



'''simple docstring'''




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class     UpperCamelCase_				(							pl.LightningModule    ):





	def __init__(						self	,	A					)    ->    Dict:
								super().__init__()
								UpperCAmelCase  :	str							  =	model
								UpperCAmelCase  :	Any							  =	2
								UpperCAmelCase  :	Tuple							  =	nn.Linear(self.model.config.hidden_size	,	self.num_labels					)






	def 				_lowercase(						self					)    ->    int:
								pass




def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							# load longformer model from model identifier
							UpperCAmelCase  :	List[Any]							  =	LongformerModel.from_pretrained(_lowercase	)
							UpperCAmelCase  :	Optional[Any]							  =	LightningModel(_lowercase	)

							UpperCAmelCase  :	Any							  =	torch.load(_lowercase			,       map_location=torch.device("""cpu"""	)	)
							lightning_model.load_state_dict(ckpt["""state_dict"""]	)

							# init longformer question answering model
							UpperCAmelCase  :	int							  =	LongformerForQuestionAnswering.from_pretrained(_lowercase	)

							# 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(_lowercase	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
							a     :    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."""
							)
							a     :    str      						=					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'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a     :    List[Any]      						=					get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")

@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				PegasusTokenizer
	lowercase    =				PegasusTokenizerFast
	lowercase    =				True
	lowercase    =				True





	def 				_lowercase(						self					)    ->    Tuple:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase  :	int							  =	PegasusTokenizer(A					)
								tokenizer.save_pretrained(self.tmpdirname					)





	@cached_property
	def 				_lowercase(						self					)    ->    int:
								return PegasusTokenizer.from_pretrained("""google/pegasus-large"""					)





	def 				_lowercase(						self	,	**A					)    ->    PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname	,	**A					)





	def 				_lowercase(						self	,	A					)    ->    List[str]:
								return ("This is a test", "This is a test")





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Optional[int]							  =	"""</s>"""
								UpperCAmelCase  :	Optional[int]							  =	1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(A					)	,	A					)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(A					)	,	A					)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	str							  =	list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0]	,	"""<pad>"""					)
								self.assertEqual(vocab_keys[1]	,	"""</s>"""					)
								self.assertEqual(vocab_keys[-1]	,	"""v"""					)
								self.assertEqual(len(A					)	,	1103					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								self.assertEqual(self.get_tokenizer().vocab_size	,	1103					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Any							  =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	Optional[Any]							  =	self.tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	Union[str, Any]							  =	(
								    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
								    """ </s> <pad> <pad> <pad>"""
								)
								UpperCAmelCase  :	Optional[Any]							  =	rust_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								UpperCAmelCase  :	Optional[Any]							  =	py_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								self.assertListEqual(A	,	A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[Any]							  =	self._large_tokenizer
								# <mask_1> masks whole sentence while <mask_2> masks single word
								UpperCAmelCase  :	Any							  =	"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
								UpperCAmelCase  :	Optional[Any]							  =	[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
								UpperCAmelCase  :	Optional[Any]							  =	tokenizer([raw_input_str]	,	return_tensors=A					).input_ids[0]
								self.assertListEqual(A	,	A					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	str							  =	self._large_tokenizer
								# The tracebacks for the following asserts are **better** without messages or self.assertEqual
								assert tokenizer.vocab_size == 96103
								assert tokenizer.pad_token_id == 0
								assert tokenizer.eos_token_id == 1
								assert tokenizer.offset == 103
								assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
								assert tokenizer.unk_token == "<unk>"
								assert tokenizer.model_max_length == 1024
								UpperCAmelCase  :	List[Any]							  =	"""To ensure a smooth flow of bank resolutions."""
								UpperCAmelCase  :	Optional[int]							  =	[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
								UpperCAmelCase  :	Union[str, Any]							  =	tokenizer([raw_input_str]	,	return_tensors=A					).input_ids[0]
								self.assertListEqual(A	,	A					)
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]					) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





	@require_torch
	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	int							  =	["""This is going to be way too long.""" * 150, """short example"""]
								UpperCAmelCase  :	Optional[int]							  =	["""not super long but more than 5 tokens""", """tiny"""]
								UpperCAmelCase  :	Tuple							  =	self._large_tokenizer(A	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	List[Any]							  =	self._large_tokenizer(
								    text_target=A	,	max_length=5	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)

								assert batch.input_ids.shape == (2, 1024)
								assert batch.attention_mask.shape == (2, 1024)
								assert targets["input_ids"].shape == (2, 5)
								assert len(A					) == 2  # input_ids, attention_mask.






	@slow
	def 				_lowercase(						self					)    ->    List[str]:
								# fmt: off
								UpperCAmelCase  :	List[str]							  =	{"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=A	,	model_name="""google/bigbird-pegasus-large-arxiv"""	,	revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,	)




@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				PegasusTokenizer
	lowercase    =				PegasusTokenizerFast
	lowercase    =				True
	lowercase    =				True





	def 				_lowercase(						self					)    ->    List[Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase  :	int							  =	PegasusTokenizer(A	,	offset=0	,	mask_token_sent=A	,	mask_token="""[MASK]"""					)
								tokenizer.save_pretrained(self.tmpdirname					)





	@cached_property
	def 				_lowercase(						self					)    ->    Optional[Any]:
								return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""					)





	def 				_lowercase(						self	,	**A					)    ->    PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname	,	**A					)





	def 				_lowercase(						self	,	A					)    ->    str:
								return ("This is a test", "This is a test")





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Dict							  =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	Any							  =	self.tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	str							  =	(
								    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
								    """ <pad> <pad> <pad>"""
								)
								UpperCAmelCase  :	List[str]							  =	rust_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								UpperCAmelCase  :	str							  =	py_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								self.assertListEqual(A	,	A					)





	@require_torch
	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Union[str, Any]							  =	["""This is going to be way too long.""" * 1000, """short example"""]
								UpperCAmelCase  :	Any							  =	["""not super long but more than 5 tokens""", """tiny"""]
								UpperCAmelCase  :	int							  =	self._large_tokenizer(A	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	Optional[int]							  =	self._large_tokenizer(
								    text_target=A	,	max_length=5	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)

								assert batch.input_ids.shape == (2, 4096)
								assert batch.attention_mask.shape == (2, 4096)
								assert targets["input_ids"].shape == (2, 5)
								assert len(A					) == 2  # input_ids, attention_mask.






	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	(
								    """This is an example string that is used to test the original TF implementation against the HF"""
								    """ implementation"""
								)

								UpperCAmelCase  :	Optional[Any]							  =	self._large_tokenizer(A					).input_ids

								self.assertListEqual(
								    A	,	[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]	,	)

