

"""simple docstring"""


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  """xlnet"""
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  ["""mems"""]
				SCREAMING_SNAKE_CASE_       :				str       				=  {
				    """n_token""": """vocab_size""",  # Backward compatibility
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}

				def __init__(     self							:    Optional[Any]					,  UpperCamelCase__							:    Any=3_2_0_0_0					,  UpperCamelCase__							:    Any=1_0_2_4					,  UpperCamelCase__							:    Any=2_4					,  UpperCamelCase__							:    Tuple=1_6					,  UpperCamelCase__							:    List[str]=4_0_9_6					,  UpperCamelCase__							:    Optional[Any]="gelu"					,  UpperCamelCase__							:    List[Any]=True					,  UpperCamelCase__							:    Optional[int]="bi"					,  UpperCamelCase__							:    Any=0.02					,  UpperCamelCase__							:    Optional[Any]=1e-12					,  UpperCamelCase__							:    Optional[int]=0.1					,  UpperCamelCase__							:    Optional[Any]=5_1_2					,  UpperCamelCase__							:    int=None					,  UpperCamelCase__							:    str=True					,  UpperCamelCase__							:    Union[str, Any]=False					,  UpperCamelCase__							:    Any=False					,  UpperCamelCase__							:    Any=-1					,  UpperCamelCase__							:    int=False					,  UpperCamelCase__							:    int="last"					,  UpperCamelCase__							:    Tuple=True					,  UpperCamelCase__							:    Dict="tanh"					,  UpperCamelCase__							:    Optional[int]=0.1					,  UpperCamelCase__							:    List[Any]=5					,  UpperCamelCase__							:    int=5					,  UpperCamelCase__							:    str=5					,  UpperCamelCase__							:    Union[str, Any]=1					,  UpperCamelCase__							:    Optional[Any]=2					,  **UpperCamelCase__							:    Union[str, Any]					,  )->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      vocab_size
						__lowerCAmelCase:	Optional[int]   				=      d_model
						__lowerCAmelCase:	Dict   				=      n_layer
						__lowerCAmelCase:	Tuple   				=      n_head
						if d_model % n_head != 0:
								raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0")
						if "d_head" in kwargs:
								if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})")
						__lowerCAmelCase:	Optional[Any]   				=      d_model // n_head
						__lowerCAmelCase:	Union[str, Any]   				=      ff_activation
						__lowerCAmelCase:	Optional[int]   				=      d_inner
						__lowerCAmelCase:	int   				=      untie_r
						__lowerCAmelCase:	Union[str, Any]   				=      attn_type

						__lowerCAmelCase:	str   				=      initializer_range
						__lowerCAmelCase:	int   				=      layer_norm_eps

						__lowerCAmelCase:	str   				=      dropout
						__lowerCAmelCase:	Any   				=      mem_len
						__lowerCAmelCase:	Any   				=      reuse_len
						__lowerCAmelCase:	Tuple   				=      bi_data
						__lowerCAmelCase:	Optional[Any]   				=      clamp_len
						__lowerCAmelCase:	List[str]   				=      same_length

						__lowerCAmelCase:	str   				=      summary_type
						__lowerCAmelCase:	Tuple   				=      summary_use_proj
						__lowerCAmelCase:	Union[str, Any]   				=      summary_activation
						__lowerCAmelCase:	str   				=      summary_last_dropout
						__lowerCAmelCase:	Tuple   				=      start_n_top
						__lowerCAmelCase:	int   				=      end_n_top

						__lowerCAmelCase:	Union[str, Any]   				=      bos_token_id
						__lowerCAmelCase:	int   				=      pad_token_id
						__lowerCAmelCase:	Any   				=      eos_token_id

						if "use_cache" in kwargs:
								warnings.warn(
								    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
								    " instead."					,  UpperCamelCase__					,  )
								__lowerCAmelCase:	str   				=      kwargs["use_cache"]

						__lowerCAmelCase:	List[Any]   				=      use_mems_eval
						__lowerCAmelCase:	str   				=      use_mems_train
						super().__init__(pad_token_id=UpperCamelCase__					,  bos_token_id=UpperCamelCase__					,  eos_token_id=UpperCamelCase__					,  **UpperCamelCase__)

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



						'''simple docstring'''


						logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
						return -1




				@max_position_embeddings.setter
				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    int)->       Any:



						'''simple docstring'''


						raise NotImplementedError(
						    f"The model {self.model_type} is one of the few models that has no sequence length limit.")





"""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
			)


