



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case		:List[str]   	=  logging.get_logger(__name__)

__snake_case		:Dict   	=  '''▁'''

__snake_case		:Dict   	=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

__snake_case		:Any   	=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

__snake_case		:Any   	=  {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

__snake_case		:Optional[Any]   	=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

__snake_case		:Tuple   	=  {'''mustc''': MUSTC_LANGS}

class 					_A (			__SCREAMING_SNAKE_CASE					):
       UpperCamelCase__						:					int		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Union[str, Any]		      =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					str		      =		MAX_MODEL_INPUT_SIZES
       UpperCamelCase__						:					str		      =		['''input_ids''', '''attention_mask''']

       UpperCamelCase__						:					Any		      =		[]



       def __init__(	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple="<s>"  ,	__SCREAMING_SNAKE_CASE							:  Dict="</s>"  ,	__SCREAMING_SNAKE_CASE							:  List[str]="<pad>"  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]="<unk>"  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,	__SCREAMING_SNAKE_CASE							:  Tuple=False  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	__SCREAMING_SNAKE_CASE							:  List[str]=None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Dict[str, Any]] = None  ,	**__SCREAMING_SNAKE_CASE							:  str  ,	):


           '''simple docstring'''

           __a		 =       {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=__snake_case  ,	eos_token=__snake_case  ,	unk_token=__snake_case  ,	pad_token=__snake_case  ,	do_upper_case=__snake_case  ,	do_lower_case=__snake_case  ,	tgt_lang=__snake_case  ,	lang_codes=__snake_case  ,	sp_model_kwargs=self.sp_model_kwargs  ,	**__snake_case  ,	)
           __a		 =       do_upper_case
           __a		 =       do_lower_case

           __a		 =       load_json(__snake_case)
           __a		 =       {v: k for k, v in self.encoder.items()}
           __a		 =       spm_file
           __a		 =       load_spm(__snake_case  ,	self.sp_model_kwargs)

           if lang_codes is not None:
               __a		 =       lang_codes
               __a		 =       LANGUAGES[lang_codes]
               __a		 =       [F'<lang:{lang}>' for lang in self.langs]
               __a		 =       {lang: self.sp_model.PieceToId(F'<lang:{lang}>') for lang in self.langs}

               __a		 =       self.lang_tokens
               __a		 =       tgt_lang if tgt_lang is not None else self.langs[0]

               self.set_tgt_lang_special_tokens(self._tgt_lang)
           else:
               __a		 =       {}



       @property
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           return len(self.encoder)



       @property
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self._tgt_lang



       @tgt_lang.setter
       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       new_tgt_lang
           self.set_tgt_lang_special_tokens(__snake_case)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           __a		 =       self.lang_code_to_id[tgt_lang]
           __a		 =       [lang_code_id]



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           return self.sp_model.encode(__snake_case  ,	out_type=__snake_case)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           return self.encoder.get(__snake_case  ,	self.encoder[self.unk_token])



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           return self.decoder.get(__snake_case  ,	self.unk_token)



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

           __a		 =       []
           __a		 =       ''''''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   __a		 =       self.sp_model.decode(__snake_case)
                   out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                   __a		 =       []
               else:
                   current_sub_tokens.append(__snake_case)
           __a		 =       self.sp_model.decode(__snake_case)
           out_string += decoded.upper() if self.do_upper_case else decoded
           return out_string.strip()



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Any=None):


           '''simple docstring'''

           if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + [self.eos_token_id]
           # 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.eos_token_id]



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = False):


           '''simple docstring'''

           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__snake_case  ,	token_ids_a=__snake_case  ,	already_has_special_tokens=__snake_case)

           __a		 =       [1] * len(self.prefix_tokens)
           __a		 =       [1]
           if token_ids_a is None:
               return prefix_ones + ([0] * len(__snake_case)) + suffix_ones
           return prefix_ones + ([0] * len(__snake_case)) + ([0] * len(__snake_case)) + suffix_ones



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.encoder.copy()
           vocab.update(self.added_tokens_encoder)
           return vocab



       def __getstate__(	self							:  str):


           '''simple docstring'''

