


"""simple docstring"""






import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import (
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )

if is_tf_available() and is_tensorflow_probability_available():
     import tensorflow as tf
     import tensorflow_probability as tfp

     from ..models.auto.modeling_tf_auto import (
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )






class     A(     lowerCAmelCase__		):

  """simple docstring"""





  def __call__(   self				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							**SCREAMING_SNAKE_CASE__      )			->  int:

     """simple docstring"""



     requires_backends(self				,							'''pandas'''      )
     import pandas as pd

     if table is None:
        raise ValueError('''Keyword argument `table` cannot be None.'''      )
     elif query is None:
        if isinstance(_lowerCamelCase				,							_lowerCamelCase      ) and table.get('''query'''      ) is not None and table.get('''table'''      ) is not None:
           _UpperCamelCase				:Any				    =  [table]
        elif isinstance(_lowerCamelCase				,							_lowerCamelCase      ) and len(_lowerCamelCase      ) > 0:
           if not all(isinstance(_lowerCamelCase				,							_lowerCamelCase      ) for d in table      ):
              raise ValueError(
                  f"Keyword argument `table` should be a list of dict, but is {(type(_lowerCamelCase      ) for d in table)}"      )

           if table[0].get('''query'''      ) is not None and table[0].get('''table'''      ) is not None:
              _UpperCamelCase				:Dict				    =  table
           else:
              raise ValueError(
                  '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
                  f" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."      )
        elif Dataset is not None and isinstance(_lowerCamelCase				,							_lowerCamelCase      ) or isinstance(_lowerCamelCase				,							types.GeneratorType      ):
           return table
        else:
           raise ValueError(
               '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
               f"is {type(_lowerCamelCase      )})"      )
     else:
        _UpperCamelCase				:Union[str, Any]				    =  [{"""table""": table, """query""": query}]

     for tqa_pipeline_input in tqa_pipeline_inputs:
        if not isinstance(tqa_pipeline_input['''table''']				,							pd.DataFrame      ):
           if tqa_pipeline_input["table"] is None:
              raise ValueError('''Table cannot be None.'''      )

           _UpperCamelCase				:List[str]				    =  pd.DataFrame(tqa_pipeline_input['''table''']      )

     return tqa_pipeline_inputs






@add_end_docstrings(lowerCAmelCase__		)
class     A(     lowerCAmelCase__		):

  """simple docstring"""





  A         =       "table,query"
  def __init__(   self				,							SCREAMING_SNAKE_CASE__=TableQuestionAnsweringArgumentHandler()				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

     """simple docstring"""



     super().__init__(*_lowerCamelCase				,							**_lowerCamelCase      )
     _UpperCamelCase				:int				    =  args_parser

     self.check_model_type(
         dict(
             TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
             + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()      )
         if self.framework == '''tf'''
         else dict(
             MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()      )      )

     _UpperCamelCase				:List[Any]				    =  bool(getattr(self.model.config				,							'''aggregation_labels'''				,							_lowerCamelCase      )      ) and bool(
         getattr(self.model.config				,							'''num_aggregation_labels'''				,							_lowerCamelCase      )      )
     _UpperCamelCase				:Union[str, Any]				    =  """tapas""" if hasattr(self.model.config				,							'''aggregation_labels'''      ) else None
  def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  List[Any]:

     """simple docstring"""



     return self.model(**_lowerCamelCase      )
  def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

     """simple docstring"""



     if self.framework == "pt":
        _UpperCamelCase				:Optional[Any]				    =  []
        _UpperCamelCase				:Union[str, Any]				    =  []
        _UpperCamelCase				:Union[str, Any]				    =  None
        _UpperCamelCase				:List[str]				    =  inputs["""input_ids"""].shape[0]

        _UpperCamelCase				:Optional[Any]				    =  inputs["""input_ids"""].to(self.device      )
        _UpperCamelCase				:List[Any]				    =  inputs["""attention_mask"""].to(self.device      )
        _UpperCamelCase				:Dict				    =  inputs["""token_type_ids"""].to(self.device      )
        _UpperCamelCase				:Optional[int]				    =  None

        for index in range(_lowerCamelCase      ):
           # If sequences have already been processed, the token type IDs will be created according to the previous
           # answer.
           if prev_answers is not None:
              _UpperCamelCase				:int				    =  token_type_ids_example[:, 3]  # shape (seq_len,)
              _UpperCamelCase				:List[str]				    =  np.zeros_like(prev_labels_example.cpu().numpy()      )  # shape (seq_len,)

