"""simple docstring"""


import flax.linen as nn
import jax
import jax.numpy as jnp





class    snake_case     (    nn.Module  ):
				SCREAMING_SNAKE_CASE_       :				int
				SCREAMING_SNAKE_CASE_       :				jnp.dtype       				=  jnp.floataa

				def 							lowercase_					(     self							:    List[str])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      nn.Conv(
						    self.out_channels					,  kernel_size=(3, 3)					,  strides=(1, 1)					,  padding=((1, 1), (1, 1))					,  dtype=self.dtype					,  )




				def __call__(     self							:    Any					,  UpperCamelCase__							:    Any)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	int   				=      hidden_states.shape
						__lowerCAmelCase:	Tuple   				=      jax.image.resize(
						    __snake_case					,  shape=(batch, height * 2, width * 2, channels)					,  method="nearest"					,  )
						__lowerCAmelCase:	List[str]   				=      self.conv(__snake_case)
						return hidden_states





class    snake_case     (    nn.Module  ):
				SCREAMING_SNAKE_CASE_       :				int
				SCREAMING_SNAKE_CASE_       :				jnp.dtype       				=  jnp.floataa

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



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      nn.Conv(
						    self.out_channels					,  kernel_size=(3, 3)					,  strides=(2, 2)					,  padding=((1, 1), (1, 1))					,  dtype=self.dtype					,  )




				def __call__(     self							:    Dict					,  UpperCamelCase__							:    Any)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self.conv(__snake_case)
						return hidden_states





class    snake_case     (    nn.Module  ):
				SCREAMING_SNAKE_CASE_       :				int
				SCREAMING_SNAKE_CASE_       :				int       				=  None
				SCREAMING_SNAKE_CASE_       :				float       				=  0.0
				SCREAMING_SNAKE_CASE_       :				bool       				=  None
				SCREAMING_SNAKE_CASE_       :				jnp.dtype       				=  jnp.floataa

				def 							lowercase_					(     self							:    Dict)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self.in_channels if self.out_channels is None else self.out_channels

						__lowerCAmelCase:	List[str]   				=      nn.GroupNorm(num_groups=3_2					,  epsilon=1e-5)
						__lowerCAmelCase:	Optional[int]   				=      nn.Conv(
						    __snake_case					,  kernel_size=(3, 3)					,  strides=(1, 1)					,  padding=((1, 1), (1, 1))					,  dtype=self.dtype					,  )

						__lowerCAmelCase:	Tuple   				=      nn.Dense(__snake_case					,  dtype=self.dtype)

						__lowerCAmelCase:	Tuple   				=      nn.GroupNorm(num_groups=3_2					,  epsilon=1e-5)
						__lowerCAmelCase:	Optional[int]   				=      nn.Dropout(self.dropout_prob)
						__lowerCAmelCase:	Union[str, Any]   				=      nn.Conv(
						    __snake_case					,  kernel_size=(3, 3)					,  strides=(1, 1)					,  padding=((1, 1), (1, 1))					,  dtype=self.dtype					,  )

						__lowerCAmelCase:	Any   				=      self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

						__lowerCAmelCase:	int   				=      None
						if use_nin_shortcut:
								__lowerCAmelCase:	List[Any]   				=      nn.Conv(
								    __snake_case					,  kernel_size=(1, 1)					,  strides=(1, 1)					,  padding="VALID"					,  dtype=self.dtype					,  )




				def __call__(     self							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Any=True)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      hidden_states
						__lowerCAmelCase:	Optional[Any]   				=      self.norma(__snake_case)
						__lowerCAmelCase:	Optional[int]   				=      nn.swish(__snake_case)
						__lowerCAmelCase:	Optional[int]   				=      self.conva(__snake_case)

						__lowerCAmelCase:	Tuple   				=      self.time_emb_proj(nn.swish(__snake_case))
						__lowerCAmelCase:	Any   				=      jnp.expand_dims(jnp.expand_dims(__snake_case					,  1)					,  1)
						__lowerCAmelCase:	Dict   				=      hidden_states + temb

						__lowerCAmelCase:	Optional[Any]   				=      self.norma(__snake_case)
						__lowerCAmelCase:	str   				=      nn.swish(__snake_case)
						__lowerCAmelCase:	Any   				=      self.dropout(__snake_case					,  __snake_case)
						__lowerCAmelCase:	Union[str, Any]   				=      self.conva(__snake_case)

						if self.conv_shortcut is not None:
								__lowerCAmelCase:	Optional[Any]   				=      self.conv_shortcut(__snake_case)

						return hidden_states + residual





"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class    snake_case     (    pl.LightningModule  ):

				def __init__(     self							:    str					,  UpperCamelCase__							:    List[str])->       str:



						'''simple docstring'''


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




				def 							lowercase_					(     self							:    Optional[int])->       str:



						'''simple docstring'''


						pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# load longformer model from model identifier
		__lowerCAmelCase:	List[str]   				=      LongformerModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	int   				=      LightningModel(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)
		lightning_model.load_state_dict(ckpt["state_dict"]			)

		# init longformer question answering model
		__lowerCAmelCase:	Optional[Any]   				=      LongformerForQuestionAnswering.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# 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(__SCREAMING_SNAKE_CASE			)

		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
			)


