"""simple docstring"""
from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel




@require_tf
class   __snake_case			:
      a__								=						MarianConfig
      a__								=						{}
      a__								=						"""gelu"""



      def __init__(		self       ,     lowercase       ,     lowercase=13       ,     lowercase=7       ,     lowercase=True       ,     lowercase=False       ,     lowercase=99       ,     lowercase=32       ,     lowercase=2       ,     lowercase=4       ,     lowercase=37       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=20       ,     lowercase=2       ,     lowercase=1       ,     lowercase=0       ,     )      ->     Tuple:





        '''simple docstring'''





        a__:	str     				=      parent
        a__:	Union[str, Any]     				=      batch_size
        a__:	Any     				=      seq_length
        a__:	int     				=      is_training
        a__:	Tuple     				=      use_labels
        a__:	List[Any]     				=      vocab_size
        a__:	List[Any]     				=      hidden_size
        a__:	Union[str, Any]     				=      num_hidden_layers
        a__:	Optional[Any]     				=      num_attention_heads
        a__:	int     				=      intermediate_size

        a__:	List[Any]     				=      hidden_dropout_prob
        a__:	List[str]     				=      attention_probs_dropout_prob
        a__:	Tuple     				=      max_position_embeddings
        a__:	int     				=      eos_token_id
        a__:	Optional[int]     				=      pad_token_id
        a__:	Dict     				=      bos_token_id



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





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

        a__:	Union[str, Any]     				=      ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size)

        a__:	Optional[int]     				=      self.config_cls(
            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_ids=[2]       ,     bos_token_id=self.bos_token_id       ,     pad_token_id=self.pad_token_id       ,     decoder_start_token_id=self.pad_token_id       ,     **self.config_updates       ,     )
        a__:	Dict     				=      prepare_marian_inputs_dict(lowercase       ,     lowercase       ,     lowercase)
        return config, inputs_dict



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        a__:	List[Any]     				=      TFMarianModel(config=lowercase).get_decoder()
        a__:	List[Any]     				=      inputs_dict['input_ids']

        a__:	Union[str, Any]     				=      input_ids[:1, :]
        a__:	Optional[int]     				=      inputs_dict['attention_mask'][:1, :]
        a__:	str     				=      inputs_dict['head_mask']
        a__:	Optional[Any]     				=      1

        # first forward pass
        a__:	Optional[int]     				=      model(lowercase       ,     attention_mask=lowercase       ,     head_mask=lowercase       ,     use_cache=lowercase)

        a__				,       a__:	List[Any]     				=      outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        a__:	List[str]     				=      ids_tensor((self.batch_size, 3)       ,     config.vocab_size)
        a__:	Dict     				=      tf.cast(ids_tensor((self.batch_size, 3)       ,     2)       ,     tf.inta)

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

        a__:	Any     				=      model(lowercase       ,     attention_mask=lowercase)[0]
        a__:	str     				=      model(lowercase       ,     attention_mask=lowercase       ,     past_key_values=lowercase)[0]

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

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

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







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	)      ->Optional[Any]:
  if attention_mask is None:
    a__:	Union[str, Any]     				=      tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE						,	config.pad_token_id  )						,	tf.inta  )
  if decoder_attention_mask is None:
    a__:	str     				=      tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape						,	dtype=tf.inta  ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,	config.pad_token_id  )						,	tf.inta  ),
        ]						,	axis=-1						,	)
  if head_mask is None:
    a__:	List[str]     				=      tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
  if decoder_head_mask is None:
    a__:	Any     				=      tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
  if cross_attn_head_mask is None:
    a__:	str     				=      tf.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": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }




@require_tf
class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      a__								=						(TFMarianMTModel,) if is_tf_available() else ()
      a__								=						(
          {
              """conversational""": TFMarianMTModel,
              """feature-extraction""": TFMarianModel,
              """summarization""": TFMarianMTModel,
              """text2text-generation""": TFMarianMTModel,
              """translation""": TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      a__								=						True
      a__								=						False
      a__								=						False



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





        '''simple docstring'''





        a__:	Optional[int]     				=      TFMarianModelTester(self)
        a__:	List[Any]     				=      ConfigTester(self       ,     config_class=lowercase)



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Dict     				=      self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase)







@require_tf
class   __snake_case			(	unittest.TestCase							):
      a__								=						1000  # show more chars for failing integration tests



      @classmethod
      def     lowerCamelCase_  (		cls)      ->     None:





        '''simple docstring'''





        a__:	Dict     				=      f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
        return cls



      @cached_property
      def     lowerCamelCase_  (		self)      ->     MarianTokenizer:





        '''simple docstring'''





        return AutoTokenizer.from_pretrained(self.model_name)



      @property
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        return self.tokenizer.eos_token_id



      @cached_property
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        warnings.simplefilter('error')
        a__:	TFMarianMTModel     				=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
        assert isinstance(lowercase       ,     lowercase)
        a__:	str     				=      model.config
        self.assertListEqual(c.bad_words_ids       ,     [[c.pad_token_id]])
        self.assertEqual(c.max_length       ,     5_12)
        self.assertEqual(c.decoder_start_token_id       ,     c.pad_token_id)
        return model



