



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import 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 (
		    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTransfoXLForSequenceClassification,
		    TFTransfoXLLMHeadModel,
		    TFTransfoXLModel,
		)

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       ) ->				Any:
									"""simple docstring"""




									a__       :						Optional[Any]							=  parent
									a__       :						Union[str, Any]							=  1_3
									a__       :						Tuple							=  7
									a__       :						Dict							=  3_0
									a__       :						List[Any]							=  self.seq_length + self.mem_len
									a__       :						Tuple							=  1_5
									a__       :						Tuple							=  True
									a__       :						Any							=  True
									a__       :						str							=  9_9
									a__       :						Tuple							=  [1_0, 5_0, 8_0]
									a__       :						Tuple							=  3_2
									a__       :						Tuple							=  3_2
									a__       :						int							=  4
									a__       :						str							=  8
									a__       :						Any							=  1_2_8
									a__       :						Union[str, Any]							=  2
									a__       :						Any							=  2
									a__       :						int							=  None
									a__       :						List[str]							=  1
									a__       :						Optional[Any]							=  0
									a__       :						int							=  3
									a__       :						int							=  self.vocab_size - 1
									a__       :						Optional[int]							=  0.0_1

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Optional[int]							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )
									a__       :						Tuple							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )

									a__       :						Any							=  None
									if self.use_labels:
												a__       :						List[str]							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )

									a__       :						Any							=  TransfoXLConfig(
									    vocab_size=self.vocab_size ,       mem_len=self.mem_len ,       clamp_len=self.clamp_len ,       cutoffs=self.cutoffs ,       d_model=self.hidden_size ,       d_embed=self.d_embed ,       n_head=self.num_attention_heads ,       d_head=self.d_head ,       d_inner=self.d_inner ,       div_val=self.div_val ,       n_layer=self.num_hidden_layers ,       eos_token_id=self.eos_token_id ,       pad_token_id=self.vocab_size - 1 ,       init_range=self.init_range ,       num_labels=self.num_labels ,       )

									return (config, input_ids_a, input_ids_a, lm_labels)

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									random.seed(self.seed  )
									tf.random.set_seed(self.seed  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						str							=  TFTransfoXLModel(__lowercase  )

									a__	,						a__       :						Optional[Any]							=  model(__lowercase  ).to_tuple()

									a__       :						Any							=  {"""input_ids""": input_ids_a, """mems""": mems_a}

									a__	,						a__       :						List[str]							=  model(__lowercase  ).to_tuple()

									self.parent.assertEqual(hidden_states_a.shape ,       (self.batch_size, self.seq_length, self.hidden_size)  )
									self.parent.assertEqual(hidden_states_a.shape ,       (self.batch_size, self.seq_length, self.hidden_size)  )
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a] ,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,       )
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a] ,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,       )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						str							=  TFTransfoXLLMHeadModel(__lowercase  )

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

									a__       :						str							=  {"""input_ids""": input_ids_a, """labels""": lm_labels}
									a__	,						a__       :						List[str]							=  model(__lowercase  ).to_tuple()

									a__	,						a__       :						Dict							=  model([input_ids_a, mems_a]  ).to_tuple()

									a__       :						Tuple							=  {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

									a__	,						a__       :						Tuple							=  model(__lowercase  ).to_tuple()

									self.parent.assertEqual(lm_logits_a.shape ,       (self.batch_size, self.seq_length, self.vocab_size)  )
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a] ,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,       )

									self.parent.assertEqual(lm_logits_a.shape ,       (self.batch_size, self.seq_length, self.vocab_size)  )
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a] ,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,       )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						List[str]							=  TFTransfoXLForSequenceClassification(__lowercase  )
									a__       :						Optional[int]							=  model(__lowercase  )
									self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  self.prepare_config_and_inputs()
									((a__)	,						(a__)	,						(a__)	,						(a__))       :						Union[str, Any]							=  config_and_inputs
									a__       :						Dict							=  {"""input_ids""": input_ids_a}
									return config, inputs_dict




