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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


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


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

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase__     :	Dict				=			{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__     :	Any				=			{
    'allenai/led-base-16384': 16_384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  (
       list(range(ord('''!'''    )  ,			ord('''~'''    ) + 1    )    ) + list(range(ord('''¡'''    )  ,			ord('''¬'''    ) + 1    )    ) + list(range(ord('''®'''    )  ,			ord('''ÿ'''    ) + 1    )    )
   )
   UpperCAmelCase__							:				Dict   				=  bs[:]
   UpperCAmelCase__							:				Tuple   				=  0
   for b in range(2**8    ):
      if b not in bs:
         bs.append(_A    )
         cs.append(2**8 + n    )
         n += 1
   UpperCAmelCase__							:				Optional[int]   				=  [chr(_A    ) for n in cs]
   return dict(zip(_A  ,			_A    )    )







def __UpperCamelCase(   _A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  set()
   UpperCAmelCase__							:				Optional[int]   				=  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)    )
      UpperCAmelCase__							:				int   				=  char
   return pairs


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      int      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      int      							=   ['''input_ids''', '''attention_mask''']




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_="replace"					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<s>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<mask>"					,lowerCamelCase_=False					,**lowerCamelCase_					,)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else bos_token
         UpperCAmelCase__							:				List[str]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else eos_token
         UpperCAmelCase__							:				Any   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else sep_token
         UpperCAmelCase__							:				Union[str, Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else cls_token
         UpperCAmelCase__							:				Union[str, Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else unk_token
         UpperCAmelCase__							:				List[Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase__							:				Union[str, Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else mask_token

         super().__init__(
             errors=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,add_prefix_space=lowerCamelCase_					,**lowerCamelCase_					,)

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

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




      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         return len(self.encoder							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         if token in self.cache:
            return self.cache[token]
         UpperCAmelCase__							:				Any   				=  tuple(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  get_pairs(lowerCamelCase_							)

         if not pairs:
            return token

         while True:
            UpperCAmelCase__							:				List[Any]   				=  min(lowerCamelCase_					,key=lambda lowerCamelCase_							: self.bpe_ranks.get(lowerCamelCase_					,float('''inf'''							)							)							)
            if bigram not in self.bpe_ranks:
               break
            UpperCAmelCase__							:				Any   				=  bigram
            UpperCAmelCase__							:				Union[str, Any]   				=  []
            UpperCAmelCase__							:				str   				=  0
            while i < len(lowerCamelCase_							):
               try:
                  UpperCAmelCase__							:				Optional[Any]   				=  word.index(lowerCamelCase_					,lowerCamelCase_							)
               except ValueError:
                  new_word.extend(word[i:]							)
                  break
               else:
                  new_word.extend(word[i:j]							)
                  UpperCAmelCase__							:				Any   				=  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
            UpperCAmelCase__							:				List[Any]   				=  tuple(lowerCamelCase_							)
            UpperCAmelCase__							:				Dict   				=  new_word
            if len(lowerCamelCase_							) == 1:
               break
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  get_pairs(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  ''' '''.join(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  word
         return word




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  []
         for token in re.findall(self.pat					,lowerCamelCase_							):
            UpperCAmelCase__							:				Dict   				=  ''''''.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 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.decoder.get(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  ''''''.join(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''					,errors=self.errors							)
         return text




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
            return
         UpperCAmelCase__							:				Optional[int]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
         UpperCAmelCase__							:				Optional[Any]   				=  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'''							)

         UpperCAmelCase__							:				str   				=  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 lowerCamelCase_							: 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!'''							)
                  UpperCAmelCase__							:				str   				=  token_index
               writer.write(''' '''.join(lowerCamelCase_							) + '''\n'''							)
               index += 1

         return vocab_file, merge_file




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase__							:				Optional[Any]   				=  [self.cls_token_id]
         UpperCAmelCase__							:				Any   				=  [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



