






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  SCREAMING_SNAKE_CASE 							=						'pt'
elif is_tf_available():
  SCREAMING_SNAKE_CASE 							=						'tf'
else:
  SCREAMING_SNAKE_CASE 							=						'jax'
class 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			int =					PerceiverTokenizer
 _SCREAMING_SNAKE_CASE							:			Tuple =					False


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     super().setUp()
     _UpperCAmelCase				:      Union[str, Any]			  =				PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname)


 @cached_property
 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''')


 def  snake_case__				(	self							,  **_A) ->		PerceiverTokenizer:

     """simple docstring"""



     return self.tokenizer_class.from_pretrained(self.tmpdirname							,  **_A)


 def  snake_case__				(	self							,  _A							,  _A=False							,  _A=20							,  _A=5) ->		Tuple[str, list]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				[]
     for i in range(len(_A)):
         try:
             _UpperCAmelCase				:      Tuple			  =				tokenizer.decode([i]							,  clean_up_tokenization_spaces=_A)
         except UnicodeDecodeError:
             pass
         toks.append((i, tok))

     _UpperCAmelCase				:      List[str]			  =				list(filter(lambda _A: re.match(R'''^[ a-zA-Z]+$'''							,  t[1])							,  _A))
     _UpperCAmelCase				:      Union[str, Any]			  =				list(filter(lambda _A: [t[0]] == tokenizer.encode(t[1]							,  add_special_tokens=_A)							,  _A))
     if max_length is not None and len(_A) > max_length:
         _UpperCAmelCase				:      int			  =				toks[:max_length]
     if min_length is not None and len(_A) < min_length and len(_A) > 0:
         while len(_A) < min_length:
             _UpperCAmelCase				:      int			  =				toks + toks
        # toks_str = [t[1] for t in toks]
     _UpperCAmelCase				:      Optional[int]			  =				[t[0] for t in toks]

     # Ensure consistency
     _UpperCAmelCase				:      Dict			  =				tokenizer.decode(_A							,  clean_up_tokenization_spaces=_A)
     if " " not in output_txt and len(_A) > 1:
         _UpperCAmelCase				:      Any			  =				(
             tokenizer.decode([toks_ids[0]]							,  clean_up_tokenization_spaces=_A)
             + ''' '''
             + tokenizer.decode(toks_ids[1:]							,  clean_up_tokenization_spaces=_A)
         )
     if with_prefix_space:
         _UpperCAmelCase				:      Dict			  =				''' ''' + output_txt
     _UpperCAmelCase				:      str			  =				tokenizer.encode(_A							,  add_special_tokens=_A)
     return output_txt, output_ids


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				self.perceiver_tokenizer
     _UpperCAmelCase				:      Any			  =				'''Unicode €.'''
     _UpperCAmelCase				:      List[Any]			  =				tokenizer(_A)
     _UpperCAmelCase				:      int			  =				[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['''input_ids''']							,  _A)

     # decoding
     _UpperCAmelCase				:      int			  =				tokenizer.decode(_A)
     self.assertEqual(_A							,  '''[CLS]Unicode €.[SEP]''')

     _UpperCAmelCase				:      Any			  =				tokenizer('''e è é ê ë''')
     _UpperCAmelCase				:      str			  =				[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['''input_ids''']							,  _A)
     # decoding
     _UpperCAmelCase				:      Optional[Any]			  =				tokenizer.decode(_A)
     self.assertEqual(_A							,  '''[CLS]e è é ê ë[SEP]''')

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))							,  '''[CLS]e è é ê ë[SEP]''')


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.perceiver_tokenizer
     _UpperCAmelCase				:      List[str]			  =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     _UpperCAmelCase				:      int			  =				[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
     # fmt: on
     _UpperCAmelCase				:      Optional[int]			  =				tokenizer(_A							,  padding=_A							,  return_tensors=_A)
     self.assertIsInstance(_A							,  _A)

     if FRAMEWORK != "jax":
         _UpperCAmelCase				:      Optional[Any]			  =				list(batch.input_ids.numpy()[0])
     else:
         _UpperCAmelCase				:      Optional[int]			  =				list(batch.input_ids.tolist()[0])

     self.assertListEqual(_A							,  _A)

     self.assertEqual((2, 38)							,  batch.input_ids.shape)
     self.assertEqual((2, 38)							,  batch.attention_mask.shape)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				self.perceiver_tokenizer
     _UpperCAmelCase				:      List[str]			  =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     _UpperCAmelCase				:      Optional[int]			  =				tokenizer(_A							,  padding=_A							,  return_tensors=_A)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('''input_ids'''							,  _A)
     self.assertIn('''attention_mask'''							,  _A)
     self.assertNotIn('''decoder_input_ids'''							,  _A)
     self.assertNotIn('''decoder_attention_mask'''							,  _A)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.perceiver_tokenizer
     _UpperCAmelCase				:      Dict			  =				[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     _UpperCAmelCase				:      Dict			  =				tokenizer(
         text_target=_A							,  max_length=32							,  padding='''max_length'''							,  truncation=_A							,  return_tensors=_A)
     self.assertEqual(32							,  targets['''input_ids'''].shape[1])


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''):
             self.assertNotEqual(tokenizer.model_max_length							,  42)

