


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


__UpperCamelCase				:  Any      					=							logging.get_logger(__name__)

__UpperCamelCase				:  Optional[int]      					=							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class  lowerCAmelCase__(    _SCREAMING_SNAKE_CASE						):






    '''simple docstring'''





    A_  :     Dict			= "swinv2"

    A_  :     int			= {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }





    def __init__(	self				:   List[str]       ,   __snake_case				:   Tuple=224       ,   __snake_case				:   Optional[Any]=4       ,   __snake_case				:   Optional[int]=3       ,   __snake_case				:   Union[str, Any]=96       ,   __snake_case				:   Dict=[2, 2, 6, 2]       ,   __snake_case				:   str=[3, 6, 12, 24]       ,   __snake_case				:   Any=7       ,   __snake_case				:   Tuple=4.0       ,   __snake_case				:   Tuple=True       ,   __snake_case				:   List[Any]=0.0       ,   __snake_case				:   Optional[int]=0.0       ,   __snake_case				:   Optional[int]=0.1       ,   __snake_case				:   str="gelu"       ,   __snake_case				:   List[Any]=False       ,   __snake_case				:   Dict=0.02       ,   __snake_case				:   Optional[Any]=1E-5       ,   __snake_case				:   str=32       ,   **__snake_case				:   Tuple       ,   ):





       '''simple docstring'''
       super().__init__(**A_	)

       UpperCAmelCase_							:      Any						=   image_size
       UpperCAmelCase_							:      str						=   patch_size
       UpperCAmelCase_							:      List[Any]						=   num_channels
       UpperCAmelCase_							:      List[Any]						=   embed_dim
       UpperCAmelCase_							:      List[Any]						=   depths
       UpperCAmelCase_							:      Optional[Any]						=   len(A_	)
       UpperCAmelCase_							:      Optional[Any]						=   num_heads
       UpperCAmelCase_							:      List[str]						=   window_size
       UpperCAmelCase_							:      Dict						=   mlp_ratio
       UpperCAmelCase_							:      Tuple						=   qkv_bias
       UpperCAmelCase_							:      int						=   hidden_dropout_prob
       UpperCAmelCase_							:      Optional[Any]						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      Optional[int]						=   drop_path_rate
       UpperCAmelCase_							:      Tuple						=   hidden_act
       UpperCAmelCase_							:      Dict						=   use_absolute_embeddings
       UpperCAmelCase_							:      Any						=   layer_norm_eps
       UpperCAmelCase_							:      Dict						=   initializer_range
       UpperCAmelCase_							:      Union[str, Any]						=   encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       UpperCAmelCase_							:      Tuple						=   int(embed_dim * 2 ** (len(A_	) - 1)	)
       UpperCAmelCase_							:      Any						=   (0, 0, 0, 0)



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)