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

         if token_ids_a is None:
            return [1] + ([0] * len(lowerCamelCase_							)) + [1]
         return [1] + ([0] * len(lowerCamelCase_							)) + [1, 1] + ([0] * len(lowerCamelCase_							)) + [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  [self.sep_token_id]
         UpperCAmelCase__							:				Optional[Any]   				=  [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]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=False					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  kwargs.pop('''add_prefix_space'''					,self.add_prefix_space							)
         if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_							) > 0 and not text[0].isspace()):
            UpperCAmelCase__							:				Any   				=  ''' ''' + text
         return (text, kwargs)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = PaddingStrategy.DO_NOT_PAD					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,)			->			dict:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  super()._pad(
             encoded_inputs=lowerCamelCase_					,max_length=lowerCamelCase_					,padding_strategy=lowerCamelCase_					,pad_to_multiple_of=lowerCamelCase_					,return_attention_mask=lowerCamelCase_					,)

         # Load from model defaults
         if return_attention_mask is None:
            UpperCAmelCase__							:				Dict   				=  '''attention_mask''' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
            UpperCAmelCase__							:				Any   				=  encoded_inputs[self.model_input_names[0]]
            # `global_attention_mask` need to have the same length as other (sequential) inputs.
            UpperCAmelCase__							:				List[str]   				=  len(encoded_inputs['''global_attention_mask''']							) != len(lowerCamelCase_							)

            if needs_to_be_padded:
               UpperCAmelCase__							:				List[str]   				=  len(lowerCamelCase_							) - len(encoded_inputs['''global_attention_mask''']							)

               if self.padding_side == "right":
                  # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                  UpperCAmelCase__							:				Union[str, Any]   				=  (
                      encoded_inputs['''global_attention_mask'''] + [-1] * difference
                  )
               elif self.padding_side == "left":
                  UpperCAmelCase__							:				Optional[int]   				=  [-1] * difference + encoded_inputs[
                      '''global_attention_mask'''
                  ]
               else:
                  raise ValueError('''Invalid padding strategy:''' + str(self.padding_side							)							)

         return encoded_inputs





'''simple docstring'''

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 __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  []
   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 __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  []
   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 __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  []
   token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
   return token







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  []
   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 __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Dict  ,			_A					:				str  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				str   				=  10_00

   UpperCAmelCase__							:				str   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				List[Any]   				=  num_labels
   UpperCAmelCase__							:				Tuple   				=  json.load(open(cached_download(hf_hub_url(_A  ,			_A  ,			repo_type='''dataset'''    )    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Any   				=  {int(_A    ): v for k, v in idalabel.items()}

   UpperCAmelCase__							:				List[str]   				=  idalabel
   UpperCAmelCase__							:				Any   				=  {v: k for k, v in idalabel.items()}

   UpperCAmelCase__							:				int   				=  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":
      UpperCAmelCase__							:				Tuple   				=  [1, 2, 10]

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

   # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
   else:
      UpperCAmelCase__							:				Optional[int]   				=  [2, 2, 20]
      UpperCAmelCase__							:				str   				=  [3, 12, 16]
      UpperCAmelCase__							:				Union[str, Any]   				=  [1_92, 7_68, 10_24]

   UpperCAmelCase__							:				Optional[int]   				=  CvtForImageClassification(_A    )
   UpperCAmelCase__							:				Any   				=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
   UpperCAmelCase__							:				Dict   				=  image_size
   UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(_A  ,			map_location=torch.device('''cpu'''    )    )

   UpperCAmelCase__							:				Union[str, Any]   				=  OrderedDict()
   UpperCAmelCase__							:				str   				=  []

   for idx in range(len(config.depth    )    ):
      if config.cls_token[idx]:
         UpperCAmelCase__							:				Optional[int]   				=  list_of_state_dict + cls_token(_A    )
      UpperCAmelCase__							:				Union[str, Any]   				=  list_of_state_dict + embeddings(_A    )
      for cnt in range(config.depth[idx]    ):
         UpperCAmelCase__							:				str   				=  list_of_state_dict + attention(_A  ,			_A    )

   UpperCAmelCase__							:				int   				=  list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(_A    )
   for i in range(len(_A    )    ):
      UpperCAmelCase__							:				List[Any]   				=  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__     :	Tuple				=			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__     :	Dict				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




