




"""simple docstring"""

from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   Union[str, Any]							,    _snake_case				:   Optional[Any]=None							,    _snake_case				:   Dict=None				)       ->   int:






      '''simple docstring'''


      if attention_mask is None:
            _A     =  tf.cast(tf.math.not_equal(snake_case__							,    config.pad_token_id				)							,    tf.inta				)
      return {"input_ids": input_ids, "attention_mask": attention_mask}





@require_tf
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	int     =      OPTConfig
 UpperCAmelCase       :	Any     =      {}
 UpperCAmelCase       :	Any     =      '''gelu'''

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Any=13       ,				_UpperCAmelCase			:			List[Any]=7       ,				_UpperCAmelCase			:			List[Any]=True       ,				_UpperCAmelCase			:			int=False       ,				_UpperCAmelCase			:			Dict=99       ,				_UpperCAmelCase			:			List[str]=16       ,				_UpperCAmelCase			:			List[str]=2       ,				_UpperCAmelCase			:			Union[str, Any]=4       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			str="gelu"       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Dict=20       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Union[str, Any]=1       ,				_UpperCAmelCase			:			Dict=0       ,				_UpperCAmelCase			:			List[str]=16       ,				_UpperCAmelCase			:			Any=16       ,				):
       _A     =  parent
       _A     =  batch_size
       _A     =  seq_length
       _A     =  is_training
       _A     =  use_labels
       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  eos_token_id
       _A     =  pad_token_id
       _A     =  bos_token_id
       _A     =  embed_dim
       _A     =  word_embed_proj_dim
       _A     =  False

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  ids_tensor([self.batch_size, self.seq_length - 1]       ,				self.vocab_size						)
       _A     =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)       ,				1						)
       _A     =  tf.concat([input_ids, eos_tensor]       ,				axis=1						)

       _A     =  self.config_cls(
           vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				ffn_dim=self.intermediate_size       ,				dropout=self.hidden_dropout_prob       ,				attention_dropout=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				eos_token_id=self.eos_token_id       ,				bos_token_id=self.bos_token_id       ,				pad_token_id=self.pad_token_id       ,				embed_dim=self.embed_dim       ,				word_embed_proj_dim=self.word_embed_proj_dim       ,				is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,				**self.config_updates       ,				)
       _A     =  prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__						)
       return config, inputs_dict



 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  TFOPTModel(config=SCREAMING_SNAKE_CASE__						)
       _A     =  inputs_dict['''input_ids''']

       _A     =  input_ids[:1, :]
       _A     =  inputs_dict['''attention_mask'''][:1, :]
       _A     =  1

       # first forward pass
       _A     =  model(SCREAMING_SNAKE_CASE__       ,				attention_mask=SCREAMING_SNAKE_CASE__       ,				use_cache=SCREAMING_SNAKE_CASE__						)

       _A     =  outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       _A     =  ids_tensor((self.batch_size, 3)       ,				config.vocab_size						)
       _A     =  tf.cast(ids_tensor((self.batch_size, 3)       ,				2						)       ,				tf.inta						)

       # append to next input_ids and
       _A     =  tf.concat([input_ids, next_tokens]       ,				axis=-1						)
       _A     =  tf.concat([attention_mask, next_attn_mask]       ,				axis=-1						)

       _A     =  model(SCREAMING_SNAKE_CASE__       ,				attention_mask=SCREAMING_SNAKE_CASE__						)[0]
       _A     =  model(SCREAMING_SNAKE_CASE__       ,				attention_mask=SCREAMING_SNAKE_CASE__       ,				past_key_values=SCREAMING_SNAKE_CASE__						)[0]

       self.parent.assertEqual(next_tokens.shape[1]       ,				output_from_past.shape[1]						)

       # select random slice
       _A     =  int(ids_tensor((1,)       ,				output_from_past.shape[-1]						)						)
       _A     =  output_from_no_past[:, -3:, random_slice_idx]
       _A     =  output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__       ,				rtol=1E-3						)





@require_tf
class 							lowercase_			(	lowerCamelCase__     ,      lowerCamelCase__     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
 UpperCAmelCase       :	Union[str, Any]     =      (TFOPTForCausalLM,) if is_tf_available() else ()
 UpperCAmelCase       :	Union[str, Any]     =      (
     {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
 )
 UpperCAmelCase       :	Tuple     =      False
 UpperCAmelCase       :	Any     =      False
 UpperCAmelCase       :	Optional[int]     =      False
 UpperCAmelCase       :	Optional[Any]     =      10

