
from typing import TYPE_CHECKING

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


_A   =     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["ViTFeatureExtractor"]
			_A   =     ["ViTImageProcessor"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ViTForImageClassification",
			    "ViTForMaskedImageModeling",
			    "ViTModel",
			    "ViTPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TFViTForImageClassification",
			    "TFViTModel",
			    "TFViTPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxViTForImageClassification",
			    "FlaxViTModel",
			    "FlaxViTPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_vit import ViTFeatureExtractor
						from .image_processing_vit import ViTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit import (
						    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTForImageClassification,
						    ViTForMaskedImageModeling,
						    ViTModel,
						    ViTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
			import sys

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





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class 	_lowerCAmelCase					(	pl.LightningModule       ):




				def __init__(					self				,				_UpperCamelCase     )				->      List[str]:
							super().__init__()
							lowerCAmelCase_						=	model
							lowerCAmelCase_						=	2
							lowerCAmelCase_						=	nn.Linear(self.model.config.hidden_size				,				self.num_labels     )




				def      __a						(					self     )				->      Tuple:
							pass






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	LongformerModel.from_pretrained(__lowerCAmelCase				)
			lowerCAmelCase_						=	LightningModel(__lowerCAmelCase				)

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

			# init longformer question answering model
			lowerCAmelCase_						=	LongformerForQuestionAnswering.from_pretrained(__lowerCAmelCase				)

			# 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(__lowerCAmelCase				)

			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
			)



