




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def       lowerCAmelCase_      (	lowerCamelCase:							Any    )       ->      Union[str, Any]:


      '''simple docstring'''




      if "model" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('model.'  ,					''    )
      if "norm1" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('norm1'  ,					'attention.output.LayerNorm'    )
      if "norm2" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('norm2'  ,					'output.LayerNorm'    )
      if "norm" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('norm'  ,					'LayerNorm'    )
      if "transformer" in orig_key:
            lowerCamelCase__   	=					orig_key.split('.'    )[0].split('_'    )[-1]
            lowerCamelCase__   	=					orig_key.replace(f"""transformer_{layer_num}"""  ,					f"""encoder.layer.{layer_num}"""    )
      if "mha.attn" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mha.attn'  ,					'attention.self'    )
      if "mha" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mha'  ,					'attention'    )
      if "W_q" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('W_q'  ,					'self.query'    )
      if "W_k" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('W_k'  ,					'self.key'    )
      if "W_v" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('W_v'  ,					'self.value'    )
      if "ff.0" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('ff.0'  ,					'intermediate.dense'    )
      if "ff.2" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('ff.2'  ,					'output.dense'    )
      if "ff" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('ff'  ,					'output.dense'    )
      if "mlm_class" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mlm.mlm_class'  ,					'cls.predictions.decoder'    )
      if "mlm" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('mlm'  ,					'cls.predictions.transform'    )
      if "backbone.backbone.encoders" in orig_key:
            lowerCamelCase__   	=					orig_key.replace('backbone.backbone.encoders'  ,					'encoder.layer'    )
      if "cls" not in orig_key:
            lowerCamelCase__   	=					'mra.' + orig_key

      return orig_key

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple  ,					lowerCamelCase:							Optional[int]    )       ->      Any:


      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__   	=					orig_state_dict.pop(lowerCamelCase    )

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  lowerCamelCase__   	=					val

      lowerCamelCase__   	=					orig_state_dict['cls.predictions.decoder.bias']
      lowerCamelCase__   	=					torch.arange(lowerCamelCase    ).expand((1, -1)    ) + 2

      return orig_state_dict

def       lowerCAmelCase_      (	lowerCamelCase:							List[Any]  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							int    )       ->      Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )['model_state_dict']
      lowerCamelCase__   	=					MraConfig.from_json_file(lowerCamelCase    )
      lowerCamelCase__   	=					MraForMaskedLM(lowerCamelCase    )

      lowerCamelCase__   	=					convert_checkpoint_helper(config.max_position_embeddings  ,					lowerCamelCase    )

      print(model.load_state_dict(lowerCamelCase    )    )
      model.eval()
      model.save_pretrained(lowerCamelCase    )

      print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""    )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : str      =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for Mra model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      __SCREAMING_SNAKE_CASE     : Dict      =parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





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


__SCREAMING_SNAKE_CASE     : str      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Any      ={
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						str  ="tvlt"




     def __init__(     self	: List[Any]						,	A__	: List[str]=2_2_4						,	A__	: Optional[int]=2_0_4_8						,	A__	: str=1_2_8						,	A__	: Union[str, Any]=[1_6, 1_6]						,	A__	: List[Any]=[1_6, 1_6]						,	A__	: int=3						,	A__	: Optional[int]=1						,	A__	: Tuple=8						,	A__	: Tuple=7_6_8						,	A__	: List[str]=1_2						,	A__	: Tuple=1_2						,	A__	: int=3_0_7_2						,	A__	: Tuple="gelu"						,	A__	: str=0.0						,	A__	: Dict=0.0						,	A__	: Any=0.02						,	A__	: int=1E-6						,	A__	: List[str]=True						,	A__	: List[str]=False						,	A__	: Optional[Any]=1_6						,	A__	: Optional[Any]=5_1_2						,	A__	: List[Any]=8						,	A__	: Any=2_0_4_8						,	A__	: List[Any]=0.75						,	A__	: List[Any]=0.15						,	A__	: str="frame-level"						,	A__	: Union[str, Any]=True						,	A__	: Optional[int]=True						,	A__	: Optional[Any]="classification"						,	**A__	: int						,	)					->							Tuple:
           super().__init__(**A__      )

           if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                     F"""got {audio_mask_type}"""      )

           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					spectrogram_length
           lowerCamelCase__   	=					frequency_length
           lowerCamelCase__   	=					image_patch_size
           lowerCamelCase__   	=					audio_patch_size
           lowerCamelCase__   	=					num_image_channels
           lowerCamelCase__   	=					num_audio_channels
           lowerCamelCase__   	=					num_frames

           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					qkv_bias
           lowerCamelCase__   	=					use_mean_pooling

           lowerCamelCase__   	=					decoder_num_attention_heads
           lowerCamelCase__   	=					decoder_hidden_size
           lowerCamelCase__   	=					decoder_num_hidden_layers
           lowerCamelCase__   	=					decoder_intermediate_size
           lowerCamelCase__   	=					pixel_mask_ratio
           lowerCamelCase__   	=					audio_mask_ratio
           lowerCamelCase__   	=					audio_mask_type

           lowerCamelCase__   	=					task_matching
           lowerCamelCase__   	=					task_mae
           lowerCamelCase__   	=					loss_type