@require_tf
class        snake_case__    (A__ ,     A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:List[Any]      =					(
						    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
						)
						__lowerCAmelCase					:Any      =					() if is_tf_available() else ()
						__lowerCAmelCase					:List[str]      =					(
						    {
						        "feature-extraction": TFTransfoXLModel,
						        "text-classification": TFTransfoXLForSequenceClassification,
						        "text-generation": TFTransfoXLLMHeadModel,
						        "zero-shot": TFTransfoXLForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
						__lowerCAmelCase					:Optional[Any]      =					False
						__lowerCAmelCase					:Tuple      =					False
						__lowerCAmelCase					:int      =					False
						__lowerCAmelCase					:Tuple      =					False

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				str:
									"""simple docstring"""




									if pipeline_test_casse_name == "TextGenerationPipelineTests":
												# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
												# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
												# tokenizer.
												return True

									return False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  TFTransfoXLModelTester(self  )
									a__       :						Dict							=  ConfigTester(self ,       config_class=__lowercase ,       d_embed=3_7  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									self.config_tester.run_common_tests()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									self.model_tester.set_seed()
									a__       :						List[Any]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									self.model_tester.set_seed()
									a__       :						List[str]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__	,						a__       :						Dict							=  self.model_tester.prepare_config_and_inputs_for_common()
									a__       :						str							=  [TFTransfoXLForSequenceClassification]

									for model_class in self.all_model_classes:
												a__       :						List[Any]							=  model_class(__lowercase  )
												assert isinstance(model.get_input_embeddings() ,       tf.keras.layers.Layer  )
												if model_class in list_other_models_with_output_ebd:
															a__       :						List[str]							=  model.get_output_embeddings()
															assert isinstance(__lowercase ,       tf.keras.layers.Layer  )
															a__       :						Any							=  model.get_bias()
															assert name is None
												else:
															a__       :						Any							=  model.get_output_embeddings()
															assert x is None
															a__       :						Dict							=  model.get_bias()
															assert name is None

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									pass

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a__       :						str							=  TFTransfoXLModel.from_pretrained(__lowercase  )
												self.assertIsNotNone(__lowercase  )

						@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									pass




@require_tf
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						@unittest.skip("""Skip test until #12651 is resolved."""  )
						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						int							=  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""  )
									# fmt: off
									a__       :						Union[str, Any]							=  tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] ,       dtype=tf.intaa  )  # noqa: E231
									# fmt: on
									#  In 1991 , the remains of Russian Tsar Nicholas II and his family
									#  ( except for Alexei and Maria ) are discovered .
									#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
									#  remainder of the story . 1883 Western Siberia ,
									#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
									#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
									#  father initially slaps him for making such an accusation , Rasputin watches as the
									#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
									#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
									#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

									# fmt: off
									a__       :						Optional[int]							=  [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
									# fmt: on
									#  In 1991, the remains of Russian Tsar Nicholas II and his family (
									#  except for Alexei and Maria ) are discovered. The voice of young son,
									#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
									#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
									#  and a group of men to perform magic. Rasputin has a vision and
									#  denounces one of the men as a horse thief. Although his father initially
									#  slaps him for making such an accusation, Rasputin watches as the man
									#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
									#  of the Virgin Mary, prompting him to become a priest.
									#  Rasputin quickly becomes famous, with people, even a bishop, begging for
									#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
									# Nicholas II and his family were discovered. The voice of <unk> young son,
									# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

									a__       :						Any							=  model.generate(__lowercase ,       max_length=2_0_0 ,       do_sample=__lowercase  )
									self.assertListEqual(output_ids[0].numpy().tolist() ,       __lowercase  )









import json
import os
from functools import lru_cache
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

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


_lowercase		:				List[str]      		={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase		:				Optional[Any]      		={
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

_lowercase		:				int      		={"facebook/blenderbot-3B": 128}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCAmelCase_					(  )							->	Optional[int]:







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

def      lowerCAmelCase_					(  _lowercase    :       Tuple)							->	List[str]:







			"""simple docstring"""
			a__       :						int							=  set()
			a__       :						Tuple							=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char))
						a__       :						Any							=  char
			return pairs





class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					VOCAB_FILES_NAMES
						__lowerCAmelCase					:int      =					PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase					:int      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase					:Optional[Any]      =					["input_ids", "attention_mask"]