        # Now let's start the test
     _UpperCAmelCase				:      Union[str, Any]			  =				self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''):
             # Isolate this from the other tests because we save additional tokens/etc
             _UpperCAmelCase				:      Optional[Any]			  =				tempfile.mkdtemp()

             _UpperCAmelCase				:      Optional[int]			  =				''' He is very happy, UNwant\u00E9d,running'''
             _UpperCAmelCase				:      Dict			  =				tokenizer.encode(_A							,  add_special_tokens=_A)
             tokenizer.save_pretrained(_A)

             _UpperCAmelCase				:      Optional[int]			  =				tokenizer.__class__.from_pretrained(_A)
             _UpperCAmelCase				:      int			  =				after_tokenizer.encode(_A							,  add_special_tokens=_A)
             self.assertListEqual(_A							,  _A)

             shutil.rmtree(_A)

     _UpperCAmelCase				:      Optional[Any]			  =				self.get_tokenizers(model_max_length=42)
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''):
             # Isolate this from the other tests because we save additional tokens/etc
             _UpperCAmelCase				:      List[Any]			  =				tempfile.mkdtemp()

             _UpperCAmelCase				:      Optional[int]			  =				''' He is very happy, UNwant\u00E9d,running'''
             tokenizer.add_tokens(['''bim''', '''bambam'''])
             _UpperCAmelCase				:      Tuple			  =				tokenizer.additional_special_tokens
             additional_special_tokens.append('''new_additional_special_token''')
             tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
             _UpperCAmelCase				:      int			  =				tokenizer.encode(_A							,  add_special_tokens=_A)
             tokenizer.save_pretrained(_A)

             _UpperCAmelCase				:      Optional[Any]			  =				tokenizer.__class__.from_pretrained(_A)
             _UpperCAmelCase				:      Tuple			  =				after_tokenizer.encode(_A							,  add_special_tokens=_A)
             self.assertListEqual(_A							,  _A)
             self.assertIn('''new_additional_special_token'''							,  after_tokenizer.additional_special_tokens)
             self.assertEqual(after_tokenizer.model_max_length							,  42)

             _UpperCAmelCase				:      List[str]			  =				tokenizer.__class__.from_pretrained(_A							,  model_max_length=43)
             self.assertEqual(tokenizer.model_max_length							,  43)

             shutil.rmtree(_A)


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				[]
     if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

     if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

     for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer_utils.save_pretrained(_A)

             with open(os.path.join(_A							,  '''special_tokens_map.json''')							,  encoding='''utf-8''') as json_file:
                 _UpperCAmelCase				:      List[Any]			  =				json.load(_A)

             with open(os.path.join(_A							,  '''tokenizer_config.json''')							,  encoding='''utf-8''') as json_file:
                 _UpperCAmelCase				:      List[str]			  =				json.load(_A)

             _UpperCAmelCase				:      Union[str, Any]			  =				[f'''<extra_id_{i}>''' for i in range(125)]

             _UpperCAmelCase				:      List[str]			  =				added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]
             _UpperCAmelCase				:      Optional[int]			  =				added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]

             with open(os.path.join(_A							,  '''special_tokens_map.json''')							,  '''w'''							,  encoding='''utf-8''') as outfile:
                 json.dump(_A							,  _A)
             with open(os.path.join(_A							,  '''tokenizer_config.json''')							,  '''w'''							,  encoding='''utf-8''') as outfile:
                 json.dump(_A							,  _A)

             # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
             # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
             # "special_tokens_map.json" files
             _UpperCAmelCase				:      Tuple			  =				tokenizer_class.from_pretrained(
                 _A							,  )
             self.assertIn(
                 '''an_additional_special_token'''							,  tokenizer_without_change_in_init.additional_special_tokens)
             self.assertEqual(
                 ['''an_additional_special_token''']							,  tokenizer_without_change_in_init.convert_ids_to_tokens(
                     tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))							,  )

             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             _UpperCAmelCase				:      int			  =				added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''							,  lstrip=_A)]
             _UpperCAmelCase				:      List[str]			  =				tokenizer_class.from_pretrained(
                 _A							,  additional_special_tokens=_A							,  )

             self.assertIn('''a_new_additional_special_token'''							,  tokenizer.additional_special_tokens)
             self.assertEqual(
                 ['''a_new_additional_special_token''']							,  tokenizer.convert_ids_to_tokens(
                     tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))							,  )


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178])							,  '''�''')


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				self.get_tokenizers(fast=_A							,  do_lower_case=_A)
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''):
             _UpperCAmelCase				:      List[Any]			  =				['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
             _UpperCAmelCase				:      Union[str, Any]			  =				tokenizer.convert_tokens_to_string(_A)

             self.assertIsInstance(_A							,  _A)












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 							A_    (						pl.LightningModule			):






 '''simple docstring'''




 def __init__(	self							,  _A) ->		List[str]:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase				:      Dict			  =				model
     _UpperCAmelCase				:      Union[str, Any]			  =				2
     _UpperCAmelCase				:      List[Any]			  =				nn.Linear(self.model.config.hidden_size							,  self.num_labels)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				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__":
  SCREAMING_SNAKE_CASE 							=						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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