              _UpperCamelCase				:int				    =  token_type_ids[index]  # shape (seq_len, 7)
              for i in range(model_labels.shape[0]      ):
                 _UpperCamelCase				:Optional[Any]				    =  token_type_ids_example[:, 0].tolist()[i]
                 _UpperCamelCase				:Optional[Any]				    =  token_type_ids_example[:, 1].tolist()[i] - 1
                 _UpperCamelCase				:str				    =  token_type_ids_example[:, 2].tolist()[i] - 1

                 if row_id >= 0 and col_id >= 0 and segment_id == 1:
                    _UpperCamelCase				:Optional[int]				    =  int(prev_answers[(col_id, row_id)]      )

              _UpperCamelCase				:List[str]				    =  torch.from_numpy(_lowerCamelCase      ).type(torch.long      ).to(self.device      )

           _UpperCamelCase				:Optional[int]				    =  input_ids[index]
           _UpperCamelCase				:Any				    =  attention_mask[index]  # shape (seq_len,)
           _UpperCamelCase				:str				    =  token_type_ids[index]  # shape (seq_len, 7)
           _UpperCamelCase				:Any				    =  self.model(
               input_ids=input_ids_example.unsqueeze(0      )				,							attention_mask=attention_mask_example.unsqueeze(0      )				,							token_type_ids=token_type_ids_example.unsqueeze(0      )				,							)
           _UpperCamelCase				:List[str]				    =  outputs.logits

           if self.aggregate:
              all_aggregations.append(outputs.logits_aggregation      )

           all_logits.append(_lowerCamelCase      )

           _UpperCamelCase				:List[Any]				    =  torch.distributions.Bernoulli(logits=_lowerCamelCase      )
           _UpperCamelCase				:str				    =  dist_per_token.probs * attention_mask_example.type(torch.floataa      ).to(
               dist_per_token.probs.device      )

           _UpperCamelCase				:List[Any]				    =  collections.defaultdict(_lowerCamelCase      )
           for i, p in enumerate(probabilities.squeeze().tolist()      ):
              _UpperCamelCase				:Optional[Any]				    =  token_type_ids_example[:, 0].tolist()[i]
              _UpperCamelCase				:Tuple				    =  token_type_ids_example[:, 1].tolist()[i] - 1
              _UpperCamelCase				:List[str]				    =  token_type_ids_example[:, 2].tolist()[i] - 1
              if col >= 0 and row >= 0 and segment_id == 1:
                 coords_to_probs[(col, row)].append(_lowerCamelCase      )

           _UpperCamelCase				:int				    =  {key: np.array(coords_to_probs[key]      ).mean() > 0.5 for key in coords_to_probs}

        _UpperCamelCase				:Optional[Any]				    =  torch.cat(tuple(_lowerCamelCase      )				,							0      )

        return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_lowerCamelCase      )				,							0      ))
     else:
        _UpperCamelCase				:Optional[int]				    =  []
        _UpperCamelCase				:Tuple				    =  []
        _UpperCamelCase				:List[str]				    =  None
        _UpperCamelCase				:Union[str, Any]				    =  inputs["""input_ids"""].shape[0]

        _UpperCamelCase				:Dict				    =  inputs["""input_ids"""]
        _UpperCamelCase				:Tuple				    =  inputs["""attention_mask"""]
        _UpperCamelCase				:Tuple				    =  inputs["""token_type_ids"""].numpy()
        _UpperCamelCase				:Dict				    =  None

        for index in range(_lowerCamelCase      ):
           # If sequences have already been processed, the token type IDs will be created according to the previous
           # answer.
           if prev_answers is not None:
              _UpperCamelCase				:int				    =  token_type_ids_example[:, 3]  # shape (seq_len,)
              _UpperCamelCase				:List[Any]				    =  np.zeros_like(_lowerCamelCase				,							dtype=np.intaa      )  # shape (seq_len,)

              _UpperCamelCase				:Tuple				    =  token_type_ids[index]  # shape (seq_len, 7)
              for i in range(model_labels.shape[0]      ):
                 _UpperCamelCase				:Dict				    =  token_type_ids_example[:, 0].tolist()[i]
                 _UpperCamelCase				:Dict				    =  token_type_ids_example[:, 1].tolist()[i] - 1
                 _UpperCamelCase				:List[str]				    =  token_type_ids_example[:, 2].tolist()[i] - 1

                 if row_id >= 0 and col_id >= 0 and segment_id == 1:
                    _UpperCamelCase				:Optional[Any]				    =  int(prev_answers[(col_id, row_id)]      )

