



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				__snake_case     :Optional[Any]			       ="platform"

				import jax
				import jax.numpy as jnp

				from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    shift_tokens_right,
				)






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

							'''simple docstring'''






							if attention_mask is None:
														A      						=    np.where(input_ids != config.pad_token_id			,	1			,	0				)
							if decoder_attention_mask is None:
														A      						=    np.where(decoder_input_ids != config.pad_token_id			,	1			,	0				)
							if head_mask is None:
														A      						=    np.ones((config.encoder_layers, config.encoder_attention_heads)				)
							if decoder_head_mask is None:
														A      						=    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
							if cross_attn_head_mask is None:
														A      						=    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}



class        lowerCAmelCase__					:


				def __init__(							self      :  Union[str, Any]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Any=13						, __UpperCamelCase      :  Optional[int]=7						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  str=False						, __UpperCamelCase      :  str=99						, __UpperCamelCase      :  Dict=16						, __UpperCamelCase      :  int=2						, __UpperCamelCase      :  Any=4						, __UpperCamelCase      :  Optional[Any]=4						, __UpperCamelCase      :  Dict="gelu"						, __UpperCamelCase      :  Any=0.1						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  Optional[int]=32						, __UpperCamelCase      :  Dict=2						, __UpperCamelCase      :  str=1						, __UpperCamelCase      :  Optional[int]=0						, __UpperCamelCase      :  List[Any]=0.0_2						, )		->     Optional[int]:
											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      						=    initializer_range


				def __UpperCamelCase						(							self      :  int       )		->     str:
											A      						=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						, self.vocab_size       )						, 3						, self.vocab_size       )
											A      						=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						, dtype=np.intaa       ))						, -1       )

											A      						=    shift_tokens_right(UpperCamelCase_						, 1						, 2       )

											A      						=    BlenderbotSmallConfig(
											    vocab_size=self.vocab_size						, d_model=self.hidden_size						, encoder_layers=self.num_hidden_layers						, decoder_layers=self.num_hidden_layers						, encoder_attention_heads=self.num_attention_heads						, decoder_attention_heads=self.num_attention_heads						, encoder_ffn_dim=self.intermediate_size						, decoder_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						, initializer_range=self.initializer_range						, use_cache=UpperCamelCase_						, )
											A      						=    prepare_blenderbot_inputs_dict(UpperCamelCase_						, UpperCamelCase_						, UpperCamelCase_       )
											return config, inputs_dict


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[int]:
											A						,   A      						=    self.prepare_config_and_inputs()
											return config, inputs_dict


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  List[str]       )		->     Tuple:
											A      						=    20
											A      						=    model_class_name(UpperCamelCase_       )

											A      						=    model.encode(inputs_dict['input_ids']       )

											A						,   A      						=    (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											A      						=    model.init_cache(decoder_input_ids.shape[0]						, UpperCamelCase_						, UpperCamelCase_       )
											A      						=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						, dtype='i4'       )

											A      						=    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )
											A      						=    model.decode(
											    decoder_input_ids[:, :-1]						, UpperCamelCase_						, decoder_attention_mask=UpperCamelCase_						, past_key_values=UpperCamelCase_						, decoder_position_ids=UpperCamelCase_						, )

											A      						=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype='i4'       )
											A      						=    model.decode(
											    decoder_input_ids[:, -1:]						, UpperCamelCase_						, decoder_attention_mask=UpperCamelCase_						, past_key_values=outputs_cache.past_key_values						, decoder_position_ids=UpperCamelCase_						, )

											A      						=    model.decode(UpperCamelCase_						, UpperCamelCase_       )

											A      						=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
											self.parent.assertTrue(diff < 1e-3						, msg=f'''Max diff is {diff}'''       )


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[str]       )		->     Any:
											A      						=    20
											A      						=    model_class_name(UpperCamelCase_       )

											A      						=    model.encode(inputs_dict['input_ids']       )

											A						,   A      						=    (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											A      						=    jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
											    ]						, axis=-1						, )

											A      						=    model.init_cache(decoder_input_ids.shape[0]						, UpperCamelCase_						, UpperCamelCase_       )
											A      						=    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )

