





'''simple docstring'''
import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def     lowercase__       (       __lowercase						:  Dict    ,  __lowercase						:  Union[str, Any]    ,  __lowercase						:  Optional[Any]					)			->				Optional[int]:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  pt_weights[F'''{prefix}.in_proj_weight'''].chunk(3    ,  dim=0					)
       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  pt_weights[F'''{prefix}.in_proj_bias'''].chunk(3    ,  dim=0					)

       __UpperCamelCase	=  pt_weights[F'''{prefix}.out_proj.weight''']
       __UpperCamelCase	=  pt_weights[F'''{prefix}.out_proj.bias''']

       __UpperCamelCase	=  q_proj
       __UpperCamelCase	=  q_proj_bias

       __UpperCamelCase	=  k_proj
       __UpperCamelCase	=  k_proj_bias

       __UpperCamelCase	=  v_proj
       __UpperCamelCase	=  v_proj_bias

       __UpperCamelCase	=  out_proj_weights
       __UpperCamelCase	=  out_proj_bias
def     lowercase__       (       __lowercase						:  Union[str, Any]    ,  __lowercase						:  str    ,  __lowercase						:  List[str]					)			->				int:



       """simple docstring"""






       copy_linear(hf_mlp.fca    ,  __lowercase    ,  F'''{prefix}.c_fc'''					)
       copy_linear(hf_mlp.fca    ,  __lowercase    ,  F'''{prefix}.c_proj'''					)
def     lowercase__       (       __lowercase						:  Tuple    ,  __lowercase						:  List[Any]    ,  __lowercase						:  Optional[Any]					)			->				Tuple:



       """simple docstring"""






       __UpperCamelCase	=  pt_weights[F'''{prefix}.weight'''].data
       __UpperCamelCase	=  pt_weights[F'''{prefix}.bias'''].data
def     lowercase__       (       __lowercase						:  Any    ,  __lowercase						:  List[Any]    ,  __lowercase						:  Optional[Any]					)			->				int:



       """simple docstring"""






       copy_linear(hf_layer.layer_norma    ,  __lowercase    ,  F'''{prefix}.ln_1'''					)
       copy_linear(hf_layer.layer_norma    ,  __lowercase    ,  F'''{prefix}.ln_2'''					)

       # copy MLP
       copy_mlp(hf_layer.mlp    ,  __lowercase    ,  F'''{prefix}.mlp'''					)

       # copy attn
       copy_attn_layer(hf_layer.self_attn    ,  __lowercase    ,  F'''{prefix}.attn'''					)
def     lowercase__       (       __lowercase						:  Union[str, Any]    ,  __lowercase						:  List[str]    ,  __lowercase						:  List[Any]					)			->				Tuple:



       """simple docstring"""






       for layer_id, hf_layer in enumerate(__lowercase					):
              copy_layer(__lowercase    ,  __lowercase    ,  F'''{prefix}.{layer_id}'''					)
def     lowercase__       (       __lowercase						:  Union[str, Any]    ,  __lowercase						:  Any					)			->				int:



       """simple docstring"""






       __UpperCamelCase	=  pt_weights['text_projection'].data.T

       # copy text encoder
       for name, param in hf_model.text_model.named_parameters():
              __UpperCamelCase	=  pt_weights[F'''bert.{name}'''].data
def     lowercase__       (       __lowercase						:  List[str]    ,  __lowercase						:  Union[str, Any]					)			->				int:



       """simple docstring"""






       __UpperCamelCase	=  pt_weights['visual.proj'].data.T

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

       # copy embeddings
       __UpperCamelCase	=  pt_weights['visual.conv1.weight'].data
       __UpperCamelCase	=  pt_weights['visual.class_embedding'].data
       __UpperCamelCase	=  pt_weights['visual.positional_embedding'].data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers    ,  __lowercase    ,  'visual.transformer.resblocks'					)
@torch.no_grad()
def     lowercase__       (       __lowercase						:  Dict    ,  __lowercase						:  Optional[Any]    ,  __lowercase						:  List[Any]=None					)			->				Optional[Any]:



       """simple docstring"""






       assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
       __UpperCamelCase	=  ChineseCLIPConfig.from_pretrained(__lowercase					)