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  TFOPTModelTester(self						)
       _A     =  ConfigTester(self       ,				config_class=SCREAMING_SNAKE_CASE__						)

 def      lowerCAmelCase_				(					self			:			Any						):
       self.config_tester.run_common_tests()

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__						)



 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

       def _get_word_embedding_weight(_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[int]						):
             if hasattr(SCREAMING_SNAKE_CASE__       ,				'weight'						):
                   return embedding_layer.weight
             else:
                   # Here we build the word embeddings weights if not exists.
                   # And then we retry to get the attribute once built.
                   model.build()
                   if hasattr(SCREAMING_SNAKE_CASE__       ,				'weight'						):
                         return embedding_layer.weight
                   else:
                         return None

       for model_class in self.all_model_classes:
             for size in [config.vocab_size - 10, config.vocab_size + 10]:
                   # build the embeddings
                   _A     =  model_class(config=SCREAMING_SNAKE_CASE__						)
                   _A     =  _get_word_embedding_weight(SCREAMING_SNAKE_CASE__       ,				model.get_input_embeddings()						)
                   _A     =  _get_word_embedding_weight(SCREAMING_SNAKE_CASE__       ,				model.get_output_embeddings()						)

                   # reshape the embeddings
                   model.resize_token_embeddings(SCREAMING_SNAKE_CASE__						)
                   _A     =  _get_word_embedding_weight(SCREAMING_SNAKE_CASE__       ,				model.get_input_embeddings()						)
                   _A     =  _get_word_embedding_weight(SCREAMING_SNAKE_CASE__       ,				model.get_output_embeddings()						)

                   # check that the resized embeddings size matches the desired size.
                   _A     =  size if size is not None else config.vocab_size

                   self.assertEqual(new_input_embeddings.shape[0]       ,				SCREAMING_SNAKE_CASE__						)

                   # check that weights remain the same after resizing
                   _A     =  True
                   for pa, pa in zip(old_input_embeddings.value()       ,				new_input_embeddings.value()						):
                         if tf.math.reduce_sum(tf.math.abs(pa - pa						)						) > 0:
                               _A     =  False
                   self.assertTrue(SCREAMING_SNAKE_CASE__						)

                   if old_output_embeddings is not None and new_output_embeddings is not None:
                         self.assertEqual(new_output_embeddings.shape[0]       ,				SCREAMING_SNAKE_CASE__						)

                         _A     =  True
                         for pa, pa in zip(old_output_embeddings.value()       ,				new_output_embeddings.value()						):
                               if tf.math.reduce_sum(tf.math.abs(pa - pa						)						) > 0:
                                     _A     =  False
                         self.assertTrue(SCREAMING_SNAKE_CASE__						)






def 		_snake_case	(   _snake_case				:   int				)       ->   Tuple:






      '''simple docstring'''


      return tf.constant(snake_case__							,    dtype=tf.intaa				)





@require_tf
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Tuple     =      99

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  tf.ones((4, 1)       ,				dtype=tf.intaa						) * 2
       _A     =  tf.concat([ids_tensor((4, 6)       ,				self.vocab_size - 3						) + 3, eos_column_vector]       ,				axis=1						)
       _A     =  input_ids.shape[0]
       _A     =  OPTConfig(
           vocab_size=self.vocab_size       ,				hidden_size=24       ,				num_hidden_layers=2       ,				num_attention_heads=2       ,				ffn_dim=32       ,				max_position_embeddings=48       ,				eos_token_id=2       ,				pad_token_id=1       ,				bos_token_id=0       ,				)
       return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 @slow
 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  TFOPTModel.from_pretrained('facebook/opt-350m'						)
       _A     =  _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
       _A     =  tf.not_equal(SCREAMING_SNAKE_CASE__       ,				model.config.pad_token_id						)
       with tf.GradientTape():
             _A     =  model(input_ids=SCREAMING_SNAKE_CASE__       ,				attention_mask=SCREAMING_SNAKE_CASE__						).last_hidden_state
       _A     =  (1, 11, 512)
       self.assertEqual(output.shape       ,				SCREAMING_SNAKE_CASE__						)
       _A     =  tf.constant(
           [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]						)
       self.assertTrue(np.allclose(output[:, :3, :3]       ,				SCREAMING_SNAKE_CASE__       ,				atol=4E-3						)						)

