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      __lowercase					:

					def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]							,							)							->		Union[str, Any]:


								'''simple docstring'''

								lowercase							=		parent
								lowercase							=		13
								lowercase							=		7
								lowercase							=		30
								lowercase							=		self.seq_length + self.mem_len
								lowercase							=		15
								lowercase							=		True
								lowercase							=		True
								lowercase							=		99
								lowercase							=		[10, 50, 80]
								lowercase							=		32
								lowercase							=		32
								lowercase							=		4
								lowercase							=		8
								lowercase							=		1_28
								lowercase							=		2
								lowercase							=		2
								lowercase							=		None
								lowercase							=		1
								lowercase							=		0
								lowercase							=		3
								lowercase							=		self.vocab_size - 1
								lowercase							=		0.01

					def        __a		(	self				:      Tuple   )							->		List[Any]:


								'''simple docstring'''

								lowercase							=		ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size   )
								lowercase							=		ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size   )

								lowercase							=		None
								if self.use_labels:
											lowercase							=		ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size   )

								lowercase							=		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        __a		(	self				:      Optional[Any]   )							->		Optional[int]:


								'''simple docstring'''

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

					def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


								'''simple docstring'''

								lowercase							=		TFTransfoXLModel(__lowerCamelCase   )

								lowercase		,lowercase							=		model(__lowerCamelCase   ).to_tuple()

								lowercase							=		{'''input_ids''': input_ids_a, '''mems''': mems_a}

								lowercase		,lowercase							=		model(__lowerCamelCase   ).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        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


								'''simple docstring'''

								lowercase							=		TFTransfoXLLMHeadModel(__lowerCamelCase   )

								lowercase		,lowercase							=		model(__lowerCamelCase   ).to_tuple()

								lowercase							=		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								lowercase		,lowercase							=		model(__lowerCamelCase   ).to_tuple()

								lowercase		,lowercase							=		model([input_ids_a, mems_a]   ).to_tuple()

								lowercase							=		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

								lowercase		,lowercase							=		model(__lowerCamelCase   ).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        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


								'''simple docstring'''

								lowercase							=		TFTransfoXLForSequenceClassification(__lowerCamelCase   )
								lowercase							=		model(__lowerCamelCase   )
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)   )



					def        __a		(	self				:      Optional[Any]   )							->		Dict:


								'''simple docstring'''

								lowercase							=		self.prepare_config_and_inputs()
								((lowercase)		,(lowercase)		,(lowercase)		,(lowercase))							=		config_and_inputs
								lowercase							=		{'''input_ids''': input_ids_a}
								return config, inputs_dict






@require_tf
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
					lowercase					 =     (
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					lowercase					 =     () if is_tf_available() else ()
					lowercase					 =     (
					    {
					        '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
					lowercase					 =     False
					lowercase					 =     False
					lowercase					 =     False
					lowercase					 =     False

					def        __a		(	self				:      int							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int   )							->		int:


								'''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        __a		(	self				:      str   )							->		List[str]:


								'''simple docstring'''

								lowercase							=		TFTransfoXLModelTester(self   )
								lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							d_embed=37   )

					def        __a		(	self				:      Any   )							->		List[str]:


								'''simple docstring'''

								self.config_tester.run_common_tests()

					def        __a		(	self				:      List[Any]   )							->		Dict:


								'''simple docstring'''

								self.model_tester.set_seed()
								lowercase							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*__lowerCamelCase   )

					def        __a		(	self				:      Dict   )							->		str:


								'''simple docstring'''

								self.model_tester.set_seed()
								lowercase							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*__lowerCamelCase   )

					def        __a		(	self				:      List[Any]   )							->		Any:


								'''simple docstring'''

								lowercase							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__lowerCamelCase   )

					def        __a		(	self				:      Tuple   )							->		Any:


								'''simple docstring'''

								lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
								lowercase							=		[TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
											lowercase							=		model_class(__lowerCamelCase   )
											assert isinstance(model.get_input_embeddings()							,							tf.keras.layers.Layer   )
											if model_class in list_other_models_with_output_ebd:
														lowercase							=		model.get_output_embeddings()
														assert isinstance(__lowerCamelCase							,							tf.keras.layers.Layer   )
														lowercase							=		model.get_bias()
														assert name is None
											else:
														lowercase							=		model.get_output_embeddings()
														assert x is None
														lowercase							=		model.get_bias()
														assert name is None

					def        __a		(	self				:      List[str]   )							->		str:


								'''simple docstring'''

								pass

					@slow
					def        __a		(	self				:      Union[str, Any]   )							->		Tuple:


								'''simple docstring'''

								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase							=		TFTransfoXLModel.from_pretrained(__lowerCamelCase   )
											self.assertIsNotNone(__lowerCamelCase   )