											A      						=    model.decode(
											    decoder_input_ids[:, :-1]						, UpperCamelCase_						, decoder_attention_mask=UpperCamelCase_						, past_key_values=UpperCamelCase_						, decoder_position_ids=UpperCamelCase_						, )
											A      						=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype='i4'       )
											A      						=    model.decode(
											    decoder_input_ids[:, -1:]						, UpperCamelCase_						, past_key_values=outputs_cache.past_key_values						, decoder_attention_mask=UpperCamelCase_						, decoder_position_ids=UpperCamelCase_						, )

											A      						=    model.decode(UpperCamelCase_						, UpperCamelCase_						, decoder_attention_mask=UpperCamelCase_       )

											A      						=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
											self.parent.assertTrue(diff < 1e-3						, msg=f'''Max diff is {diff}'''       )





@require_flax
class        lowerCAmelCase__					(  unittest.TestCase       ):
				A_   :     Dict								=     9_9


				def __UpperCamelCase						(							self      :  List[str]       )		->     Dict:
											A      						=    np.array(
											    [
											        [71, 82, 18, 33, 46, 91, 2],
											        [68, 34, 26, 58, 30, 82, 2],
											        [5, 97, 17, 39, 94, 40, 2],
											        [76, 83, 94, 25, 70, 78, 2],
											        [87, 59, 41, 35, 48, 66, 2],
											        [55, 13, 16, 58, 5, 2, 1],  # note padding
											        [64, 27, 31, 51, 12, 75, 2],
											        [52, 64, 86, 17, 83, 39, 2],
											        [48, 61, 9, 24, 71, 82, 2],
											        [26, 1, 60, 48, 22, 13, 2],
											        [21, 5, 62, 28, 14, 76, 2],
											        [45, 98, 37, 86, 59, 48, 2],
											        [70, 70, 50, 9, 28, 0, 2],
											    ]						, dtype=np.intaa						, )

											A      						=    input_ids.shape[0]
											A      						=    BlenderbotSmallConfig(
											    vocab_size=self.vocab_size						, d_model=24						, encoder_layers=2						, decoder_layers=2						, encoder_attention_heads=2						, decoder_attention_heads=2						, encoder_ffn_dim=32						, decoder_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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A						,   A						,   A      						=    self._get_config_and_data()
											A      						=    FlaxBlenderbotSmallForConditionalGeneration(UpperCamelCase_       )
											A      						=    lm_model(input_ids=UpperCamelCase_       )
											A      						=    (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape						, UpperCamelCase_       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[Any]:
											A      						=    BlenderbotSmallConfig(
											    vocab_size=self.vocab_size						, d_model=14						, encoder_layers=2						, decoder_layers=2						, encoder_attention_heads=2						, decoder_attention_heads=2						, encoder_ffn_dim=8						, decoder_ffn_dim=8						, max_position_embeddings=48						, )
											A      						=    FlaxBlenderbotSmallForConditionalGeneration(UpperCamelCase_       )
											A      						=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						, dtype=np.intaa       )
											A      						=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						, dtype=np.intaa       )
											A      						=    lm_model(input_ids=UpperCamelCase_						, decoder_input_ids=UpperCamelCase_       )
											A      						=    (*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape						, UpperCamelCase_       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											A      						=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						, dtype=np.intaa       )
											A      						=    shift_tokens_right(UpperCamelCase_						, 1						, 2       )
											A      						=    np.equal(UpperCamelCase_						, 1       ).astype(np.floataa       ).sum()
											A      						=    np.equal(UpperCamelCase_						, 1       ).astype(np.floataa       ).sum()
											self.assertEqual(shifted.shape						, input_ids.shape       )
											self.assertEqual(UpperCamelCase_						, n_pad_before - 1       )
											self.assertTrue(np.equal(shifted[:, 0]						, 2       ).all()       )





@require_flax
class        lowerCAmelCase__					(  lowercase_ ,				unittest.TestCase ,				lowercase_       ):
				A_   :     Any								=     True
				A_   :     Optional[int]								=     (
				    (
				        FlaxBlenderbotSmallModel,
				        FlaxBlenderbotSmallForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				A_   :     int								=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[Any]:
											A      						=    FlaxBlenderbotSmallModelTester(self       )


				def __UpperCamelCase						(							self      :  str       )		->     Any:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(UpperCamelCase_						, UpperCamelCase_						, UpperCamelCase_       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_						, UpperCamelCase_						, UpperCamelCase_       )


				def __UpperCamelCase						(							self      :  Any       )		->     List[Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									A      						=    self._prepare_for_class(UpperCamelCase_						, UpperCamelCase_       )
																									A      						=    model_class(UpperCamelCase_       )

