





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class     _UpperCAmelCase   (   __snake_case		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			LongformerTokenizer
  a_      =			True
  a_      =			LongformerTokenizerFast
  a_      =			True






  def     lowercase		(    self			:		List[str]					)							->						Optional[int]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __lowerCAmelCase							     =     [
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''\u0120''',
         '''\u0120l''',
         '''\u0120n''',
         '''\u0120lo''',
         '''\u0120low''',
         '''er''',
         '''\u0120lowest''',
         '''\u0120newer''',
         '''\u0120wider''',
         '''<unk>''',
     ]
     __lowerCAmelCase							     =     dict(zip(a_ ,  range(len(a_					)					)					)					)
     __lowerCAmelCase							     =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
     __lowerCAmelCase							     =     {'''unk_token''': '''<unk>'''}

     __lowerCAmelCase							     =     os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['vocab_file']					)
     __lowerCAmelCase							     =     os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['merges_file']					)
     with open(self.vocab_file ,  'w' ,  encoding='utf-8'					) as fp:
        fp.write(json.dumps(a_					) + '\n'					)
     with open(self.merges_file ,  'w' ,  encoding='utf-8'					) as fp:
        fp.write('\n'.join(a_					)					)






  def     lowercase		(    self			:		Optional[Any] ,  **lowerCAmelCase_			:		List[Any]					)							->						List[Any]:
     kwargs.update(self.special_tokens_map					)
     return self.tokenizer_class.from_pretrained(self.tmpdirname ,  **a_					)






  def     lowercase		(    self			:		Tuple ,  **lowerCAmelCase_			:		Optional[int]					)							->						List[str]:
     kwargs.update(self.special_tokens_map					)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,  **a_					)






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		int					)							->						Optional[Any]:
     __lowerCAmelCase							     =     '''lower newer'''
     __lowerCAmelCase							     =     '''lower newer'''
     return input_text, output_text






  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     __lowerCAmelCase							     =     self.tokenizer_class(self.vocab_file ,  self.merges_file ,  **self.special_tokens_map					)
     __lowerCAmelCase							     =     '''lower newer'''
     __lowerCAmelCase							     =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
     __lowerCAmelCase							     =     tokenizer.tokenize(a_					)  # , add_prefix_space=True)
     self.assertListEqual(a_ ,  a_					)

     __lowerCAmelCase							     =     tokens + [tokenizer.unk_token]
     __lowerCAmelCase							     =     [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					) ,  a_					)






  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('Hello world!' ,  add_special_tokens=a_					) ,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]					)
     self.assertListEqual(
         tokenizer.encode('Hello world! cécé herlolip 418' ,  add_special_tokens=a_					) ,  [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2] ,  )






  @slow
  def     lowercase		(    self			:		Any					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

     __lowerCAmelCase							     =     tokenizer.encode('sequence builders' ,  add_special_tokens=a_					)
     __lowerCAmelCase							     =     tokenizer.encode('multi-sequence build' ,  add_special_tokens=a_					)

     __lowerCAmelCase							     =     tokenizer.encode(
         'sequence builders' ,  add_special_tokens=a_ ,  add_prefix_space=a_					)
     __lowerCAmelCase							     =     tokenizer.encode(
         'sequence builders' ,  'multi-sequence build' ,  add_special_tokens=a_ ,  add_prefix_space=a_					)

     __lowerCAmelCase							     =     tokenizer.build_inputs_with_special_tokens(a_					)
     __lowerCAmelCase							     =     tokenizer.build_inputs_with_special_tokens(a_ ,  a_					)

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode






  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.get_tokenizer()

     __lowerCAmelCase							     =     '''Encode this sequence.'''
     __lowerCAmelCase							     =     tokenizer.byte_encoder[''' '''.encode('utf-8'					)[0]]

     # Testing encoder arguments
     __lowerCAmelCase							     =     tokenizer.encode(a_ ,  add_special_tokens=a_ ,  add_prefix_space=a_					)
     __lowerCAmelCase							     =     tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
     self.assertNotEqual(a_ ,  a_					)

     __lowerCAmelCase							     =     tokenizer.encode(a_ ,  add_special_tokens=a_ ,  add_prefix_space=a_					)
     __lowerCAmelCase							     =     tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
     self.assertEqual(a_ ,  a_					)

     tokenizer.add_special_tokens({'bos_token': '<s>'}					)
     __lowerCAmelCase							     =     tokenizer.encode(a_ ,  add_special_tokens=a_					)
     __lowerCAmelCase							     =     tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
     self.assertNotEqual(a_ ,  a_					)

     # Testing spaces after special tokens
     __lowerCAmelCase							     =     '''<mask>'''
     tokenizer.add_special_tokens(
         {'mask_token': AddedToken(a_ ,  lstrip=a_ ,  rstrip=a_					)}					)  # mask token has a left space
     __lowerCAmelCase							     =     tokenizer.convert_tokens_to_ids(a_					)