       _A     =  tf.function(SCREAMING_SNAKE_CASE__       ,				jit_compile=SCREAMING_SNAKE_CASE__						)
       _A     =  xla_generate(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__						)[0]
       self.assertTrue(np.allclose(output[:, :3, :3]       ,				SCREAMING_SNAKE_CASE__       ,				atol=4E-2						)						)





@require_tf
@slow
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       super().setUp()
       _A     =  '''facebook/opt-350m'''



 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  TFOPTForCausalLM.from_pretrained(self.path_model						)
       _A     =  GPTaTokenizer.from_pretrained(self.path_model						)

       _A     =  [
           '''Today is a beautiful day and I want to''',
           '''In the city of''',
           '''Paris is the capital of France and''',
           '''Computers and mobile phones have taken''',
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       _A     =  tokenizer(SCREAMING_SNAKE_CASE__       ,				return_tensors='tf'       ,				padding=SCREAMING_SNAKE_CASE__       ,				add_special_tokens=SCREAMING_SNAKE_CASE__						)
       _A     =  tf.math.reduce_mean(model(inputs.input_ids       ,				attention_mask=inputs.attention_mask						)[0]       ,				axis=-1						)
       _A     =  tf.constant(
           [
               [1.3851, -13.8_923, -10.5_229, -10.7_533, -0.2309, -10.2_384, -0.5365, -9.0947, -5.1670],
               [-4.7073, -10.6_276, -3.9415, -21.5_242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
               [0.6247, -3.4229, -8.9179, -1.4297, -14.1_650, 1.4146, -9.0218, -0.2703, -0.2703],
               [6.4783, -1.9913, -10.7_926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
           ]						)
       self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__       ,				atol=1E-4						)						)

       _A     =  tf.function(SCREAMING_SNAKE_CASE__       ,				jit_compile=SCREAMING_SNAKE_CASE__						)
       _A     =  tf.math.reduce_mean(xla_generate(inputs.input_ids       ,				attention_mask=inputs.attention_mask						)[0]       ,				axis=-1						)
       self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__       ,				atol=1E-4						)						)





@require_tf
@slow
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			int						):
       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  '''facebook/opt-125m'''

       _A     =  [
           '''Today is a beautiful day and I want to''',
           '''In the city of New York, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       _A     =  []
       _A     =  GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)
       _A     =  TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__						)

       for prompt in self.prompts:
             _A     =  tokenizer(SCREAMING_SNAKE_CASE__       ,				return_tensors='tf'						).input_ids

             _A     =  model.generate(SCREAMING_SNAKE_CASE__       ,				max_length=10						)

             _A     =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       ,				skip_special_tokens=SCREAMING_SNAKE_CASE__						)
             predicted_outputs += generated_string

       self.assertListEqual(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  '''facebook/opt-350m'''

       _A     =  GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)
       _A     =  TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__						)

       _A     =  '''left'''

       # use different length sentences to test batching
       _A     =  [
           '''Hello, my dog is a little''',
           '''Today, I''',
       ]

       _A     =  tokenizer(SCREAMING_SNAKE_CASE__       ,				return_tensors='tf'       ,				padding=SCREAMING_SNAKE_CASE__						)
       _A     =  inputs['''input_ids''']

       _A     =  model.generate(input_ids=SCREAMING_SNAKE_CASE__       ,				attention_mask=inputs['attention_mask']						)

       _A     =  tokenizer(sentences[0]       ,				return_tensors='tf'						).input_ids
       _A     =  model.generate(input_ids=SCREAMING_SNAKE_CASE__						)

       _A     =  inputs_non_padded.shape[-1] - tf.math.reduce_sum(
           tf.cast(inputs['attention_mask'][-1]       ,				tf.intaa						)						)
       _A     =  tokenizer(sentences[1]       ,				return_tensors='tf'						).input_ids
       _A     =  model.generate(input_ids=SCREAMING_SNAKE_CASE__       ,				max_length=model.config.max_length - num_paddings						)

       _A     =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       ,				skip_special_tokens=SCREAMING_SNAKE_CASE__						)
       _A     =  tokenizer.decode(output_non_padded[0]       ,				skip_special_tokens=SCREAMING_SNAKE_CASE__						)
       _A     =  tokenizer.decode(output_padded[0]       ,				skip_special_tokens=SCREAMING_SNAKE_CASE__						)

