


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_      : Optional[Any]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Optional[int]  					=       '▁'

UpperCAmelCase_      : List[Any]  					=       {'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase_      : List[str]  					=       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

UpperCAmelCase_      : int  					=       {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						VOCAB_FILES_NAMES
      snake_case__				:     Dict        =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__				:     Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__				:     str        =						['''input_ids''', '''attention_mask''']
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]="<s>"     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]="</s>"     ,							SCREAMING_SNAKE_CASE__				:       str="</s>"     ,							SCREAMING_SNAKE_CASE__				:       int="<s>"     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]="<unk>"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]="<pad>"     ,							SCREAMING_SNAKE_CASE__				:       str="<mask>"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict[str, Any]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Any     ,							)		->			None:
             a_					:		Tuple							=  AddedToken(__UpperCAmelCase     ,							lstrip=__UpperCAmelCase     ,							rstrip=__UpperCAmelCase     ) if isinstance(__UpperCAmelCase     ,							__UpperCAmelCase     ) else mask_token

             a_					:		List[str]							=  {} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=__UpperCAmelCase     ,							eos_token=__UpperCAmelCase     ,							unk_token=__UpperCAmelCase     ,							sep_token=__UpperCAmelCase     ,							cls_token=__UpperCAmelCase     ,							pad_token=__UpperCAmelCase     ,							mask_token=__UpperCAmelCase     ,							sp_model_kwargs=self.sp_model_kwargs     ,							**__UpperCAmelCase     ,							)

             a_					:		int							=  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
             self.sp_model.Load(str(__UpperCAmelCase     )     )
             a_					:		Optional[int]							=  vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             a_					:		Union[str, Any]							=  {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             a_					:		List[str]							=  1

             a_					:		Any							=  len(self.sp_model     ) + self.fairseq_offset
             a_					:		int							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(       self				:       List[str]     )		->			Optional[int]:
             a_					:		Any							=  self.__dict__.copy()
             a_					:		int							=  None
             a_					:		List[Any]							=  self.sp_model.serialized_model_proto()
             return state
      def __setstate__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[Any]:
             a_					:		int							=  d

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

             a_					:		List[Any]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[List[int]] = None     )		->			List[int]:


             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             a_					:		Optional[Any]							=  [self.cls_token_id]
             a_					:		Optional[Any]							=  [self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[List[int]] = None     ,							SCREAMING_SNAKE_CASE__				:       bool = False     )		->			List[int]:


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

             if token_ids_a is None:
                    return [1] + ([0] * len(__UpperCAmelCase     )) + [1]
             return [1] + ([0] * len(__UpperCAmelCase     )) + [1, 1] + ([0] * len(__UpperCAmelCase     )) + [1]
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[List[int]] = None     )		->			List[int]:
             a_					:		int							=  [self.sep_token_id]
             a_					:		Optional[int]							=  [self.cls_token_id]

             if token_ids_a is None:
                    return len(cls + token_ids_a + sep     ) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			List[Any]:
             return len(self.sp_model     ) + self.fairseq_offset + 1  # Add the <mask> token
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Tuple:
             a_					:		Tuple							=  {self.convert_ids_to_tokens(__UpperCAmelCase     ): i for i in range(self.vocab_size     )}
             vocab.update(self.added_tokens_encoder     )
             return vocab
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             return self.sp_model.encode(__UpperCAmelCase     ,							out_type=__UpperCAmelCase     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Optional[int]:


             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             a_					:		Tuple							=  self.sp_model.PieceToId(__UpperCAmelCase     )

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:


             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Union[str, Any]:
             a_					:		str							=  ''.join(__UpperCAmelCase     ).replace(__UpperCAmelCase     ,							' '     ).strip()
             return out_string

      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[str] = None     )		->			Tuple[str]:


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

             if os.path.abspath(self.vocab_file     ) != os.path.abspath(__UpperCAmelCase     ) and os.path.isfile(self.vocab_file     ):
                    copyfile(self.vocab_file     ,							__UpperCAmelCase     )
             elif not os.path.isfile(self.vocab_file     ):
                    with open(__UpperCAmelCase     ,							'wb'     ) as fi:
                           a_					:		Tuple							=  self.sp_model.serialized_model_proto()
                           fi.write(__UpperCAmelCase     )

             return (out_vocab_file,)










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 						_a    (      a :List[Any] )							->			Optional[int]:
 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 						_a    (      a :List[Any]	,							a :Optional[int] )							->			Dict:
 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 						_a    (      a :Any )							->			List[Any]:
 a		  =      []
 token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
 return token
def 						_a    (      )							->			Optional[int]:
 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 						_a    (      a :Tuple	,							a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Optional[int]:
 a		  =      '''imagenet-1k-id2label.json'''
 a		  =      1_000

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

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

 a		  =      a		  =      CvtConfig(num_labels=a	,							idalabel=a	,							labelaid=a )

 # 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, 1_024]

 a		  =      CvtForImageClassification(a )
 a		  =      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
 a		  =      image_size
 a		  =      torch.load(a	,							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(a )
  a		  =      list_of_state_dict + embeddings(a )
  for cnt in range(config.depth[idx] ):
   a		  =      list_of_state_dict + attention(a	,							a )

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

 model.load_state_dict(a )
 model.save_pretrained(a )
 image_processor.save_pretrained(a )


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

if __name__ == "__main__":
     UpperCAmelCase__				   =		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."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