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     Any:





        '''simple docstring'''





        a__:	str     				=      self.translate_src_text(**lowercase)
        self.assertListEqual(self.expected_text       ,     lowercase)



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	List[Any]     				=      self.tokenizer(self.src_text       ,     **lowercase       ,     padding=lowercase       ,     return_tensors='tf')
        a__:	Optional[int]     				=      self.model.generate(
            model_inputs.input_ids       ,     attention_mask=model_inputs.attention_mask       ,     num_beams=2       ,     max_length=1_28)
        a__:	Dict     				=      self.tokenizer.batch_decode(generated_ids.numpy()       ,     skip_special_tokens=lowercase)
        return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""mt"""
      a__								=						"""en"""
      a__								=						["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
      a__								=						["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""en"""
      a__								=						"""zh"""
      a__								=						["""My name is Wolfgang and I live in Berlin"""]
      a__								=						["""我叫沃尔夫冈 我住在柏林"""]



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""en"""
      a__								=						"""ROMANCE"""
      a__								=						[
          """>>fr<< Don't spend so much time watching TV.""",
          """>>pt<< Your message has been sent.""",
          """>>es<< He's two years older than me.""",
      ]
      a__								=						[
          """Ne passez pas autant de temps à regarder la télé.""",
          """A sua mensagem foi enviada.""",
          """Es dos años más viejo que yo.""",
      ]



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        self._assert_generated_batch_equal_expected()



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Any     				=      TranslationPipeline(self.model       ,     self.tokenizer       ,     framework='tf')
        a__:	Dict     				=      pipeline(self.src_text)
        self.assertEqual(self.expected_text       ,     [x['translation_text'] for x in output])










'''simple docstring'''

import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A:   List[str]						=					logging.get_logger(__name__)

A:   List[Any]						=					{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

A:   Tuple						=					{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

A:   Optional[int]						=					{
    "microsoft/deberta-base": 5_1_2,
    "microsoft/deberta-large": 5_1_2,
    "microsoft/deberta-xlarge": 5_1_2,
    "microsoft/deberta-base-mnli": 5_1_2,
    "microsoft/deberta-large-mnli": 5_1_2,
    "microsoft/deberta-xlarge-mnli": 5_1_2,
}

A:   Any						=					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Any   	=      (
						    list(range(ord('!'				)    ,      ord('~'				) + 1				)				) + list(range(ord('¡'				)    ,      ord('¬'				) + 1				)				) + list(range(ord('®'				)    ,      ord('ÿ'				) + 1				)				)
						)
						lowercase_   : Union[str, Any]   	=      bs[:]
						lowercase_   : Dict   	=      0
						for b in range(2**8				):
												if b not in bs:
																		bs.append(a				)
																		cs.append(2**8 + n				)
																		n += 1
						lowercase_   : List[str]   	=      [chr(a				) for n in cs]
						return dict(zip(a    ,      a				)				)






def 						_UpperCAmelCase     (	a		:						Optional[int]				)      ->      Union[str, Any]:





						"""simple docstring"""
						lowercase_   : str   	=      set()
						lowercase_   : Optional[int]   	=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)				)
												lowercase_   : str   	=      char
						return pairs






class 				__magic_name__  (						UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   List[str]   =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_       :   Tuple   =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_       :   Dict   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_       :   Dict   =	['input_ids', 'attention_mask', 'token_type_ids']
	def __init__(				self	,   _lowercase	,   _lowercase	,   _lowercase="replace"	,   _lowercase="[CLS]"	,   _lowercase="[SEP]"	,   _lowercase="[SEP]"	,   _lowercase="[CLS]"	,   _lowercase="[UNK]"	,   _lowercase="[PAD]"	,   _lowercase="[MASK]"	,   _lowercase=False	,   _lowercase=False	,   **_lowercase	,   )    ->	Union[str, Any]:
							lowercase_   : List[str]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else bos_token
							lowercase_   : str   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else eos_token
							lowercase_   : Tuple   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else sep_token
							lowercase_   : List[Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else cls_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else unk_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_   : Union[str, Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else mask_token

							super().__init__(
							    errors=_lowercase	,   bos_token=_lowercase	,   eos_token=_lowercase	,   unk_token=_lowercase	,   sep_token=_lowercase	,   cls_token=_lowercase	,   pad_token=_lowercase	,   mask_token=_lowercase	,   add_prefix_space=_lowercase	,   add_bos_token=_lowercase	,   **_lowercase	,   )
							lowercase_   : str   	=      add_bos_token