						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase="replace" ,       __lowercase="<s>" ,       __lowercase="</s>" ,       __lowercase="</s>" ,       __lowercase="<s>" ,       __lowercase="<unk>" ,       __lowercase="<pad>" ,       __lowercase="<mask>" ,       __lowercase=False ,       **__lowercase ,       ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Any							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else bos_token
									a__       :						Optional[Any]							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else eos_token
									a__       :						Tuple							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else sep_token
									a__       :						Any							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else cls_token
									a__       :						List[Any]							=  AddedToken(__lowercase ,       lstrip=__lowercase ,       rstrip=__lowercase  ) if isinstance(__lowercase ,       __lowercase  ) else unk_token
									a__       :						str							=  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
									a__       :						List[str]							=  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 ,       **__lowercase ,       )

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

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									a__       :						Union[str, Any]							=  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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									return len(self.encoder  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									return dict(self.encoder ,       **self.added_tokens_encoder  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									if token in self.cache:
												return self.cache[token]
									a__       :						List[Any]							=  tuple(__lowercase  )
									a__       :						Optional[int]							=  get_pairs(__lowercase  )

									if not pairs:
												return token

									while True:
												a__       :						List[Any]							=  min(__lowercase ,       key=lambda __lowercase  : self.bpe_ranks.get(__lowercase ,       float("""inf"""  )  )  )
												if bigram not in self.bpe_ranks:
															break
												a__	,						a__       :						Dict							=  bigram
												a__       :						List[Any]							=  []
												a__       :						int							=  0
												while i < len(__lowercase  ):
															try:
																		a__       :						str							=  word.index(__lowercase ,       __lowercase  )
															except ValueError:
																		new_word.extend(word[i:]  )
																		break
															else:
																		new_word.extend(word[i:j]  )
																		a__       :						Optional[int]							=  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
												a__       :						List[Any]							=  tuple(__lowercase  )
												a__       :						Any							=  new_word
												if len(__lowercase  ) == 1:
															break
												else:
															a__       :						List[Any]							=  get_pairs(__lowercase  )
									a__       :						Optional[Any]							=  """ """.join(__lowercase  )
									a__       :						Tuple							=  word
									return word

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						int							=  []
									for token in re.findall(self.pat ,       __lowercase  ):
												a__       :						List[Any]							=  """""".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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									return self.encoder.get(__lowercase ,       self.encoder.get(self.unk_token  )  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




									return self.decoder.get(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  """""".join(__lowercase  )
									a__       :						Tuple							=  bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8""" ,       errors=self.errors  )
									return text

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Tuple[str]:
									"""simple docstring"""




									if not os.path.isdir(__lowercase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
									a__       :						int							=  os.path.join(
									    __lowercase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
									a__       :						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"""  )

									a__       :						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!"""  )
																		a__       :						Tuple							=  token_index
															writer.write(""" """.join(__lowercase  ) + """\n"""  )
															index += 1

									return vocab_file, merge_file

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None ,       __lowercase = False  ) ->				List[int]:
									"""simple docstring"""




									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, 1] + ([0] * len(__lowercase  )) + [1]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[int]:
									"""simple docstring"""




									a__       :						Any							=  [self.sep_token_id]
									a__       :						List[Any]							=  [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]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=False ,       **__lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Tuple							=  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()):
												a__       :						Union[str, Any]							=  """ """ + text
									return (text, kwargs)

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[str]:
									"""simple docstring"""




									return token_ids_a + [self.eos_token_id]

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[int]:
									"""simple docstring"""




									a__       :						List[Any]							=  []
									for is_user, text in conversation.iter_texts():
												if is_user:
															# We need to space prefix as it's being done within blenderbot
															inputs.append(""" """ + text  )
												else:
															# Generated responses should contain them already.
															inputs.append(__lowercase  )

									a__       :						Optional[int]							=  """  """.join(__lowercase  )
									a__       :						Any							=  self.encode(__lowercase  )
									if len(__lowercase  ) > self.model_max_length:
												a__       :						List[str]							=  input_ids[-self.model_max_length :]
												logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''  )
									return input_ids