           __a		 =       self.__dict__.copy()
           __a		 =       None
           return state



       def __setstate__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       d

           # for backward compatibility
           if not hasattr(self  ,	'''sp_model_kwargs'''):
               __a		 =       {}

           __a		 =       load_spm(self.spm_file  ,	self.sp_model_kwargs)





       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = None):


           '''simple docstring'''

           __a		 =       Path(__snake_case)
           assert save_dir.is_dir(), F'{save_directory} should be a directory'
           __a		 =       save_dir / (
               (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
           )
           __a		 =       save_dir / (
               (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
           )

           save_json(self.encoder  ,	__snake_case)

           if os.path.abspath(self.spm_file) != os.path.abspath(__snake_case) and os.path.isfile(self.spm_file):
               copyfile(self.spm_file  ,	__snake_case)
           elif not os.path.isfile(self.spm_file):
               with open(__snake_case  ,	'''wb''') as fi:
                   __a		 =       self.sp_model.serialized_model_proto()
                   fi.write(__snake_case)

           return (str(__snake_case), str(__snake_case))





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       sentencepiece.SentencePieceProcessor(**a_       )
    spm.Load(str(a_       )       )
    return spm





def   __snake_case			(    _UpperCAmelCase       ):
    with open(a_     ,				'''r'''       ) as f:
        return json.load(a_       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    with open(a_     ,				'''w'''       ) as f:
        json.dump(a_     ,				a_     ,				indent=2       )










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
            f'stage{idx}.patch_embed.proj.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
            f'stage{idx}.patch_embed.proj.bias',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
            f'stage{idx}.patch_embed.norm.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
            f'stage{idx}.patch_embed.norm.bias',
        )       )
    return embed





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       []
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj.bias',
        )       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')       )
    return attention_weights





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')       )
    return token





def   __snake_case			(    ):
    __a		 =       []
    head.append(('''layernorm.weight''', '''norm.weight''')       )
    head.append(('''layernorm.bias''', '''norm.bias''')       )
    head.append(('''classifier.weight''', '''head.weight''')       )
    head.append(('''classifier.bias''', '''head.bias''')       )
    return head





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       '''imagenet-1k-id2label.json'''
    __a		 =       1000

    __a		 =       '''huggingface/label-files'''
    __a		 =       num_labels
    __a		 =       json.load(open(cached_download(hf_hub_url(_UpperCAmelCase     ,				_UpperCAmelCase     ,				repo_type='''dataset'''       )       )     ,				'''r'''       )       )
    __a		 =       {int(_UpperCAmelCase       ): v for k, v in idalabel.items()}

    __a		 =       idalabel
    __a		 =       {v: k for k, v in idalabel.items()}

    __a		 =       __a		 =       CvtConfig(num_labels=_UpperCAmelCase     ,				idalabel=_UpperCAmelCase     ,				labelaid=_UpperCAmelCase       )

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "13":
        __a		 =       [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "21":
        __a		 =       [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        __a		 =       [2, 2, 20]
        __a		 =       [3, 12, 16]
        __a		 =       [192, 768, 1024]

    __a		 =       CvtForImageClassification(_UpperCAmelCase       )
    __a		 =       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
    __a		 =       image_size
    __a		 =       torch.load(_UpperCAmelCase     ,				map_location=torch.device('''cpu'''       )       )

    __a		 =       OrderedDict()
    __a		 =       []

    for idx in range(len(config.depth       )       ):
        if config.cls_token[idx]:
            __a		 =       list_of_state_dict + cls_token(_UpperCAmelCase       )
        __a		 =       list_of_state_dict + embeddings(_UpperCAmelCase       )
        for cnt in range(config.depth[idx]       ):
            __a		 =       list_of_state_dict + attention(_UpperCAmelCase     ,				_UpperCAmelCase       )

    __a		 =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase       )
    for i in range(len(_UpperCAmelCase       )       ):
        __a		 =       original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase       )
    model.save_pretrained(_UpperCAmelCase       )
    image_processor.save_pretrained(_UpperCAmelCase       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
 __snake_case		:str   	=  argparse.ArgumentParser()
 parser.add_argument(
     '''--cvt_model''',
     default='''cvt-w24''',
     type=str,
     help='''Name of the cvt model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--image_size''',
     default=384,
     type=int,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--cvt_file_name''',
     default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
     type=str,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 __snake_case		:Dict   	=  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





