





'''simple docstring'''

import copy
import os
from typing import Union

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


a   	=    logging.get_logger(__name__)

a   	=    {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}







class __a			( _snake_case      ):
   __UpperCamelCase :			int						   =						'git_vision_model'





   def __init__(       self					:      int					,lowerCamelCase					:      Optional[int]=768					,lowerCamelCase					:      Dict=3072					,lowerCamelCase					:      List[str]=12					,lowerCamelCase					:      List[Any]=12					,lowerCamelCase					:      int=3					,lowerCamelCase					:      Tuple=224					,lowerCamelCase					:      str=16					,lowerCamelCase					:      int="quick_gelu"					,lowerCamelCase					:      List[Any]=1E-5					,lowerCamelCase					:      Dict=0.0					,lowerCamelCase					:      Union[str, Any]=0.02					,**lowerCamelCase					:      Union[str, Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							patch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							attention_dropout
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							hidden_act





   @classmethod
   def   UpperCAmelCase__  (       cls					:      Optional[Any]					,lowerCamelCase					:      Union[str, os.PathLike]					,**lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      cls._set_token_in_kwargs(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							cls.get_config_dict(lowerCamelCase					,**lowerCamelCase     )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get("""model_type"""     ) == "git":
         __SCREAMING_SNAKE_CASE   				=							config_dict["""vision_config"""]

      if "model_type" in config_dict and hasattr(cls					,"""model_type"""     ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
             f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

      return cls.from_dict(lowerCamelCase					,**lowerCamelCase     )







class __a			( _snake_case      ):
   __UpperCamelCase :			str						   =						'git'





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[int]=None					,lowerCamelCase					:      List[str]=3_0522					,lowerCamelCase					:      Any=768					,lowerCamelCase					:      int=6					,lowerCamelCase					:      Tuple=12					,lowerCamelCase					:      List[Any]=3072					,lowerCamelCase					:      int="gelu"					,lowerCamelCase					:      Optional[Any]=0.1					,lowerCamelCase					:      Optional[int]=0.1					,lowerCamelCase					:      int=1024					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      Union[str, Any]=1E-1_2					,lowerCamelCase					:      Any=0					,lowerCamelCase					:      str="absolute"					,lowerCamelCase					:      Dict=True					,lowerCamelCase					:      List[Any]=False					,lowerCamelCase					:      List[Any]=101					,lowerCamelCase					:      Tuple=102					,lowerCamelCase					:      List[str]=None					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      super().__init__(bos_token_id=lowerCamelCase					,eos_token_id=lowerCamelCase					,pad_token_id=lowerCamelCase					,**lowerCamelCase     )

      if vision_config is None:
         __SCREAMING_SNAKE_CASE   				=							{}
         logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""     )

      __SCREAMING_SNAKE_CASE   				=							GitVisionConfig(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							position_embedding_type
      __SCREAMING_SNAKE_CASE   				=							use_cache
      __SCREAMING_SNAKE_CASE   				=							tie_word_embeddings
      __SCREAMING_SNAKE_CASE   				=							num_image_with_embedding

      __SCREAMING_SNAKE_CASE   				=							bos_token_id
      __SCREAMING_SNAKE_CASE   				=							eos_token_id





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							self.vision_config.to_dict()
      __SCREAMING_SNAKE_CASE   				=							self.__class__.model_type
      return output












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(lowerCamelCase )

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

