

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a       =							{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a       =							['MobileViTFeatureExtractor']
   _a       =							['MobileViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a       =							[
       'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTForImageClassification',
       'MobileViTForSemanticSegmentation',
       'MobileViTModel',
       'MobileViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a       =							[
       'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFMobileViTForImageClassification',
       'TFMobileViTForSemanticSegmentation',
       'TFMobileViTModel',
       'TFMobileViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


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 					_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
   )




