'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
       OpenAIGPTConfig,
       OpenAIGPTDoubleHeadsModel,
       OpenAIGPTForSequenceClassification,
       OpenAIGPTLMHeadModel,
       OpenAIGPTModel,
   )






class 		__A       :






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_=13				,     UpperCamelCase_=7				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=99				,     UpperCamelCase_=32				,     UpperCamelCase_=5				,     UpperCamelCase_=4				,     UpperCamelCase_=37				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=16				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=3				,     UpperCamelCase_=4				,     UpperCamelCase_=None				,     ):
              __UpperCAmelCase  :				Union[str, Any]       				=		parent
              __UpperCAmelCase  :				Dict       				=		batch_size
              __UpperCAmelCase  :				str       				=		seq_length
              __UpperCAmelCase  :				Union[str, Any]       				=		is_training
              __UpperCAmelCase  :				Tuple       				=		use_token_type_ids
              __UpperCAmelCase  :				Union[str, Any]       				=		use_labels
              __UpperCAmelCase  :				str       				=		vocab_size
              __UpperCAmelCase  :				Optional[Any]       				=		hidden_size
              __UpperCAmelCase  :				Tuple       				=		num_hidden_layers
              __UpperCAmelCase  :				Optional[Any]       				=		num_attention_heads
              __UpperCAmelCase  :				int       				=		intermediate_size
              __UpperCAmelCase  :				Optional[Any]       				=		hidden_act
              __UpperCAmelCase  :				Dict       				=		hidden_dropout_prob
              __UpperCAmelCase  :				List[str]       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				Optional[Any]       				=		max_position_embeddings
              __UpperCAmelCase  :				str       				=		type_vocab_size
              __UpperCAmelCase  :				List[Any]       				=		type_sequence_label_size
              __UpperCAmelCase  :				Optional[int]       				=		initializer_range
              __UpperCAmelCase  :				int       				=		num_labels
              __UpperCAmelCase  :				Any       				=		num_choices
              __UpperCAmelCase  :				Dict       				=		scope
              __UpperCAmelCase  :				str       				=		self.vocab_size - 1






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size      )

              __UpperCAmelCase  :				str       				=		None
              if self.use_token_type_ids:
                     __UpperCAmelCase  :				List[str]       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size      )

              __UpperCAmelCase  :				Dict       				=		None
              __UpperCAmelCase  :				Optional[int]       				=		None
              __UpperCAmelCase  :				str       				=		None
              if self.use_labels:
                     __UpperCAmelCase  :				Union[str, Any]       				=		ids_tensor([self.batch_size]				,     self.type_sequence_label_size      )
                     __UpperCAmelCase  :				Dict       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels      )
                     __UpperCAmelCase  :				Optional[Any]       				=		ids_tensor([self.batch_size]				,     self.num_choices      )

              __UpperCAmelCase  :				List[str]       				=		OpenAIGPTConfig(
                  vocab_size=self.vocab_size				,     n_embd=self.hidden_size				,     n_layer=self.num_hidden_layers				,     n_head=self.num_attention_heads				,     n_positions=self.max_position_embeddings				,     pad_token_id=self.pad_token_id				,     )

              __UpperCAmelCase  :				Optional[Any]       				=		ids_tensor([self.num_hidden_layers, self.num_attention_heads]				,     2      )

              return (
                  config,
                  input_ids,
                  head_mask,
                  token_type_ids,
                  sequence_labels,
                  token_labels,
                  choice_labels,
              )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     *UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		OpenAIGPTModel(config=UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.eval()

              __UpperCAmelCase  :				List[str]       				=		model(UpperCamelCase_				,     token_type_ids=UpperCamelCase_				,     head_mask=UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		model(UpperCamelCase_				,     token_type_ids=UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		model(UpperCamelCase_      )

              self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     *UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		OpenAIGPTLMHeadModel(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.eval()

              __UpperCAmelCase  :				List[Any]       				=		model(UpperCamelCase_				,     token_type_ids=UpperCamelCase_				,     labels=UpperCamelCase_      )
              self.parent.assertEqual(result.loss.shape				,     ()      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     *UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		OpenAIGPTDoubleHeadsModel(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.eval()

              __UpperCAmelCase  :				str       				=		model(UpperCamelCase_				,     token_type_ids=UpperCamelCase_				,     labels=UpperCamelCase_      )
              self.parent.assertEqual(result.loss.shape				,     ()      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     *UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		self.num_labels
              __UpperCAmelCase  :				int       				=		OpenAIGPTForSequenceClassification(UpperCamelCase_      )
              model.to(UpperCamelCase_      )
              model.eval()