       __UpperCamelCase	=  ChineseCLIPModel(__lowercase					).eval()

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

       copy_text_model_and_projection(__lowercase    ,  __lowercase					)
       copy_vision_model_and_projection(__lowercase    ,  __lowercase					)
       __UpperCamelCase	=  pt_weights['logit_scale'].data

       hf_model.save_pretrained(__lowercase					)


if __name__ == "__main__":
    a__							:							List[str]			=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__							:							int			=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'''
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:
    a__							:							Union[str, Any]			=None


a__							:							str			=logging.get_logger(__name__)

a__							:							Optional[Any]			={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a__							:							List[Any]			={
    '''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'''
        ),
    },
}

a__							:							List[Any]			={
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}

# fmt: off
a__							:							int			=['''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 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Any      		=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_     : Dict      		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_     : List[str]      		=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_     : Any      		=["input_ids", "attention_mask"]
 SCREAMING_SNAKE_CASE_     : Dict      		=MBartaaTokenizer

 SCREAMING_SNAKE_CASE_     : List[int]      		=[]
 SCREAMING_SNAKE_CASE_     : List[int]      		=[]






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

        __UpperCamelCase	=  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__(
            __A							,     src_lang=__A							,     tgt_lang=__A							,     tokenizer_file=__A							,     eos_token=__A							,     sep_token=__A							,     cls_token=__A							,     unk_token=__A							,     pad_token=__A							,     mask_token=__A							,     **__A							,     )

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

        __UpperCamelCase	=  {
            lang_code: self.convert_tokens_to_ids(__A     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        __UpperCamelCase	=  src_lang if src_lang is not None else 'en_XX'
        __UpperCamelCase	=  tgt_lang
        __UpperCamelCase	=  self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang     )






 @property
 def 	_lowerCamelCase (	self	:							Tuple     ):
        return self._src_lang






 @src_lang.setter
 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							str     ):
        __UpperCamelCase	=  new_src_lang
        self.set_src_lang_special_tokens(self._src_lang     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							List[int]							,     __A	:							Optional[List[int]] = None     ):
        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 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							List[str]							,     __A	:							str = "en_XX"							,     __A	:							Optional[List[str]] = None							,     __A	:							str = "ro_RO"							,     **__A	:							Optional[int]							,     ):
        __UpperCamelCase	=  src_lang
        __UpperCamelCase	=  tgt_lang
        return super().prepare_seqaseq_batch(__A							,     __A							,     **__A     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return self.set_src_lang_special_tokens(self.src_lang     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return self.set_tgt_lang_special_tokens(self.tgt_lang     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							str     ):
        __UpperCamelCase	=  self.convert_tokens_to_ids(__A     )
        __UpperCamelCase	=  [self.cur_lang_code_id]
        __UpperCamelCase	=  [self.eos_token_id]

        __UpperCamelCase	=  self.convert_ids_to_tokens(self.prefix_tokens     )
        __UpperCamelCase	=  self.convert_ids_to_tokens(self.suffix_tokens     )

        __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							str     ):
        __UpperCamelCase	=  self.convert_tokens_to_ids(__A     )
        __UpperCamelCase	=  [self.cur_lang_code_id]
        __UpperCamelCase	=  [self.eos_token_id]

        __UpperCamelCase	=  self.convert_ids_to_tokens(self.prefix_tokens     )
        __UpperCamelCase	=  self.convert_ids_to_tokens(self.suffix_tokens     )

        __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							str							,     __A	:							Union[str, Any]							,     __A	:							str							,     __A	:							Optional[str]							,     __A	:							Optional[str]							,     **__A	:							Dict     ):
        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	=  src_lang
        __UpperCamelCase	=  self(__A							,     add_special_tokens=__A							,     return_tensors=__A							,     **__A     )
        __UpperCamelCase	=  self.convert_tokens_to_ids(__A     )
        __UpperCamelCase	=  tgt_lang_id
        return inputs






 def 	_lowerCamelCase (	self	:							List[str]							,     __A	:							str							,     __A	:							Optional[str] = None     ):
        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
        __UpperCamelCase	=  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,)
