





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin







@dataclass
class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    torch.FloatTensor







class 	__lowerCAmelCase      (       _UpperCamelCase		,_UpperCamelCase	):





   @register_to_config
   def __init__(			self       ,      snake_case = 16       ,      snake_case = 88       ,      snake_case = None       ,      snake_case = None       ,      snake_case = 1       ,      snake_case = 0.0       ,      snake_case = 32       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = "geglu"       ,      snake_case = True       ,      snake_case = True       ,      )					->	Tuple:
      """simple docstring"""





      super().__init__()
      a__						:    List[str]   		=	num_attention_heads
      a__						:    List[str]   		=	attention_head_dim
      a__						:    Tuple   		=	num_attention_heads * attention_head_dim

      a__						:    Dict   		=	in_channels

      a__						:    int   		=	torch.nn.GroupNorm(num_groups=snake_case       ,      num_channels=snake_case       ,      eps=1E-6       ,      affine=snake_case   )
      a__						:    Optional[Any]   		=	nn.Linear(snake_case       ,      snake_case   )

      # 3. Define transformers blocks
      a__						:    Union[str, Any]   		=	nn.ModuleList(
          [
              BasicTransformerBlock(
                  snake_case       ,      snake_case       ,      snake_case       ,      dropout=snake_case       ,      cross_attention_dim=snake_case       ,      activation_fn=snake_case       ,      attention_bias=snake_case       ,      double_self_attention=snake_case       ,      norm_elementwise_affine=snake_case       ,      )
              for d in range(snake_case   )
          ]   )

      a__						:    int   		=	nn.Linear(snake_case       ,      snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=1       ,      snake_case=None       ,      snake_case = True       ,      )					->	Optional[int]:
      """simple docstring"""





      a__		,  a__		,  a__		,  a__						:    Any   		=	hidden_states.shape
      a__						:    Optional[Any]   		=	batch_frames // num_frames

      a__						:    Optional[int]   		=	hidden_states

      a__						:    Union[str, Any]   		=	hidden_states[None, :].reshape(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )
      a__						:    Any   		=	hidden_states.permute(0       ,      2       ,      1       ,      3       ,      4   )

      a__						:    Dict   		=	self.norm(snake_case   )
      a__						:    Optional[int]   		=	hidden_states.permute(0       ,      3       ,      4       ,      2       ,      1   ).reshape(batch_size * height * width       ,      snake_case       ,      snake_case   )

      a__						:    Dict   		=	self.proj_in(snake_case   )

      # 2. Blocks
      for block in self.transformer_blocks:
         a__						:    Any   		=	block(
             snake_case       ,      encoder_hidden_states=snake_case       ,      timestep=snake_case       ,      cross_attention_kwargs=snake_case       ,      class_labels=snake_case       ,      )

      # 3. Output
      a__						:    Any   		=	self.proj_out(snake_case   )
      a__						:    Tuple   		=	(
          hidden_states[None, None, :]
          .reshape(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )
          .permute(0       ,      3       ,      4       ,      1       ,      2   )
          .contiguous()
      )
      a__						:    Dict   		=	hidden_states.reshape(snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )

      a__						:    List[str]   		=	hidden_states + residual

      if not return_dict:
         return (output,)

      return TransformerTemporalModelOutput(sample=snake_case   )








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
  )

