


"""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        lowerCamelCase_(		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				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        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[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        lowerCamelCase_(		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     []
   token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")       )
   return token







def        lowerCamelCase_(		)    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				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        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[int]							=     "imagenet-1k-id2label.json"
   _lowerCamelCase   :				List[Any]							=     1000

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

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

   _lowerCamelCase   :				List[str]							=     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":
      _lowerCamelCase   :				List[str]							=     [1, 2, 10]

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

   # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
   else:
      _lowerCamelCase   :				Tuple							=     [2, 2, 20]
      _lowerCamelCase   :				List[str]							=     [3, 12, 16]
      _lowerCamelCase   :				Optional[Any]							=     [192, 768, 1024]

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

   _lowerCamelCase   :				Any							=     OrderedDict()
   _lowerCamelCase   :				Dict							=     []

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

   _lowerCamelCase   :				str							=     list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(_lowerCamelCase       )
   for i in range(len(_lowerCamelCase       )       ):
      _lowerCamelCase   :				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__":
  _lowerCAmelCase							:    int					   =	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.'''
  )

  _lowerCAmelCase							:    Tuple					   =	parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Any =  "xlnet"
 a__					: Dict =  ["mems"]
 a__					: List[str] =  {
     "n_token": "vocab_size",  # Backward compatibility
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					int			,						__lowerCAmelCase					:					Dict=3_20_00			,						__lowerCAmelCase					:					List[str]=10_24			,						__lowerCAmelCase					:					Dict=24			,						__lowerCAmelCase					:					Optional[Any]=16			,						__lowerCAmelCase					:					Dict=40_96			,						__lowerCAmelCase					:					Any="gelu"			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					List[str]="bi"			,						__lowerCAmelCase					:					Dict=0.02			,						__lowerCAmelCase					:					Union[str, Any]=1E-12			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						__lowerCAmelCase					:					Optional[Any]=5_12			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Tuple=False			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Union[str, Any]=-1			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Any="last"			,						__lowerCAmelCase					:					List[Any]=True			,						__lowerCAmelCase					:					Tuple="tanh"			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					str=5			,						__lowerCAmelCase					:					str=5			,						__lowerCAmelCase					:					List[str]=5			,						__lowerCAmelCase					:					List[str]=1			,						__lowerCAmelCase					:					Optional[int]=2			,						**__lowerCAmelCase					:					List[str]			,						)   ->	Tuple:
    _A      =       vocab_size
    _A      =       d_model
    _A      =       n_layer
    _A      =       n_head
    if d_model % n_head != 0:
       raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''      )
    if "d_head" in kwargs:
       if kwargs["d_head"] != d_model // n_head:
          raise ValueError(
              f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''      )
    _A      =       d_model // n_head
    _A      =       ff_activation
    _A      =       d_inner
    _A      =       untie_r
    _A      =       attn_type

    _A      =       initializer_range
    _A      =       layer_norm_eps

    _A      =       dropout
    _A      =       mem_len
    _A      =       reuse_len
    _A      =       bi_data
    _A      =       clamp_len
    _A      =       same_length

    _A      =       summary_type
    _A      =       summary_use_proj
    _A      =       summary_activation
    _A      =       summary_last_dropout
    _A      =       start_n_top
    _A      =       end_n_top

    _A      =       bos_token_id
    _A      =       pad_token_id
    _A      =       eos_token_id

    if "use_cache" in kwargs:
       warnings.warn(
           '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
           ''' instead.'''			,						__lowerCAmelCase			,						)
       _A      =       kwargs['''use_cache''']

    _A      =       use_mems_eval
    _A      =       use_mems_train
    super().__init__(pad_token_id=__lowerCAmelCase			,						bos_token_id=__lowerCAmelCase			,						eos_token_id=__lowerCAmelCase			,						**__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Union[str, Any]:
    logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''      )
    return -1

 @max_position_embeddings.setter
 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    # Message copied from Transformer-XL documentation
    raise NotImplementedError(
        f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''      )



