



"""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    __magic_name__     ( _lowerCamelCase					:			Optional[Any]       ):
							__a		:		Any						=							[]
							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    __magic_name__     ( _lowerCamelCase					:			Dict	,							_lowerCamelCase					:			List[str]       ):
							__a		:		Any						=							[]
							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    __magic_name__     ( _lowerCamelCase					:			str       ):
							__a		:		Any						=							[]
							token.append((F'''cvt.encoder.stages.{idx}.cls_token''', """stage2.cls_token""")       )
							return token





def    __magic_name__     ( ):
							__a		:		Tuple						=							[]
							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    __magic_name__     ( _lowerCamelCase					:			Optional[int]	,							_lowerCamelCase					:			Optional[Any]	,							_lowerCamelCase					:			List[Any]	,							_lowerCamelCase					:			Tuple       ):
							__a		:		Dict						=							"""imagenet-1k-id2label.json"""
							__a		:		List[str]						=							1_0_0_0

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

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

							__a		:		Union[str, 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":
														__a		:		Tuple						=							[1, 2, 1_0]

							# For depth size 21 (21 = 1+4+16)
							elif cvt_model.rsplit("""/"""	,							1       )[-1][4:6] == "21":
														__a		:		List[Any]						=							[1, 4, 1_6]

							# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
							else:
														__a		:		Any						=							[2, 2, 2_0]
														__a		:		str						=							[3, 1_2, 1_6]
														__a		:		Dict						=							[1_9_2, 7_6_8, 1_0_2_4]

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

							__a		:		Optional[int]						=							OrderedDict()
							__a		:		Optional[Any]						=							[]

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

							__a		:		Any						=							list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(_lowerCamelCase       )
							for i in range(len(_lowerCamelCase       )       ):
														__a		:		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 warnings
from typing import Dict, List, Optional, Tuple

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


_A							=  logging.get_logger(__name__)

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['input_ids', 'attention_mask']



  def __init__(self							,       _lowerCamelCase="</s>"							,       _lowerCamelCase="<unk>"							,       _lowerCamelCase="<pad>"							,       _lowerCamelCase=125							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if extra_ids > 0 and additional_special_tokens is None:
              UpperCAmelCase__			:							List[str]							=    [F"""<extra_id_{i}>""" for i in range(_lowerCamelCase	)]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              UpperCAmelCase__			:							int							=    len(set(filter(lambda _lowerCamelCase	: bool("""extra_id""" in str(_lowerCamelCase	)	)							,       _lowerCamelCase	)	)	)
              if extra_tokens != extra_ids:
                    raise ValueError(
                        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                        """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
                        """ extra_ids tokens"""	)

        UpperCAmelCase__			:							List[str]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else pad_token
        UpperCAmelCase__			:							int							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else eos_token
        UpperCAmelCase__			:							Optional[int]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else unk_token

        super().__init__(
            eos_token=_lowerCamelCase							,       unk_token=_lowerCamelCase							,       pad_token=_lowerCamelCase							,       extra_ids=_lowerCamelCase							,       additional_special_tokens=_lowerCamelCase							,       **_lowerCamelCase							,       )

        UpperCAmelCase__			:							int							=    extra_ids

        UpperCAmelCase__			:							List[str]							=    2**8  # utf is 8 bits

        # define special tokens dict
        UpperCAmelCase__			:							Dict[int, str]							=    {
            self.pad_token: 0,
            self.eos_token: 1,
            self.unk_token: 2,
        }
        UpperCAmelCase__			:							str							=    len(self.special_tokens_encoder	)
        UpperCAmelCase__			:							Union[str, Any]							=    len(_lowerCamelCase	)
        for i, token in enumerate(_lowerCamelCase	):
              UpperCAmelCase__			:							Optional[Any]							=    self.vocab_size + i - n
        UpperCAmelCase__			:							Dict[str, int]							=    {v: k for k, v in self.special_tokens_encoder.items()}



  @property
  def  _a      (self	):


        """simple docstring"""

        return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = False	):


        """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	)

        # normal case: some special tokens
        if token_ids_a is None:
              return ([0] * len(_lowerCamelCase	)) + [1]
        return ([0] * len(_lowerCamelCase	)) + [1] + ([0] * len(_lowerCamelCase	)) + [1]



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if len(_lowerCamelCase	) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                  """ eos tokens being added."""	)
              return token_ids
        else:
              return token_ids + [self.eos_token_id]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    [self.eos_token_id]

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



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self._add_eos_if_not_present(_lowerCamelCase	)
        if token_ids_a is None:
              return token_ids_a
        else:
              UpperCAmelCase__			:							List[str]							=    self._add_eos_if_not_present(_lowerCamelCase	)
              return token_ids_a + token_ids_a



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    [chr(_lowerCamelCase	) for i in text.encode("""utf-8"""	)]
        return tokens



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if token in self.special_tokens_encoder:
              UpperCAmelCase__			:							List[str]							=    self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
              UpperCAmelCase__			:							Tuple							=    self.added_tokens_encoder[token]
        elif len(_lowerCamelCase	) != 1:
              UpperCAmelCase__			:							Dict							=    self.unk_token_id
        else:
              UpperCAmelCase__			:							Tuple							=    ord(_lowerCamelCase	) + self._num_special_tokens
        return token_id



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if index in self.special_tokens_decoder:
              UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[index]
        else:
              UpperCAmelCase__			:							Dict							=    chr(index - self._num_special_tokens	)
        return token



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    B""""""
        for token in tokens:
              if token in self.special_tokens_decoder:
                    UpperCAmelCase__			:							Tuple							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.added_tokens_decoder:
                    UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.special_tokens_encoder:
                    UpperCAmelCase__			:							str							=    token.encode("""utf-8"""	)
              elif token in self.added_tokens_encoder:
                    UpperCAmelCase__			:							List[Any]							=    token.encode("""utf-8"""	)
              else:
                    UpperCAmelCase__			:							Optional[int]							=    bytes([ord(_lowerCamelCase	)]	)
              bstring += tok_string
        UpperCAmelCase__			:							int							=    bstring.decode("""utf-8"""							,       errors="""ignore"""	)
        return string




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        return ()