					@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''   )
					def        __a		(	self				:      Optional[int]   )							->		Union[str, Any]:


								'''simple docstring'''

								pass






@require_tf
class      __lowercase					(    unittest.TestCase    ):

					@unittest.skip('''Skip test until #12651 is resolved.'''   )
					@slow
					def        __a		(	self				:      int   )							->		List[str]:


								'''simple docstring'''

								lowercase							=		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''   )
								# fmt: off
								lowercase							=		tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]							,							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
								lowercase							=		[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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>

								lowercase							=		model.generate(__lowerCamelCase							,							max_length=2_00							,							do_sample=__lowerCamelCase   )
								self.assertListEqual(output_ids[0].numpy().tolist()							,							__lowerCamelCase   )






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_      =      logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
A_      =      {
    "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_      =      {
    "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        __UpperCAmelCase    (       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		(
			    list(range(ord('''!'''       ),		ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       ),		ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       ),		ord('''ÿ'''       ) + 1       )       )
			)
			lowercase							=		bs[:]
			lowercase							=		0
			for b in range(2**8       ):
						if b not in bs:
									bs.append(UpperCAmelCase       )
									cs.append(2**8 + n       )
									n += 1
			lowercase							=		[chr(UpperCAmelCase       ) for n in cs]
			return dict(zip(UpperCAmelCase,		UpperCAmelCase       )       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase							=		set()
			lowercase							=		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)       )
						lowercase							=		char
			return pairs






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     ['input_ids', 'attention_mask']

						def __init__(	self				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]="replace"							,							__lowerCamelCase				:      Optional[Any]="<s>"							,							__lowerCamelCase				:      int="</s>"							,							__lowerCamelCase				:      int="</s>"							,							__lowerCamelCase				:      Optional[Any]="<s>"							,							__lowerCamelCase				:      Optional[int]="<unk>"							,							__lowerCamelCase				:      List[str]="<pad>"							,							__lowerCamelCase				:      Union[str, Any]="<mask>"							,							__lowerCamelCase				:      str=False							,							**__lowerCamelCase				:      List[str]							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else bos_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else eos_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else sep_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else cls_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else unk_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else mask_token

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

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

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

						@property
						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									return len(self.encoder   )

						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Any   )							->		List[str]:


									'''simple docstring'''

									if token in self.cache:
												return self.cache[token]
									lowercase							=		tuple(__lowerCamelCase   )
									lowercase							=		get_pairs(__lowerCamelCase   )

									if not pairs:
												return token

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

															if word[i] == first and i < len(__lowerCamelCase   ) - 1 and word[i + 1] == second:
																		new_word.append(first + second   )
																		i += 2
															else:
																		new_word.append(word[i]   )
																		i += 1
												lowercase							=		tuple(__lowerCamelCase   )
												lowercase							=		new_word
												if len(__lowerCamelCase   ) == 1:
															break
												else:
															lowercase							=		get_pairs(__lowerCamelCase   )
									lowercase							=		''' '''.join(__lowerCamelCase   )
									lowercase							=		word
									return word

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									for token in re.findall(self.pat							,							__lowerCamelCase   ):
												lowercase							=		''''''.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(__lowerCamelCase   ).split(''' '''   )   )
									return bpe_tokens

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Any:


									'''simple docstring'''

									return self.decoder.get(__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[Any]   )							->		Any:


									'''simple docstring'''

									lowercase							=		''''''.join(__lowerCamelCase   )
									lowercase							=		bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8'''							,							errors=self.errors   )
									return text

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Tuple[str]:


									'''simple docstring'''

									if not os.path.isdir(__lowerCamelCase   ):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
												return
									lowercase							=		os.path.join(
									    __lowerCamelCase							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
									lowercase							=		os.path.join(
									    __lowerCamelCase							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

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

									lowercase							=		0
									with open(__lowerCamelCase							,							'''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 __lowerCamelCase   : 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							=		token_index
															writer.write(''' '''.join(__lowerCamelCase   ) + '''\n'''   )
															index += 1

									return vocab_file, merge_file

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase							=		[self.cls_token_id]
									lowercase							=		[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None							,							__lowerCamelCase				:      bool = False   )							->		List[int]:


									'''simple docstring'''

									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowerCamelCase							,							token_ids_a=__lowerCamelCase							,							already_has_special_tokens=__lowerCamelCase   )

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

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									lowercase							=		[self.sep_token_id]
									lowercase							=		[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        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[Any]=False							,							**__lowerCamelCase				:      List[str]   )							->		int:


									'''simple docstring'''

									lowercase							=		kwargs.pop('''add_prefix_space'''							,							self.add_prefix_space   )
									if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase   ) > 0 and not text[0].isspace()):
												lowercase							=		''' ''' + text
									return (text, kwargs)





