'''simple docstring'''




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class     __magic_name__						(    pl.LightningModule):







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										pass




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str )			->      Dict:
							# load longformer model from model identifier
							lowercase_					:			str										=       LongformerModel.from_pretrained(_SCREAMING_SNAKE_CASE )
							lowercase_					:			Any										=       LightningModel(_SCREAMING_SNAKE_CASE )

							lowercase_					:			str										=       torch.load(_SCREAMING_SNAKE_CASE      ,      map_location=torch.device("""cpu""" ) )
							lightning_model.load_state_dict(ckpt["""state_dict"""] )

							# init longformer question answering model
							lowercase_					:			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__":
						_lowercase						: List[str]						=						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."
						)
						_lowercase						: List[Any]						=						parser.parse_args()
						convert_longformer_qa_checkpoint_to_pytorch(
						    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
						)









'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						str       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