              _UpperCamelCase				:Dict				    =  model_labels

           _UpperCamelCase				:int				    =  input_ids[index]
           _UpperCamelCase				:int				    =  attention_mask[index]  # shape (seq_len,)
           _UpperCamelCase				:Any				    =  token_type_ids[index]  # shape (seq_len, 7)
           _UpperCamelCase				:Optional[int]				    =  self.model(
               input_ids=np.expand_dims(_lowerCamelCase				,							axis=0      )				,							attention_mask=np.expand_dims(_lowerCamelCase				,							axis=0      )				,							token_type_ids=np.expand_dims(_lowerCamelCase				,							axis=0      )				,							)
           _UpperCamelCase				:Dict				    =  outputs.logits

           if self.aggregate:
              all_aggregations.append(outputs.logits_aggregation      )

           all_logits.append(_lowerCamelCase      )

           _UpperCamelCase				:List[str]				    =  tfp.distributions.Bernoulli(logits=_lowerCamelCase      )
           _UpperCamelCase				:Dict				    =  dist_per_token.probs_parameter() * tf.cast(_lowerCamelCase				,							tf.floataa      )

           _UpperCamelCase				:int				    =  collections.defaultdict(_lowerCamelCase      )
           _UpperCamelCase				:List[Any]				    =  token_type_ids_example
           for i, p in enumerate(tf.squeeze(_lowerCamelCase      ).numpy().tolist()      ):
              _UpperCamelCase				:Optional[int]				    =  token_type_ids_example[:, 0].tolist()[i]
              _UpperCamelCase				:Dict				    =  token_type_ids_example[:, 1].tolist()[i] - 1
              _UpperCamelCase				:str				    =  token_type_ids_example[:, 2].tolist()[i] - 1
              if col >= 0 and row >= 0 and segment_id == 1:
                 coords_to_probs[(col, row)].append(_lowerCamelCase      )

           _UpperCamelCase				:Dict				    =  {key: np.array(coords_to_probs[key]      ).mean() > 0.5 for key in coords_to_probs}

        _UpperCamelCase				:Tuple				    =  tf.concat(tuple(_lowerCamelCase      )				,							0      )

        return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_lowerCamelCase      )				,							0      ))
  def __call__(   self				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

     """simple docstring"""



     _UpperCamelCase				:str				    =  self._args_parser(*_lowerCamelCase				,							**_lowerCamelCase      )

     _UpperCamelCase				:Any				    =  super().__call__(_lowerCamelCase				,							**_lowerCamelCase      )
     if len(_lowerCamelCase      ) == 1:
        return results[0]
     return results
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=None				,							**SCREAMING_SNAKE_CASE__      )			->  Any:

     """simple docstring"""



     _UpperCamelCase				:Optional[Any]				    =  {}
     if padding is not None:
        _UpperCamelCase				:Optional[Any]				    =  padding
     if truncation is not None:
        _UpperCamelCase				:Optional[Any]				    =  truncation

     _UpperCamelCase				:List[str]				    =  {}
     if sequential is not None:
        _UpperCamelCase				:str				    =  sequential
     return preprocess_params, forward_params, {}
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=None      )			->  Optional[int]:

     """simple docstring"""



     if truncation is None:
        if self.type == "tapas":
           _UpperCamelCase				:Dict				    =  """drop_rows_to_fit"""
        else:
           _UpperCamelCase				:Optional[int]				    =  """do_not_truncate"""

     _UpperCamelCase				:Optional[int]				    =  pipeline_input["""table"""], pipeline_input["""query"""]
     if table.empty:
        raise ValueError('''table is empty'''      )
     if query is None or query == "":
        raise ValueError('''query is empty'''      )
     _UpperCamelCase				:Optional[Any]				    =  self.tokenizer(_lowerCamelCase				,							_lowerCamelCase				,							return_tensors=self.framework				,							truncation=_lowerCamelCase				,							padding=_lowerCamelCase      )
     _UpperCamelCase				:Optional[int]				    =  table
     return inputs
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=False      )			->  List[str]:

     """simple docstring"""



     _UpperCamelCase				:Tuple				    =  model_inputs.pop('''table'''      )

     if self.type == "tapas":
        if sequential:
           _UpperCamelCase				:Dict				    =  self.sequential_inference(**_lowerCamelCase      )
        else:
           _UpperCamelCase				:Tuple				    =  self.batch_inference(**_lowerCamelCase      )
     else:
        _UpperCamelCase				:Optional[Any]				    =  self.model.generate(**_lowerCamelCase      )
     _UpperCamelCase				:int				    =  {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
     return model_outputs





  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Optional[Any]:

     """simple docstring"""



     _UpperCamelCase				:str				    =  model_outputs["""model_inputs"""]
     _UpperCamelCase				:Optional[Any]				    =  model_outputs["""table"""]
     _UpperCamelCase				:Any				    =  model_outputs["""outputs"""]
     if self.type == "tapas":
        if self.aggregate:
           _UpperCamelCase				:Optional[Any]				    =  outputs[:2]
           _UpperCamelCase				:List[str]				    =  self.tokenizer.convert_logits_to_predictions(_lowerCamelCase				,							_lowerCamelCase				,							_lowerCamelCase      )
           _UpperCamelCase				:Optional[int]				    =  predictions
           _UpperCamelCase				:int				    =  {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_lowerCamelCase      )}