       _A     =  [
           '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
           '''Today, I was in the middle of a conversation with a friend about the''',
       ]
       self.assertListEqual(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__						)
       self.assertListEqual(SCREAMING_SNAKE_CASE__       ,				[non_padded_sentence, padded_sentence]						)



 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  '''facebook/opt-350m'''

       _A     =  [
           '''Today is a beautiful day and I want to''',
           '''In the city of San Francisco, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       _A     =  []
       _A     =  GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)
       _A     =  TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__						)

       for prompt in self.prompts:
             _A     =  tokenizer(SCREAMING_SNAKE_CASE__       ,				return_tensors='tf'						).input_ids

             _A     =  model.generate(SCREAMING_SNAKE_CASE__       ,				max_length=10						)

             _A     =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       ,				skip_special_tokens=SCREAMING_SNAKE_CASE__						)
             predicted_outputs += generated_string

       self.assertListEqual(SCREAMING_SNAKE_CASE__       ,				SCREAMING_SNAKE_CASE__						)





"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


UpperCamelCase__				:List[Any]			   =	logging.get_logger(__name__)


UpperCamelCase__				:Tuple			   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__				:List[str]			   =	{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase__				:List[Any]			   =	{
    """RUCAIBox/mvp""": 1_024,
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		A    =							MvpTokenizer
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__="replace"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="<unk>"					,				SCREAMING_SNAKE_CASE__="<pad>"					,				SCREAMING_SNAKE_CASE__="<mask>"					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	List[str]:

					"""simple docstring"""



					super().__init__(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				tokenizer_file=SCREAMING_SNAKE_CASE__					,				errors=SCREAMING_SNAKE_CASE__					,				bos_token=SCREAMING_SNAKE_CASE__					,				eos_token=SCREAMING_SNAKE_CASE__					,				sep_token=SCREAMING_SNAKE_CASE__					,				cls_token=SCREAMING_SNAKE_CASE__					,				unk_token=SCREAMING_SNAKE_CASE__					,				pad_token=SCREAMING_SNAKE_CASE__					,				mask_token=SCREAMING_SNAKE_CASE__					,				add_prefix_space=SCREAMING_SNAKE_CASE__					,				trim_offsets=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:int					     =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
					if pre_tok_state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
								_UpperCamelCase					:int					     =	getattr(SCREAMING_SNAKE_CASE__					,				pre_tok_state.pop('''type'''   )   )
								_UpperCamelCase					:Optional[int]					     =	add_prefix_space
								_UpperCamelCase					:List[Any]					     =	pre_tok_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					_UpperCamelCase					:Any					     =	'''post_processor'''
					_UpperCamelCase					:Optional[Any]					     =	getattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					if tokenizer_component_instance:
								_UpperCamelCase					:Optional[Any]					     =	json.loads(tokenizer_component_instance.__getstate__()   )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''sep''']   )
								if "cls" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''cls''']   )

								_UpperCamelCase					:str					     =	False

								if state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
											_UpperCamelCase					:Optional[int]					     =	add_prefix_space
											_UpperCamelCase					:Union[str, Any]					     =	True

								if state.get('''trim_offsets'''					,				SCREAMING_SNAKE_CASE__   ) != trim_offsets:
											_UpperCamelCase					:Union[str, Any]					     =	trim_offsets
											_UpperCamelCase					:Optional[Any]					     =	True

								if changes_to_apply:
											_UpperCamelCase					:Union[str, Any]					     =	getattr(SCREAMING_SNAKE_CASE__					,				state.pop('''type'''   )   )
											_UpperCamelCase					:Optional[Any]					     =	component_class(**SCREAMING_SNAKE_CASE__   )
											setattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					if self._mask_token is None:
								if self.verbose:
											logger.error('''Using mask_token, but it is not set yet.'''   )
								return None
					return str(self._mask_token   )
		@mask_token.setter
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	AddedToken(SCREAMING_SNAKE_CASE__					,				lstrip=SCREAMING_SNAKE_CASE__					,				rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) else value
					_UpperCamelCase					:int					     =	value
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					,				name=SCREAMING_SNAKE_CASE__   )
					return tuple(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
								return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[int]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	[self.sep_token_id]
					_UpperCamelCase					:List[str]					     =	[self.cls_token_id]

					if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]