																									@jax.jit
																									def encode_jitted(__UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Union[str, Any]=None						, **__UpperCamelCase      :  Union[str, Any]       ):
																																return model.encode(input_ids=UpperCamelCase_						, attention_mask=UpperCamelCase_       )

																									with self.subTest('JIT Enabled'       ):
																																A      						=    encode_jitted(**UpperCamelCase_       ).to_tuple()

																									with self.subTest('JIT Disabled'       ):
																																with jax.disable_jit():
																																							A      						=    encode_jitted(**UpperCamelCase_       ).to_tuple()

																									self.assertEqual(len(UpperCamelCase_       )						, len(UpperCamelCase_       )       )
																									for jitted_output, output in zip(UpperCamelCase_						, UpperCamelCase_       ):
																																self.assertEqual(jitted_output.shape						, output.shape       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Union[str, Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									A      						=    model_class(UpperCamelCase_       )
																									A      						=    model.encode(inputs_dict['input_ids']						, inputs_dict['attention_mask']       )

																									A      						=    {
																									    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																									    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																									    'encoder_outputs': encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(__UpperCamelCase      :  int						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  int       ):
																																return model.decode(
																																    decoder_input_ids=UpperCamelCase_						, decoder_attention_mask=UpperCamelCase_						, encoder_outputs=UpperCamelCase_						, )

																									with self.subTest('JIT Enabled'       ):
																																A      						=    decode_jitted(**UpperCamelCase_       ).to_tuple()

																									with self.subTest('JIT Disabled'       ):
																																with jax.disable_jit():
																																							A      						=    decode_jitted(**UpperCamelCase_       ).to_tuple()

																									self.assertEqual(len(UpperCamelCase_       )						, len(UpperCamelCase_       )       )
																									for jitted_output, output in zip(UpperCamelCase_						, UpperCamelCase_       ):
																																self.assertEqual(jitted_output.shape						, output.shape       )


				@slow
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:



											for model_class_name in self.all_model_classes:
																		A      						=    model_class_name.from_pretrained('facebook/blenderbot_small-90M'       )
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		A      						=    np.ones((1, 1)       ) * model.config.eos_token_id
																		A      						=    model(UpperCamelCase_       )
																		self.assertIsNotNone(UpperCamelCase_       )







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_roberta import RobertaTokenizer


__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							List[str]						= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a   :							Dict						= {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__a   :							List[str]						= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE     =					RobertaTokenizer




     def __init__(					self							:      List[Any]			,						UpperCamelCase_							:      Any=None			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      Optional[int]=None			,						UpperCamelCase_							:      Union[str, Any]="replace"			,						UpperCamelCase_							:      Any="<s>"			,						UpperCamelCase_							:      Tuple="</s>"			,						UpperCamelCase_							:      Optional[Any]="</s>"			,						UpperCamelCase_							:      Dict="<s>"			,						UpperCamelCase_							:      int="<unk>"			,						UpperCamelCase_							:      Optional[int]="<pad>"			,						UpperCamelCase_							:      str="<mask>"			,						UpperCamelCase_							:      Tuple=False			,						UpperCamelCase_							:      Optional[Any]=True			,						**UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



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

         __A       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
             __A       =						getattr(UpperCamelCase_			,						pre_tok_state.pop("""type"""					)					)
             __A       =						add_prefix_space
             __A       =						pre_tok_class(**UpperCamelCase_					)

         __A       =						add_prefix_space

         __A       =						"""post_processor"""
         __A       =						getattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)
         if tokenizer_component_instance:
             __A       =						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:
                 __A       =						tuple(state["""sep"""]					)
             if "cls" in state:
                 __A       =						tuple(state["""cls"""]					)

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

             if changes_to_apply:
                 __A       =						getattr(UpperCamelCase_			,						state.pop("""type"""					)					)
                 __A       =						component_class(**UpperCamelCase_					)
                 setattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					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 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Tuple			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Optional[int]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      int=None					):


         """simple docstring"""



         __A       =						[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 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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]