     __lowerCAmelCase							     =     '''Encode <mask> sequence'''
     __lowerCAmelCase							     =     '''Encode <mask>sequence'''

     __lowerCAmelCase							     =     tokenizer.encode(a_					)
     __lowerCAmelCase							     =     encoded.index(a_					)
     __lowerCAmelCase							     =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
     self.assertEqual(a_ ,  a_					)

     __lowerCAmelCase							     =     tokenizer.encode(a_					)
     __lowerCAmelCase							     =     encoded.index(a_					)
     __lowerCAmelCase							     =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
     self.assertNotEqual(a_ ,  a_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     pass






  def     lowercase		(    self			:		List[str]					)							->						Dict:

     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(a_ ,  **a_					)
           __lowerCAmelCase							     =     self.tokenizer_class.from_pretrained(a_ ,  **a_					)
           __lowerCAmelCase							     =     '''A, <mask> AllenNLP sentence.'''
           __lowerCAmelCase							     =     tokenizer_r.encode_plus(a_ ,  add_special_tokens=a_ ,  return_token_type_ids=a_					)
           __lowerCAmelCase							     =     tokenizer_p.encode_plus(a_ ,  add_special_tokens=a_ ,  return_token_type_ids=a_					)

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r['token_type_ids']					) ,  sum(tokens_p['token_type_ids']					)					)

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					) ,  sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					) ,  )

           __lowerCAmelCase							     =     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
           __lowerCAmelCase							     =     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p['input_ids'] ,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)
           self.assertSequenceEqual(tokens_r['input_ids'] ,  [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)

           self.assertSequenceEqual(
               a_ ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
           self.assertSequenceEqual(
               a_ ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)






  def     lowercase		(    self			:		Dict					)							->						Any:

     for trim_offsets, add_prefix_space in itertools.product([True, False] ,  repeat=2					):
        __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
            self.tmpdirname ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)

        __lowerCAmelCase							     =     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
        __lowerCAmelCase							     =     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

        self.assertEqual(pre_tokenizer_state['add_prefix_space'] ,  a_					)

        self.assertEqual(post_processor_state['add_prefix_space'] ,  a_					)
        self.assertEqual(post_processor_state['trim_offsets'] ,  a_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						int:

     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
           __lowerCAmelCase							     =     '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
           __lowerCAmelCase							     =     f"""{text_of_1_token} {text_of_1_token}"""

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (0, len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (len(a_					) + 1, len(a_					) + 1 + len(a_					)) ,  )

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (0, len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (len(a_					) + 1, len(a_					) + 1 + len(a_					)) ,  )

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (0, len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (len(a_					), len(a_					) + 1 + len(a_					)) ,  )

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (0, len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (len(a_					), len(a_					) + 1 + len(a_					)) ,  )

           __lowerCAmelCase							     =     f""" {text}"""

           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
           # )
           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
           # self.assertEqual(
           #     encoding.offset_mapping[1],
           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
           # )

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (1, 1 + len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (1 + len(a_					) + 1, 1 + len(a_					) + 1 + len(a_					)) ,  )

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (0, 1 + len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (1 + len(a_					), 1 + len(a_					) + 1 + len(a_					)) ,  )

           __lowerCAmelCase							     =     self.rust_tokenizer_class.from_pretrained(
               a_ ,  use_fast=a_ ,  add_prefix_space=a_ ,  trim_offsets=a_					)
           __lowerCAmelCase							     =     tokenizer_r(a_ ,  return_offsets_mapping=a_ ,  add_special_tokens=a_					)
           self.assertEqual(encoding.offset_mapping[0] ,  (0, 1 + len(a_					))					)
           self.assertEqual(
               encoding.offset_mapping[1] ,  (1 + len(a_					), 1 + len(a_					) + 1 + len(a_					)) ,  )








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     False
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     36.4519
      __lowerCAmelCase							     =     0.90_3421
      __lowerCAmelCase							     =     222.088
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     0.76_3141

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   else:
      raise ValueError(F"""Task {task} not supported."""     )

   print(F"""Building PyTorch model from configuration: {config}"""     )
   # Load weights from tf checkpoint
   load_tf_weights_in_tapas(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
   model.save_pretrained(lowerCAmelCase_     )

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

   print('Used relative position embeddings:',     model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
     )
     parser.add_argument(
         '--reset_position_index_per_cell',
         default=False,
         action='store_true',
         help='Whether to use relative position embeddings or not. Defaults to True.',
     )
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--tapas_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained TAPAS model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     _snake_case  :   Optional[Any]						   =  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.task,
         args.reset_position_index_per_cell,
         args.tf_checkpoint_path,
         args.tapas_config_file,
         args.pytorch_dump_path,
     )