              __UpperCAmelCase  :				str       				=		ids_tensor([self.batch_size]				,     self.type_sequence_label_size      )
              __UpperCAmelCase  :				Tuple       				=		model(UpperCamelCase_				,     token_type_ids=UpperCamelCase_				,     labels=UpperCamelCase_      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		self.prepare_config_and_inputs()
              (
                  (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, 
              )  :				Union[str, Any]       				=		config_and_inputs
              __UpperCAmelCase  :				List[str]       				=		{
                  "input_ids": input_ids,
                  "token_type_ids": token_type_ids,
                  "head_mask": head_mask,
              }

              return config, inputs_dict






@require_torch
class 		__A       (__magic_name__ ,					__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					(
           (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
           if is_torch_available()
           else ()
       )
       snake_case       :List[str]				    =					(
           (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       snake_case       :Optional[int]				    =					(
           {
               "feature-extraction": OpenAIGPTModel,
               "text-classification": OpenAIGPTForSequenceClassification,
               "text-generation": OpenAIGPTLMHeadModel,
               "zero-shot": OpenAIGPTForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                     # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                     # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                     # tiny config could not be created.
                     return True

              return False






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=False      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		super()._prepare_for_class(UpperCamelCase_				,     UpperCamelCase_				,     return_labels=UpperCamelCase_      )

              if return_labels:
                     if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                            __UpperCAmelCase  :				List[str]       				=		torch.zeros(
                                (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)				,     dtype=torch.long				,     device=UpperCamelCase_				,     )
                            __UpperCAmelCase  :				str       				=		inputs_dict["labels"]
                            __UpperCAmelCase  :				int       				=		inputs_dict["labels"]
                            __UpperCAmelCase  :				List[Any]       				=		torch.zeros(
                                (self.model_tester.batch_size, self.model_tester.num_choices)				,     dtype=torch.long				,     device=UpperCamelCase_				,     )
                            __UpperCAmelCase  :				Any       				=		torch.zeros(
                                self.model_tester.batch_size				,     dtype=torch.long				,     device=UpperCamelCase_      )
              return inputs_dict






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		OpenAIGPTModelTester(self      )
              __UpperCAmelCase  :				Optional[int]       				=		ConfigTester(self				,     config_class=UpperCamelCase_				,     n_embd=37      )






       def 			_snake_case						(      self      ):
              self.config_tester.run_common_tests()






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_lm_head_model(*UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_double_lm_head_model(*UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase_      )







       @slow
       def 			_snake_case						(      self      ):
              for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     __UpperCAmelCase  :				Dict       				=		OpenAIGPTModel.from_pretrained(UpperCamelCase_      )
                     self.assertIsNotNone(UpperCamelCase_      )






@require_torch
class 		__A       (unittest.TestCase   ):






       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"      )
              model.to(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.tensor([[4_81, 47_35, 5_44]]				,     dtype=torch.long				,     device=UpperCamelCase_      )  # the president is
              __UpperCAmelCase  :				List[str]       				=		[
                  4_81,
                  47_35,
                  5_44,
                  2_46,
                  9_63,
                  8_70,
                  7_62,
                  2_39,
                  2_44,
                  4_04_77,
                  2_44,
                  2_49,
                  7_19,
                  8_81,
                  4_87,
                  5_44,
                  2_40,
                  2_44,
                  6_03,
                  4_81,
              ]  # the president is a very good man. " \n " i\'m sure he is, " said the

              __UpperCAmelCase  :				Tuple       				=		model.generate(UpperCamelCase_				,     do_sample=UpperCamelCase_      )
              self.assertListEqual(output_ids[0].tolist()				,     UpperCamelCase_      )







'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_a   :	Optional[Any] 							=   logging.get_logger(__name__)


_a   :	int 							=   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
_a   :	Tuple 							=   {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

_a   :	List[Any] 							=   {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}

@lru_cache()
def _lowercase	(      )					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		(
           list(range(ord("!"						)							,    ord("~"						) + 1						)						) + list(range(ord("¡"						)							,    ord("¬"						) + 1						)						) + list(range(ord("®"						)							,    ord("ÿ"						) + 1						)						)
       )
       __UpperCAmelCase  :				Optional[Any]       				=		bs[:]
       __UpperCAmelCase  :				Optional[int]       				=		0
       for b in range(2**8						):
              if b not in bs:
                     bs.append(lowerCamelCase__						)
                     cs.append(2**8 + n						)
                     n += 1
       __UpperCAmelCase  :				Dict       				=		[chr(lowerCamelCase__						) for n in cs]
       return dict(zip(lowerCamelCase__							,    lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		set()
       __UpperCAmelCase  :				Union[str, Any]       				=		word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              __UpperCAmelCase  :				Optional[Any]       				=		char
       return pairs






