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    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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    lowerCAmelCase_ (     __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      token.append((f"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")							)
      return token




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      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    lowerCAmelCase_ (     __A,  __A,  __A,  __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  1_000

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

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

      UpperCAmelCase__								=  UpperCAmelCase__								=  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__								=  [1, 2, 10]

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

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            UpperCAmelCase__								=  [2, 2, 20]
            UpperCAmelCase__								=  [3, 12, 16]
            UpperCAmelCase__								=  [192, 768, 1_024]

      UpperCAmelCase__								=  CvtForImageClassification(__A							)
      UpperCAmelCase__								=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"							)
      UpperCAmelCase__								=  image_size
      UpperCAmelCase__								=  torch.load(__A,  map_location=torch.device("cpu"							)							)

      UpperCAmelCase__								=  OrderedDict()
      UpperCAmelCase__								=  []

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

      UpperCAmelCase__								=  list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(__A							)
      for i in range(len(__A							)							):
            UpperCAmelCase__								=  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=3_8_4,
          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)




import json
import os
from typing import Dict, List, Optional, Tuple

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


UpperCamelCase__											=					logging.get_logger(__name__)


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

UpperCamelCase__											=					{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__											=					'</w>'
UpperCamelCase__											=					'@@ '




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  set()
      UpperCAmelCase__								=  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            UpperCAmelCase__								=  char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase__											=					{'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Dict      =			['input_ids', 'attention_mask']


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



         super().__init__(
             unk_token=__UpperCAmelCase       ,  bos_token=__UpperCAmelCase       ,  eos_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  do_lower_case

         with open(__UpperCAmelCase       ,  encoding="utf-8"		) as vocab_handle:
               UpperCAmelCase__								=  json.load(__UpperCAmelCase		)
         UpperCAmelCase__								=  {v: k for k, v in self.encoder.items()}

         if merges_file is None:
               logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""		)

               UpperCAmelCase__								=  None
               UpperCAmelCase__								=  None
         else:
               with open(__UpperCAmelCase       ,  encoding="utf-8"		) as merges_handle:
                     UpperCAmelCase__								=  merges_handle.read().split("\n"		)[:-1]

               UpperCAmelCase__								=  [tuple(merge.split()[:2]		) for merge in merges]
               UpperCAmelCase__								=  dict(zip(__UpperCAmelCase       ,  range(len(__UpperCAmelCase		)		)		)		)
               UpperCAmelCase__								=  {}


   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return len(self.decoder		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
               return self.cache[token]
         UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)

         if not pairs:
               return token

         while True:
               UpperCAmelCase__								=  min(__UpperCAmelCase       ,  key=lambda __UpperCAmelCase		: self.bpe_ranks.get(__UpperCAmelCase       ,  float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                     break
               UpperCAmelCase__		,      UpperCAmelCase__								=  bigram
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  0
               while i < len(__UpperCAmelCase		):
                     try:
                           UpperCAmelCase__								=  word.index(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     except ValueError:
                           new_word.extend(word[i:]		)
                           break
                     else:
                           new_word.extend(word[i:j]		)
                           UpperCAmelCase__								=  j

                     if word[i] == first and i < len(__UpperCAmelCase		) - 1 and word[i + 1] == second:
                           new_word.append(first + second		)
                           i += 2
                     else:
                           new_word.append(word[i]		)
                           i += 1
               UpperCAmelCase__								=  tuple(__UpperCAmelCase		)
               UpperCAmelCase__								=  new_word
               if len(__UpperCAmelCase		) == 1:
                     break
               else:
                     UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         if word == "\n  " + BPE_TOKEN_MERGES:
               UpperCAmelCase__								=  "\n" + BPE_TOKEN_MERGES

         if word.endswith(__UpperCAmelCase		):
               UpperCAmelCase__								=  word.replace(__UpperCAmelCase       ,  ""		)

         UpperCAmelCase__								=  word.replace(" "       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  word
         return word


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int		)						->				Optional[int]:
         """simple docstring"""



         if self.bpe_ranks is None:
               raise ValueError(
                   "This tokenizer was instantiated without a `merges.txt` file, so"
                   " that it can only be used for decoding, not for encoding."
                   "Make sure to provide `merges.txt` file at instantiation to enable "
                   "encoding."		)

         if self.do_lower_case:
               UpperCAmelCase__								=  text.lower()

         UpperCAmelCase__								=  text.split()

         UpperCAmelCase__								=  []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(__UpperCAmelCase		).split(" "		)		)		)

         return split_tokens


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				int:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.decoder.get(__UpperCAmelCase       ,  self.unk_token		)
         return result


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						List[str]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)

         # make sure @@ tokens are concatenated
         UpperCAmelCase__								=  "".join(string.split(__UpperCAmelCase		)		)

         return string





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



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

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder       ,  indent=2       ,  sort_keys=__UpperCAmelCase       ,  ensure_ascii=__UpperCAmelCase		) + "\n"		)

         UpperCAmelCase__								=  0
         if self.bpe_ranks is None:
               return (vocab_file,)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda __UpperCAmelCase		: kv[1]		):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                               " Please check that the tokenizer is not corrupted!"		)
                           UpperCAmelCase__								=  token_index
                     writer.write(" ".join(__UpperCAmelCase		) + "\n"		)
                     index += 1

         return (vocab_file, merges_file)



