


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


lowerCamelCase__							:						Union[str, Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						str							     =   {
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'trajectory_transformer'
     __lowercase				:							List[Any]	   =       ['past_key_values']
     __lowercase				:							Dict	   =       {
         'hidden_size': 'n_embd',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }

     def __init__(					self:List[Any]     ,		_a:Any=1_00     ,		_a:List[str]=5     ,		_a:Optional[Any]=1     ,		_a:List[Any]=1     ,		_a:Optional[int]=2_49     ,		_a:Dict=6     ,		_a:int=17     ,		_a:Union[str, Any]=25     ,		_a:Optional[Any]=4     ,		_a:Union[str, Any]=4     ,		_a:int=1_28     ,		_a:Optional[Any]=0.1     ,		_a:Tuple=0.1     ,		_a:List[Any]=0.1     ,		_a:List[str]=0.0006     ,		_a:List[str]=5_12     ,		_a:Tuple=0.02     ,		_a:Optional[Any]=1e-12     ,		_a:Tuple=1     ,		_a:Dict=True     ,		_a:str=1     ,		_a:Union[str, Any]=5_02_56     ,		_a:List[str]=5_02_56     ,		**_a:List[str]     ,		):
       snake_case__						= vocab_size
       snake_case__						= action_weight
       snake_case__						= reward_weight
       snake_case__						= value_weight
       snake_case__						= max_position_embeddings
       snake_case__						= block_size
       snake_case__						= action_dim
       snake_case__						= observation_dim
       snake_case__						= transition_dim
       snake_case__						= learning_rate
       snake_case__						= n_layer
       snake_case__						= n_head
       snake_case__						= n_embd
       snake_case__						= embd_pdrop
       snake_case__						= attn_pdrop
       snake_case__						= resid_pdrop
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps
       snake_case__						= kaiming_initializer_range
       snake_case__						= use_cache
       super().__init__(pad_token_id=_a     ,		bos_token_id=_a     ,		eos_token_id=_a     ,		**_a   )



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: int     		=   {}

  for key, value in state_dict.items():
    if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
      continue

    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mim_head.cls.predictions"""			,			"""mmm_image_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mlm_head.cls.predictions"""			,			"""mmm_text_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.itm_head.cls"""			,			"""itm_head""" )
    lowercase__	: Tuple     		=   key.replace("""heads.cmd.itm_head.pooler"""			,			"""itm_head.pooler""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.clip_head.logit_scale"""			,			"""flava.logit_scale""" )
    lowercase__	: Optional[int]     		=   key.replace("""heads.fairseq_mlm.cls.predictions"""			,			"""mlm_head""" )
    lowercase__	: List[Any]     		=   key.replace("""heads.imagenet.mim_head.cls.predictions"""			,			"""mim_head""" )
    lowercase__	: int     		=   key.replace("""mm_text_projection"""			,			"""flava.text_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_image_projection"""			,			"""flava.image_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""image_encoder.module"""			,			"""flava.image_model""" )
    lowercase__	: Any     		=   key.replace("""text_encoder.module"""			,			"""flava.text_model""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_encoder.module.encoder.cls_token"""			,			"""flava.multimodal_model.cls_token""" )
    lowercase__	: Tuple     		=   key.replace("""mm_encoder.module"""			,			"""flava.multimodal_model""" )
    lowercase__	: Any     		=   key.replace("""text_projection"""			,			"""flava.text_projection""" )
    lowercase__	: List[Any]     		=   key.replace("""image_projection"""			,			"""flava.image_projection""" )

    lowercase__	: str     		=   value.float()

  for key, value in codebook_state_dict.items():
    lowercase__	: Any     		=   value

  return upgrade

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=None )		->						Union[str, Any]:


  '''simple docstring'''






  if config_path is not None:
    lowercase__	: int     		=   FlavaConfig.from_pretrained(lowercase_ )
  else:
    lowercase__	: Optional[int]     		=   FlavaConfig()

  lowercase__	: List[Any]     		=   FlavaForPreTraining(lowercase_ ).eval()

  lowercase__	: Dict     		=   convert_dalle_checkpoint(lowercase_			,			lowercase_			,			save_checkpoint=lowercase_ )

  if os.path.exists(lowercase_ ):
    lowercase__	: Dict     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  else:
    lowercase__	: Dict     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )

  lowercase__	: int     		=   upgrade_state_dict(lowercase_			,			lowercase_ )
  hf_model.load_state_dict(lowercase_ )
  lowercase__	: Optional[int]     		=   hf_model.state_dict()
  lowercase__	: Optional[int]     		=   count_parameters(lowercase_ )
  lowercase__	: Any     		=   count_parameters(lowercase_ ) + count_parameters(lowercase_ )

  assert torch.allclose(lowercase_			,			lowercase_			,			atol=1E-3 )

  hf_model.save_pretrained(lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		int        = argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
     parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     lowerCamelCase__     :		List[str]        = parser.parse_args()

     convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