           _UpperCamelCase				:Tuple				    =  self.model.config.no_aggregation_label_index
           _UpperCamelCase				:Tuple				    =  {
               i: aggregators[i] + """ > """ for i, pred in enumerate(_lowerCamelCase      ) if pred != no_agg_label_index
           }
        else:
           _UpperCamelCase				:int				    =  outputs[0]
           _UpperCamelCase				:Optional[int]				    =  self.tokenizer.convert_logits_to_predictions(_lowerCamelCase				,							_lowerCamelCase      )
           _UpperCamelCase				:int				    =  predictions[0]
           _UpperCamelCase				:Dict				    =  {}
           _UpperCamelCase				:Dict				    =  {}
        _UpperCamelCase				:Dict				    =  []
        for index, coordinates in enumerate(_lowerCamelCase      ):
           _UpperCamelCase				:Any				    =  [table.iat[coordinate] for coordinate in coordinates]
           _UpperCamelCase				:Optional[Any]				    =  aggregators.get(_lowerCamelCase				,							''''''      )
           _UpperCamelCase				:Union[str, Any]				    =  aggregators_prefix.get(_lowerCamelCase				,							''''''      )
           _UpperCamelCase				:Any				    =  {
               """answer""": aggregator_prefix + """, """.join(_lowerCamelCase      ),
               """coordinates""": coordinates,
               """cells""": [table.iat[coordinate] for coordinate in coordinates],
           }
           if aggregator:
              _UpperCamelCase				:int				    =  aggregator

           answers.append(_lowerCamelCase      )
        if len(_lowerCamelCase      ) == 0:
           raise PipelineException('''Empty answer'''      )
     else:
        _UpperCamelCase				:Optional[int]				    =  [{"""answer""": answer} for answer in self.tokenizer.batch_decode(_lowerCamelCase				,							skip_special_tokens=_lowerCamelCase      )]

     return answers if len(_lowerCamelCase      ) > 1 else answers[0]





"""simple docstring"""






import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__							:Optional[int]       =  get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class     A(     lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       ErnieMTokenizer
		A         =       False
		A         =       True
		A         =       False
		A         =       False
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCamelCase				:int				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )
					tokenizer.save_pretrained(self.tmpdirname      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  '''this is a test'''
					_UpperCamelCase				:Optional[Any]				    =  '''this is a test'''
					return input_text, output_text
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  '''<pad>'''
					_UpperCamelCase				:Optional[Any]				    =  0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  list(self.get_tokenizer().get_vocab().keys()      )

					self.assertEqual(vocab_keys[0]				,							'''<pad>'''      )
					self.assertEqual(vocab_keys[1]				,							'''<unk>'''      )
					self.assertEqual(vocab_keys[-1]				,							'''▁eloquent'''      )
					self.assertEqual(len(SCREAMING_SNAKE_CASE__      )				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.assertEqual(self.get_tokenizer().vocab_size				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					if not self.test_rust_tokenizer:
								return

					_UpperCamelCase				:Tuple				    =  self.get_tokenizer()
					_UpperCamelCase				:str				    =  self.get_rust_tokenizer()

					_UpperCamelCase				:List[Any]				    =  '''I was born in 92000, and this is falsé.'''

					_UpperCamelCase				:Any				    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[Any]				    =  rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:List[str]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Any				    =  self.get_rust_tokenizer()
					_UpperCamelCase				:List[Any]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							do_lower_case=SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:List[str]				    =  tokenizer.tokenize('''This is a test'''      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']      )

					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )				,							[48, 25, 21, 12_89]      )

					_UpperCamelCase				:str				    =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
					# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']      )
					_UpperCamelCase				:Optional[int]				    =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]      )

					_UpperCamelCase				:Dict				    =  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:str				    =  tokenizer.encode('''sequence builders'''      )
					_UpperCamelCase				:Optional[Any]				    =  tokenizer.encode('''multi-sequence build'''      )

					_UpperCamelCase				:int				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
					assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
					    tokenizer.sep_token_id
					] + text_a + [tokenizer.sep_token_id]





		@slow
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]]}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=SCREAMING_SNAKE_CASE__				,							model_name='''susnato/ernie-m-base_pytorch'''				,							sequences=[
					        '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
					        '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
					        '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
					        '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
					        '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
					        '''conditioning on both left and right context in all layers.''',
					        '''The quick brown fox jumps over the lazy dog.''',
					    ]				,							)

