @src_lang.setter
     def   UpperCAmelCase_			(						self					:	List[Any]	,     lowercase_					:	str			)		->      None:
      UpperCAmelCase			:      Optional[int]		   =					new_src_lang
      self.set_src_lang_special_tokens(self._src_lang			)


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     lowercase_					:	List[int]	,     lowercase_					:	Optional[List[int]] = None			)		->      List[int]:
      if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	List[str]	,     lowercase_					:	str = "en_XX"	,     lowercase_					:	Optional[List[str]] = None	,     lowercase_					:	str = "ro_RO"	,     **lowercase_					:	int	,     )		->      BatchEncoding:
      UpperCAmelCase			:      Optional[int]		   =					src_lang
      UpperCAmelCase			:      List[str]		   =					tgt_lang
      return super().prepare_seqaseq_batch(lowercase_	,     lowercase_	,     **lowercase_			)


     def   UpperCAmelCase_			(						self					:	List[Any]			)		->      Tuple:
      return self.set_src_lang_special_tokens(self.src_lang			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Union[str, Any]:
      return self.set_tgt_lang_special_tokens(self.tgt_lang			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	str			)		->      None:
      UpperCAmelCase			:      Dict		   =					self.convert_tokens_to_ids(lowercase_			)
      UpperCAmelCase			:      str		   =					[self.cur_lang_code_id]
      UpperCAmelCase			:      Dict		   =					[self.eos_token_id]

      UpperCAmelCase			:      int		   =					self.convert_ids_to_tokens(self.prefix_tokens			)
      UpperCAmelCase			:      Optional[int]		   =					self.convert_ids_to_tokens(self.suffix_tokens			)

      UpperCAmelCase			:      Optional[int]		   =					processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str	,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str	,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,     self.prefix_tokens + self.suffix_tokens			)			)	,     )


     def   UpperCAmelCase_			(						self					:	int	,     lowercase_					:	str			)		->      None:
      UpperCAmelCase			:      Optional[Any]		   =					self.convert_tokens_to_ids(lowercase_			)
      UpperCAmelCase			:      List[str]		   =					[self.cur_lang_code_id]
      UpperCAmelCase			:      List[Any]		   =					[self.eos_token_id]

      UpperCAmelCase			:      Optional[Any]		   =					self.convert_ids_to_tokens(self.prefix_tokens			)
      UpperCAmelCase			:      Union[str, Any]		   =					self.convert_ids_to_tokens(self.suffix_tokens			)

      UpperCAmelCase			:      str		   =					processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str	,     pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str	,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,     self.prefix_tokens + self.suffix_tokens			)			)	,     )


     def   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	str	,     lowercase_					:	Optional[str]	,     lowercase_					:	Optional[str]	,     **lowercase_					:	Any			)		->      List[Any]:
      if src_lang is None or tgt_lang is None:
       raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
      UpperCAmelCase			:      Optional[Any]		   =					src_lang
      UpperCAmelCase			:      List[Any]		   =					self(lowercase_	,     add_special_tokens=lowercase_	,     return_tensors=lowercase_	,     **lowercase_			)
      UpperCAmelCase			:      Dict		   =					self.convert_tokens_to_ids(lowercase_			)
      UpperCAmelCase			:      Any		   =					tgt_lang_id
      return inputs




     def   UpperCAmelCase_			(						self					:	Dict	,     lowercase_					:	str	,     lowercase_					:	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(lowercase_			):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
       return
      UpperCAmelCase			:      Optional[int]		   =					os.path.join(
          lowercase_	,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

      return (out_vocab_file,)







'''simple docstring'''

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Optional[Any]		   =					pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      str		   =					pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3    ,			dim=0      )

 UpperCAmelCase			:      Dict		   =					pt_weights[F"""{prefix}.out_proj.weight"""]
 UpperCAmelCase			:      Tuple		   =					pt_weights[F"""{prefix}.out_proj.bias"""]

 UpperCAmelCase			:      Dict		   =					q_proj
 UpperCAmelCase			:      Optional[Any]		   =					q_proj_bias

 UpperCAmelCase			:      Union[str, Any]		   =					k_proj
 UpperCAmelCase			:      Optional[Any]		   =					k_proj_bias

 UpperCAmelCase			:      Dict		   =					v_proj
 UpperCAmelCase			:      Tuple		   =					v_proj_bias

 UpperCAmelCase			:      List[Any]		   =					out_proj_weights
 UpperCAmelCase			:      Any		   =					out_proj_bias




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 copy_linear(hf_mlp.fca    ,			UpperCAmelCase_    ,			F"""{prefix}.c_fc"""      )
 copy_linear(hf_mlp.fca    ,			UpperCAmelCase_    ,			F"""{prefix}.c_proj"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					pt_weights[F"""{prefix}.weight"""].data
 UpperCAmelCase			:      Dict		   =					pt_weights[F"""{prefix}.bias"""].data




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 # copy layer norms
 copy_linear(hf_layer.layer_norma    ,			UpperCAmelCase_    ,			F"""{prefix}.ln_1"""      )
 copy_linear(hf_layer.layer_norma    ,			UpperCAmelCase_    ,			F"""{prefix}.ln_2"""      )

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

 # copy attn
 copy_attn_layer(hf_layer.self_attn    ,			UpperCAmelCase_    ,			F"""{prefix}.attn"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 for layer_id, hf_layer in enumerate(UpperCAmelCase_      ):
  copy_layer(UpperCAmelCase_    ,			UpperCAmelCase_    ,			F"""{prefix}.{layer_id}"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 # copy projection
 UpperCAmelCase			:      Optional[int]		   =					pt_weights['text_projection'].data.T

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




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 # copy projection
 UpperCAmelCase			:      Optional[int]		   =					pt_weights['visual.proj'].data.T

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

 # copy embeddings
 UpperCAmelCase			:      List[str]		   =					pt_weights['visual.conv1.weight'].data
 UpperCAmelCase			:      Tuple		   =					pt_weights['visual.class_embedding'].data
 UpperCAmelCase			:      str		   =					pt_weights['visual.positional_embedding'].data

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




@torch.no_grad()
def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_=None      ):
 assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
 UpperCAmelCase			:      str		   =					ChineseCLIPConfig.from_pretrained(UpperCAmelCase_      )

 UpperCAmelCase			:      List[Any]		   =					ChineseCLIPModel(UpperCAmelCase_      ).eval()

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

 copy_text_model_and_projection(UpperCAmelCase_    ,			UpperCAmelCase_      )
 copy_vision_model_and_projection(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      Optional[int]		   =					pt_weights['logit_scale'].data

 hf_model.save_pretrained(UpperCAmelCase_      )


if __name__ == "__main__":
 lowercase__            =     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."
 )
 lowercase__            =     parser.parse_args()

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