							with open(_lowercase	,   encoding='utf-8'							) as vocab_handle:
													lowercase_   : Union[str, Any]   	=      json.load(_lowercase							)
							lowercase_   : List[Any]   	=      {v: k for k, v in self.encoder.items()}
							lowercase_   : int   	=      errors  # how to handle errors in decoding
							lowercase_   : str   	=      bytes_to_unicode()
							lowercase_   : List[Any]   	=      {v: k for k, v in self.byte_encoder.items()}
							with open(_lowercase	,   encoding='utf-8'							) as merges_handle:
													lowercase_   : Tuple   	=      merges_handle.read().split('\n'							)[1:-1]
							lowercase_   : List[str]   	=      [tuple(merge.split()							) for merge in bpe_merges]
							lowercase_   : Dict   	=      dict(zip(_lowercase	,   range(len(_lowercase							)							)							)							)
							lowercase_   : Any   	=      {}
							lowercase_   : str   	=      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowercase_   : Tuple   	=      re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'							)
	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							return len(self.encoder							)
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return dict(self.encoder	,   **self.added_tokens_encoder							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							if token in self.cache:
													return self.cache[token]
							lowercase_   : Any   	=      tuple(_lowercase							)
							lowercase_   : Union[str, Any]   	=      get_pairs(_lowercase							)

							if not pairs:
													return token

							while True:
													lowercase_   : int   	=      min(_lowercase	,   key=lambda _lowercase							: self.bpe_ranks.get(_lowercase	,   float('inf'							)							)							)
													if bigram not in self.bpe_ranks:
																			break
													lowercase_	,   lowercase_   : Any   	=      bigram
													lowercase_   : Dict   	=      []
													lowercase_   : Tuple   	=      0
													while i < len(_lowercase							):
																			try:
																									lowercase_   : str   	=      word.index(_lowercase	,   _lowercase							)
																			except ValueError:
																									new_word.extend(word[i:]							)
																									break
																			else:
																									new_word.extend(word[i:j]							)
																									lowercase_   : str   	=      j

																			if word[i] == first and i < len(_lowercase							) - 1 and word[i + 1] == second:
																									new_word.append(first + second							)
																									i += 2
																			else:
																									new_word.append(word[i]							)
																									i += 1
													lowercase_   : Optional[int]   	=      tuple(_lowercase							)
													lowercase_   : Tuple   	=      new_word
													if len(_lowercase							) == 1:
																			break
													else:
																			lowercase_   : Optional[Any]   	=      get_pairs(_lowercase							)
							lowercase_   : Tuple   	=      ' '.join(_lowercase							)
							lowercase_   : int   	=      word
							return word
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_   : Union[str, Any]   	=      [self.cls_token_id]
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None	,   _lowercase = False							)    ->	List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowercase	,   token_ids_a=_lowercase	,   already_has_special_tokens=_lowercase							)

							if token_ids_a is None:
													return [1] + ([0] * len(_lowercase							)) + [1]
							return [1] + ([0] * len(_lowercase							)) + [1] + ([0] * len(_lowercase							)) + [1]
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							lowercase_   : Optional[int]   	=      [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							) * [0] + len(token_ids_a + sep							) * [1]
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Optional[int]   	=      []
							for token in re.findall(self.pat	,   _lowercase							):
													lowercase_   : List[str]   	=      ''.join(
													    self.byte_encoder[b] for b in token.encode('utf-8'							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase							).split(' '							)							)
							return bpe_tokens
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[str]:
							return self.encoder.get(_lowercase	,   self.encoder.get(self.unk_token							)							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							return self.decoder.get(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	int:
							lowercase_   : List[str]   	=      ''.join(_lowercase							)
							lowercase_   : List[str]   	=      bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'	,   errors=self.errors							)
							return text
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	Tuple[str]:
							if not os.path.isdir(_lowercase							):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
													return
							lowercase_   : Any   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
							lowercase_   : Optional[Any]   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

							with open(_lowercase	,   'w'	,   encoding='utf-8'							) as f:
													f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=_lowercase	,   ensure_ascii=_lowercase							) + '\n'							)

							lowercase_   : List[str]   	=      0
							with open(_lowercase	,   'w'	,   encoding='utf-8'							) as writer:
													writer.write('#version: 0.2\n'							)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,   key=lambda _lowercase							: kv[1]							):
																			if index != token_index:
																									logger.warning(
																									    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																									    ' Please check that the tokenizer is not corrupted!'							)
																									lowercase_   : Any   	=      token_index
																			writer.write(' '.join(_lowercase							) + '\n'							)
																			index += 1

							return vocab_file, merge_file
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase=False	,   **_lowercase							)    ->	Union[str, Any]:
							lowercase_   : List[Any]   	=      kwargs.pop('add_prefix_space'	,   self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(_lowercase							) > 0 and not text[0].isspace()):
													lowercase_   : Dict   	=      ' ' + text
							return (text, kwargs)







	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[int]:
							lowercase_   : Optional[int]   	=      []
							for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(_lowercase	,   add_special_tokens=_lowercase							) + [self.eos_token_id]							)
							if len(_lowercase							) > self.model_max_length:
													lowercase_   : Optional[int]   	=      input_ids[-self.model_max_length :]
							return input_ids
