

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers.generation import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )





@require_torch
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   3
       SCREAMING_SNAKE_CASE										=   250

       SCREAMING_SNAKE_CASE										=   ids_tensor((batch_size, length)     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   torch.ones((batch_size, length)     , device=__lowerCamelCase     , dtype=torch.float			) / length
       return input_ids, scores





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(5			)

       SCREAMING_SNAKE_CASE										=   StoppingCriteriaList(
           [
               MaxLengthCriteria(max_length=10			),
               MaxTimeCriteria(max_time=0.1			),
           ]			)

       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(9			)
       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(10			)
       self.assertTrue(criteria(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   MaxLengthCriteria(max_length=10			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(5			)
       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(9			)
       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(10			)
       self.assertTrue(criteria(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   MaxNewTokensCriteria(start_length=5     , max_new_tokens=5			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(5			)
       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(9			)
       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(10			)
       self.assertTrue(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE										=   StoppingCriteriaList([criteria]			)
       self.assertEqual(criteria_list.max_length     , 10			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_tensors(5			)

       SCREAMING_SNAKE_CASE										=   MaxTimeCriteria(max_time=0.1			)
       self.assertFalse(criteria(__lowerCamelCase     , __lowerCamelCase			)			)

       SCREAMING_SNAKE_CASE										=   MaxTimeCriteria(max_time=0.1     , initial_timestamp=time.time() - 0.2			)
       self.assertTrue(criteria(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			List[Any]			):
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10			)]			)     , 10			)

       with self.assertWarns(__lowerCamelCase			):
           validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10			)]			)     , 11			)

       SCREAMING_SNAKE_CASE										=   validate_stopping_criteria(StoppingCriteriaList()     , 11			)

       self.assertEqual(len(__lowerCamelCase			)     , 1			)
'''simple docstring'''


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class     A ( pl.LightningModule ):





							def __init__(   self					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

	__UpperCAmelCase      					=      torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)
	lightning_model.load_state_dict(ckpt['''state_dict''']							)

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