"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A						       =      {
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Data2VecAudioForAudioFrameClassification',
			    'Data2VecAudioForCTC',
			    'Data2VecAudioForSequenceClassification',
			    'Data2VecAudioForXVector',
			    'Data2VecAudioModel',
			    'Data2VecAudioPreTrainedModel',
			]
			__A						       =      [
			    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Data2VecTextForCausalLM',
			    'Data2VecTextForMaskedLM',
			    'Data2VecTextForMultipleChoice',
			    'Data2VecTextForQuestionAnswering',
			    'Data2VecTextForSequenceClassification',
			    'Data2VecTextForTokenClassification',
			    'Data2VecTextModel',
			    'Data2VecTextPreTrainedModel',
			]
			__A						       =      [
			    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Data2VecVisionForImageClassification',
			    'Data2VecVisionForMaskedImageModeling',
			    'Data2VecVisionForSemanticSegmentation',
			    'Data2VecVisionModel',
			    'Data2VecVisionPreTrainedModel',
			]

if is_tf_available():
			__A						       =      [
			    'TFData2VecVisionForImageClassification',
			    'TFData2VecVisionForSemanticSegmentation',
			    'TFData2VecVisionModel',
			    'TFData2VecVisionPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
			from .configuration_dataavec_text import (
			    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecTextConfig,
			    DataaVecTextOnnxConfig,
			)
			from .configuration_dataavec_vision import (
			    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecVisionConfig,
			    DataaVecVisionOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dataavec_audio import (
						    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecAudioForAudioFrameClassification,
						    DataaVecAudioForCTC,
						    DataaVecAudioForSequenceClassification,
						    DataaVecAudioForXVector,
						    DataaVecAudioModel,
						    DataaVecAudioPreTrainedModel,
						)
						from .modeling_dataavec_text import (
						    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecTextForCausalLM,
						    DataaVecTextForMaskedLM,
						    DataaVecTextForMultipleChoice,
						    DataaVecTextForQuestionAnswering,
						    DataaVecTextForSequenceClassification,
						    DataaVecTextForTokenClassification,
						    DataaVecTextModel,
						    DataaVecTextPreTrainedModel,
						)
						from .modeling_dataavec_vision import (
						    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForMaskedImageModeling,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						    DataaVecVisionPreTrainedModel,
						)
			if is_tf_available():
						from .modeling_tf_dataavec_vision import (
						    TFDataaVecVisionForImageClassification,
						    TFDataaVecVisionForSemanticSegmentation,
						    TFDataaVecVisionModel,
						    TFDataaVecVisionPreTrainedModel,
						)

else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""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
			)