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					VOCAB_FILES_NAMES
       snake_case       :List[Any]				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Any				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[int]				    =					["input_ids", "attention_mask"]






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_="replace"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="<unk>"				,     UpperCamelCase_="<pad>"				,     UpperCamelCase_="<mask>"				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				str       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else bos_token
              __UpperCAmelCase  :				List[str]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else eos_token
              __UpperCAmelCase  :				Optional[int]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else sep_token
              __UpperCAmelCase  :				int       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else cls_token
              __UpperCAmelCase  :				Optional[int]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else unk_token
              __UpperCAmelCase  :				Dict       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else pad_token

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

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

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

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              __UpperCAmelCase  :				Dict       				=		re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"      )






       @property
       def 			_snake_case						(      self      ):
              return len(self.encoder      )






       def 			_snake_case						(      self      ):
              return dict(self.encoder				,     **self.added_tokens_encoder      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if token in self.cache:
                     return self.cache[token]
              __UpperCAmelCase  :				List[str]       				=		tuple(UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		get_pairs(UpperCamelCase_      )

              if not pairs:
                     return token

              while True:
                     __UpperCAmelCase  :				str       				=		min(UpperCamelCase_				,     key=lambda UpperCamelCase_      : self.bpe_ranks.get(UpperCamelCase_				,     float("inf"      )      )      )
                     if bigram not in self.bpe_ranks:
                            break
                     __UpperCAmelCase				, __UpperCAmelCase  :				List[Any]       				=		bigram
                     __UpperCAmelCase  :				Any       				=		[]
                     __UpperCAmelCase  :				List[str]       				=		0
                     while i < len(UpperCamelCase_      ):
                            try:
                                   __UpperCAmelCase  :				Union[str, Any]       				=		word.index(UpperCamelCase_				,     UpperCamelCase_      )
                            except ValueError:
                                   new_word.extend(word[i:]      )
                                   break
                            else:
                                   new_word.extend(word[i:j]      )
                                   __UpperCAmelCase  :				str       				=		j

                            if word[i] == first and i < len(UpperCamelCase_      ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second      )
                                   i += 2
                            else:
                                   new_word.append(word[i]      )
                                   i += 1
                     __UpperCAmelCase  :				Dict       				=		tuple(UpperCamelCase_      )
                     __UpperCAmelCase  :				str       				=		new_word
                     if len(UpperCamelCase_      ) == 1:
                            break
                     else:
                            __UpperCAmelCase  :				int       				=		get_pairs(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		" ".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		word
              return word






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[Any]       				=		[]
              for token in re.findall(self.pat				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				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(UpperCamelCase_      ).split(" "      )      )
              return bpe_tokens






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.encoder.get(UpperCamelCase_				,     self.encoder.get(self.unk_token      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.decoder.get(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		"".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"				,     errors=self.errors      )
              return text






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(UpperCamelCase_      ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
                     return
              __UpperCAmelCase  :				Any       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
              __UpperCAmelCase  :				Optional[int]       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

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

              __UpperCAmelCase  :				str       				=		0
              with open(UpperCamelCase_				,     "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 UpperCamelCase_      : 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!"      )
                                   __UpperCAmelCase  :				str       				=		token_index
                            writer.write(" ".join(UpperCamelCase_      ) + "\n"      )
                            index += 1

              return vocab_file, merge_file






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __UpperCAmelCase  :				List[Any]       				=		[self.cls_token_id]
              __UpperCAmelCase  :				Tuple       				=		[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):
              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=UpperCamelCase_				,     token_ids_a=UpperCamelCase_				,     already_has_special_tokens=UpperCamelCase_      )

              if token_ids_a is None:
                     return [1] + ([0] * len(UpperCamelCase_      )) + [1]
              return [1] + ([0] * len(UpperCamelCase_      )) + [1, 1] + ([0] * len(UpperCamelCase_      )) + [1]






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				int       				=		[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]







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=False				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		kwargs.pop("add_prefix_space"				,     self.add_prefix_space      )
              if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_      ) > 0 and not text[0].isspace()):
                     __UpperCAmelCase  :				Tuple       				=		" " + text
              return (text, kwargs)






