


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      tempfile.mkdtemp()

									_UpperCAmelCase					=      [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_UpperCAmelCase					=      os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['vocab_file']						)
									with open(self.vocab_file      ,   'w'      ,   encoding='utf-8'						) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

									_UpperCAmelCase					=      {
									    'do_resize': True,
									    'size': 20,
									    'do_center_crop': True,
									    'crop_size': 18,
									    'do_normalize': True,
									    'image_mean': [0.48145466, 0.4578275, 0.40821073],
									    'image_std': [0.26862954, 0.26130258, 0.27577711],
									}
									_UpperCAmelCase					=      os.path.join(self.tmpdirname      ,   _SCREAMING_SNAKE_CASE						)
									with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'						) as fp:
																json.dump(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									return BertTokenizer.from_pretrained(self.tmpdirname      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									shutil.rmtree(self.tmpdirname						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta						)]
									_UpperCAmelCase					=      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE      ,   0      ,   -1						)						) for x in image_inputs]
									return image_inputs
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_tokenizer()
									_UpperCAmelCase					=      self.get_rust_tokenizer()
									_UpperCAmelCase					=      self.get_image_processor()

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)
									processor_slow.save_pretrained(self.tmpdirname						)
									_UpperCAmelCase					=      AlignProcessor.from_pretrained(self.tmpdirname      ,   use_fast=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)
									processor_fast.save_pretrained(self.tmpdirname						)
									_UpperCAmelCase					=      AlignProcessor.from_pretrained(self.tmpdirname						)

									self.assertEqual(processor_slow.tokenizer.get_vocab()      ,   tokenizer_slow.get_vocab()						)
									self.assertEqual(processor_fast.tokenizer.get_vocab()      ,   tokenizer_fast.get_vocab()						)
									self.assertEqual(tokenizer_slow.get_vocab()      ,   tokenizer_fast.get_vocab()						)
									self.assertIsInstance(processor_slow.tokenizer      ,   _SCREAMING_SNAKE_CASE						)
									self.assertIsInstance(processor_fast.tokenizer      ,   _SCREAMING_SNAKE_CASE						)

									self.assertEqual(processor_slow.image_processor.to_json_string()      ,   image_processor.to_json_string()						)
									self.assertEqual(processor_fast.image_processor.to_json_string()      ,   image_processor.to_json_string()						)
									self.assertIsInstance(processor_slow.image_processor      ,   _SCREAMING_SNAKE_CASE						)
									self.assertIsInstance(processor_fast.image_processor      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      AlignProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									_UpperCAmelCase					=      self.get_tokenizer(bos_token='(BOS)'      ,   eos_token='(EOS)'						)
									_UpperCAmelCase					=      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE      ,   padding_value=1.0						)

									_UpperCAmelCase					=      AlignProcessor.from_pretrained(
									    self.tmpdirname      ,   bos_token='(BOS)'      ,   eos_token='(EOS)'      ,   do_normalize=_SCREAMING_SNAKE_CASE      ,   padding_value=1.0						)

									self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab()						)
									self.assertIsInstance(processor.tokenizer      ,   _SCREAMING_SNAKE_CASE						)

									self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()						)
									self.assertIsInstance(processor.image_processor      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.prepare_image_inputs()

									_UpperCAmelCase					=      image_processor(_SCREAMING_SNAKE_CASE      ,   return_tensors='np'						)
									_UpperCAmelCase					=      processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='np'						)

									for key in input_image_proc.keys():
																self.assertAlmostEqual(input_image_proc[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      'lower newer'

									_UpperCAmelCase					=      processor(text=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      tokenizer(_SCREAMING_SNAKE_CASE      ,   padding='max_length'      ,   max_length=64						)

									for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      'lower newer'
									_UpperCAmelCase					=      self.prepare_image_inputs()

									_UpperCAmelCase					=      processor(text=_SCREAMING_SNAKE_CASE      ,   images=_SCREAMING_SNAKE_CASE						)

									self.assertListEqual(list(inputs.keys()						)      ,   ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

									# test if it raises when no input is passed
									with pytest.raises(_SCREAMING_SNAKE_CASE						):
																processor()
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									_UpperCAmelCase					=      processor.batch_decode(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

									self.assertListEqual(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      AlignProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      'lower newer'
									_UpperCAmelCase					=      self.prepare_image_inputs()

									_UpperCAmelCase					=      processor(text=_SCREAMING_SNAKE_CASE      ,   images=_SCREAMING_SNAKE_CASE						)

									self.assertListEqual(list(inputs.keys()						)      ,   processor.model_input_names						)




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      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

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

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# 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(a__	)

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


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							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__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
