
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__ (				snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Optional[int] =						[]
  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__ (				snake_case    ,       snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     str =						[]
  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__ (				snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     List[str] =						[]
  token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')				)
  return token


def 				a__ (				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     List[str] =						[]
  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__ (				snake_case    ,       snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     List[Any] =						'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE	:     Optional[Any] =						1_000

  __SCREAMING_SNAKE_CASE	:     Optional[Any] =						'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE	:     Dict =						num_labels
  __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						json.load(open(cached_download(hf_hub_url(__lowerCamelCase    ,       __lowerCamelCase    ,       repo_type='''dataset'''				)				)    ,       '''r'''				)				)
  __SCREAMING_SNAKE_CASE	:     int =						{int(__lowerCamelCase				): v for k, v in idalabel.items()}

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

  __SCREAMING_SNAKE_CASE	:     Any =						CvtConfig(num_labels=__lowerCamelCase    ,       idalabel=__lowerCamelCase    ,       labelaid=__lowerCamelCase				)

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

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

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

  __SCREAMING_SNAKE_CASE	:     List[Any] =						CvtForImageClassification(__lowerCamelCase				)
  __SCREAMING_SNAKE_CASE	:     str =						AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				)
  __SCREAMING_SNAKE_CASE	:     List[str] =						image_size
  __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						torch.load(__lowerCamelCase    ,       map_location=torch.device('''cpu'''				)				)

  __SCREAMING_SNAKE_CASE	:     int =						OrderedDict()
  __SCREAMING_SNAKE_CASE	:     List[Any] =						[]

  for idx in range(len(config.depth				)				):
    if config.cls_token[idx]:
      __SCREAMING_SNAKE_CASE	:     Any =						list_of_state_dict + cls_token(__lowerCamelCase				)
    __SCREAMING_SNAKE_CASE	:     Any =						list_of_state_dict + embeddings(__lowerCamelCase				)
    for cnt in range(config.depth[idx]				):
      __SCREAMING_SNAKE_CASE	:     Tuple =						list_of_state_dict + attention(__lowerCamelCase    ,       __lowerCamelCase				)

  __SCREAMING_SNAKE_CASE	:     List[Any] =						list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__lowerCamelCase				)
  for i in range(len(__lowerCamelCase				)				):
    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__lowerCamelCase				)
  model.save_pretrained(__lowerCamelCase				)
  image_processor.save_pretrained(__lowerCamelCase				)


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

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

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





"""simple docstring"""







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

import sentencepiece as spm

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


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   '▁'

lowerCAmelCase_   						=   {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_   						=   {
    'facebook/xglm-564M': 2_048,
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Any   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					str   	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					int   	=				["input_ids", "attention_mask"]







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






				"""simple docstring"""



				lowercase__   :    Any				=			{} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				lowercase__   :    Any				=			7
				lowercase__   :    Optional[int]				=			[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

				lowercase__   :    Dict				=			kwargs.get('''additional_special_tokens'''			,[]						)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

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

				lowercase__   :    List[str]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(str(_snake_case						)						)
				lowercase__   :    str				=			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'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowercase__   :    Optional[int]				=			1

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

				lowercase__   :    List[str]				=			len(self.sp_model						)
				lowercase__   :    Tuple				=			{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
				self.fairseq_tokens_to_ids.update(_snake_case						)

				lowercase__   :    Union[str, Any]				=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







		def __getstate__(	self						:  int						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.__dict__.copy()
				lowercase__   :    Optional[int]				=			None
				lowercase__   :    Any				=			self.sp_model.serialized_model_proto()
				return state







		def __setstate__(	self						:  Dict			,_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



				lowercase__   :    int				=			d

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

				lowercase__   :    Optional[int]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None			,_snake_case						:  bool = False						)					->		List[int]:






				"""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						)

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







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[self.sep_token_id]

				if token_ids_a is None:
						return len(sep + token_ids_a						) * [0]
				return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]







		@property
		def 					UpperCAmelCase    (	self						:  str						)					->		Tuple:






				"""simple docstring"""



				return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			{self.convert_ids_to_tokens(_snake_case						): i for i in range(self.vocab_size						)}
				vocab.update(self.added_tokens_encoder						)
				return vocab







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  str						)					->		List[str]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Optional[int]						)					->		List[Any]:






				"""simple docstring"""



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

				# 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 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



				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 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Tuple						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			''''''.join(_snake_case						).replace(_snake_case			,''' '''						).strip()
				return out_string







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






				"""simple docstring"""



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

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

				return (out_vocab_file,)
