from ...configuration_utils import PretrainedConfig


lowercase__			:				int		=	{
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}




class    lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  :					Union[str, Any]   =				"tapas"


	def __init__(							self		,			__SCREAMING_SNAKE_CASE=30522		,			__SCREAMING_SNAKE_CASE=768		,			__SCREAMING_SNAKE_CASE=12		,			__SCREAMING_SNAKE_CASE=12		,			__SCREAMING_SNAKE_CASE=3072		,			__SCREAMING_SNAKE_CASE="gelu"		,			__SCREAMING_SNAKE_CASE=0.1		,			__SCREAMING_SNAKE_CASE=0.1		,			__SCREAMING_SNAKE_CASE=1024		,			__SCREAMING_SNAKE_CASE=[3, 256, 256, 2, 256, 256, 10]		,			__SCREAMING_SNAKE_CASE=0.0_2		,			__SCREAMING_SNAKE_CASE=1e-12		,			__SCREAMING_SNAKE_CASE=0		,			__SCREAMING_SNAKE_CASE=1_0.0		,			__SCREAMING_SNAKE_CASE=0		,			__SCREAMING_SNAKE_CASE=1.0		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=1.0		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=1.0		,			__SCREAMING_SNAKE_CASE=1.0		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE="ratio"		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=64		,			__SCREAMING_SNAKE_CASE=32		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=True		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=True		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=None		,			**__SCREAMING_SNAKE_CASE		,			)						->List[Any]:
								super().__init__(pad_token_id=UpperCAmelCase__		,			**UpperCAmelCase__				)

								# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
								lowerCAmelCase			=		vocab_size
								lowerCAmelCase			=		hidden_size
								lowerCAmelCase			=		num_hidden_layers
								lowerCAmelCase			=		num_attention_heads
								lowerCAmelCase			=		hidden_act
								lowerCAmelCase			=		intermediate_size
								lowerCAmelCase			=		hidden_dropout_prob
								lowerCAmelCase			=		attention_probs_dropout_prob
								lowerCAmelCase			=		max_position_embeddings
								lowerCAmelCase			=		type_vocab_sizes
								lowerCAmelCase			=		initializer_range
								lowerCAmelCase			=		layer_norm_eps

								# Fine-tuning task hyperparameters
								lowerCAmelCase			=		positive_label_weight
								lowerCAmelCase			=		num_aggregation_labels
								lowerCAmelCase			=		aggregation_loss_weight
								lowerCAmelCase			=		use_answer_as_supervision
								lowerCAmelCase			=		answer_loss_importance
								lowerCAmelCase			=		use_normalized_answer_loss
								lowerCAmelCase			=		huber_loss_delta
								lowerCAmelCase			=		temperature
								lowerCAmelCase			=		aggregation_temperature
								lowerCAmelCase			=		use_gumbel_for_cells
								lowerCAmelCase			=		use_gumbel_for_aggregation
								lowerCAmelCase			=		average_approximation_function
								lowerCAmelCase			=		cell_selection_preference
								lowerCAmelCase			=		answer_loss_cutoff
								lowerCAmelCase			=		max_num_rows
								lowerCAmelCase			=		max_num_columns
								lowerCAmelCase			=		average_logits_per_cell
								lowerCAmelCase			=		select_one_column
								lowerCAmelCase			=		allow_empty_column_selection
								lowerCAmelCase			=		init_cell_selection_weights_to_zero
								lowerCAmelCase			=		reset_position_index_per_cell
								lowerCAmelCase			=		disable_per_token_loss

								# Aggregation hyperparameters
								lowerCAmelCase			=		aggregation_labels
								lowerCAmelCase			=		no_aggregation_label_index

								if isinstance(self.aggregation_labels		,			UpperCAmelCase__				):
															lowerCAmelCase			=		{int(UpperCAmelCase__				): v for k, v in aggregation_labels.items()}






"""simple docstring"""





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 					_lowerCAmelCase  (	pl.LightningModule   ):







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			str	):
    super().__init__()
    __lowercase								=						model
    __lowercase								=						2
    __lowercase								=						nn.Linear(self.model.config.hidden_size,					self.num_labels	)



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   __lowercase								=						LongformerModel.from_pretrained(UpperCamelCase_)
   __lowercase								=						LightningModel(UpperCamelCase_)

   __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))
   lightning_model.load_state_dict(ckpt["state_dict"])

   # init longformer question answering model
   __lowercase								=						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__":
   _a       =							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.'
   )
   _a       =							parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )




