
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class  UpperCamelCase__		(    pl.LightningModule   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__		):
					super().__init__()
					A__  :	Dict         =							model
					A__  :	Union[str, Any]         =							2
					A__  :	Any         =							nn.Linear(self.model.config.hidden_size				,       self.num_labels		)





	def   __snake_case				(			self		):
					pass




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						str							,						__UpperCamelCase :						str       ) ->       Tuple:

				"""simple docstring"""







				A__  :	List[Any]         =							LongformerModel.from_pretrained(__UpperCamelCase       )
				A__  :	Optional[int]         =							LightningModel(__UpperCamelCase       )

				A__  :	List[Any]         =							torch.load(__UpperCamelCase							,						map_location=torch.device('''cpu'''       )       )
				lightning_model.load_state_dict(ckpt['''state_dict''']       )

				# init longformer question answering model
				A__  :	Dict         =							LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase       )

				# 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(__UpperCamelCase       )

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


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      Dict          =			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    :      List[str]          =			parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP