'''simple docstring'''






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  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Tuple     )							->	List[str]:
  # Load checkpoint
  __UpperCAmelCase:					Union[str, Any]		= torch.load(__A   ,    map_location="""cpu"""     )

  __UpperCAmelCase:					Union[str, Any]		= chkpt["""model"""]

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

  __UpperCAmelCase:					Dict		= chkpt["""params"""]
  __UpperCAmelCase:					Dict		= {n: v for n, v in config.items() if not isinstance(__A   ,    (torch.FloatTensor, numpy.ndarray)     )}

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

  # Save pytorch-model
  __UpperCAmelCase:					Optional[int]		= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __UpperCAmelCase:					Union[str, Any]		= pytorch_dump_folder_path + """/""" + CONFIG_NAME
  __UpperCAmelCase:					List[Any]		= pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

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

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

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			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.'
    )
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
'''simple docstring'''






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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""timesformer"""







  def __init__(							self   ,    snake_case_=224   ,    snake_case_=16   ,    snake_case_=3   ,    snake_case_=8   ,    snake_case_=768   ,    snake_case_=12   ,    snake_case_=12   ,    snake_case_=3072   ,    snake_case_="gelu"   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=0.0_2   ,    snake_case_=1e-6   ,    snake_case_=True   ,    snake_case_="divided_space_time"   ,    snake_case_=0   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					List[Any]		= patch_size
    __UpperCAmelCase:					Optional[Any]		= num_channels
    __UpperCAmelCase:					int		= num_frames

    __UpperCAmelCase:					List[str]		= hidden_size
    __UpperCAmelCase:					List[str]		= num_hidden_layers
    __UpperCAmelCase:					Dict		= num_attention_heads
    __UpperCAmelCase:					List[str]		= intermediate_size
    __UpperCAmelCase:					Union[str, Any]		= hidden_act
    __UpperCAmelCase:					Dict		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Union[str, Any]		= initializer_range
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					int		= qkv_bias

    __UpperCAmelCase:					str		= attention_type
    __UpperCAmelCase:					Tuple		= drop_path_rate