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

import unittest

from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=13		,    lowerCAmelCase=7		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=99		,    lowerCAmelCase=32		,    lowerCAmelCase=2		,    lowerCAmelCase=4		,    lowerCAmelCase=37		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=16		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=3		,    lowerCAmelCase=4		,    lowerCAmelCase=None		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			13
										snake_case								=			7
										snake_case								=			True
										snake_case								=			True
										snake_case								=			True
										snake_case								=			True
										snake_case								=			99
										snake_case								=			32
										snake_case								=			2
										snake_case								=			4
										snake_case								=			37
										snake_case								=			'gelu'
										snake_case								=			0.1
										snake_case								=			0.1
										snake_case								=			5_12
										snake_case								=			16
										snake_case								=			2
										snake_case								=			0.02
										snake_case								=			3
										snake_case								=			4
										snake_case								=			None

				def 		snake_case (				self    ):






										"""simple docstring"""

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

										snake_case								=			None
										if self.use_input_mask:
																snake_case								=			random_attention_mask([self.batch_size, self.seq_length]    )

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

										snake_case								=			None
										snake_case								=			None
										snake_case								=			None
										if self.use_labels:
																snake_case								=			ids_tensor([self.batch_size]		,    self.type_sequence_label_size    )
																snake_case								=			ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels    )
																snake_case								=			ids_tensor([self.batch_size]		,    self.num_choices    )

										snake_case								=			RoFormerConfig(
										    vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    initializer_range=self.initializer_range		,    return_dict=lowerCAmelCase		,    )

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerModel(config=lowerCAmelCase    )
										snake_case								=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

										snake_case								=			[input_ids, input_mask]
										snake_case								=			model(lowerCAmelCase    )

										snake_case								=			model(lowerCAmelCase    )

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			True
										snake_case								=			TFRoFormerForCausalLM(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )['logits']
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape    )		,    [self.batch_size, self.seq_length, self.vocab_size]    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerForMaskedLM(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.num_labels
										snake_case								=			TFRoFormerForSequenceClassification(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.num_choices
										snake_case								=			TFRoFormerForMultipleChoice(config=lowerCAmelCase    )
										snake_case								=			tf.tile(tf.expand_dims(lowerCAmelCase		,    1    )		,    (1, self.num_choices, 1)    )
										snake_case								=			tf.tile(tf.expand_dims(lowerCAmelCase		,    1    )		,    (1, self.num_choices, 1)    )
										snake_case								=			tf.tile(tf.expand_dims(lowerCAmelCase		,    1    )		,    (1, self.num_choices, 1)    )
										snake_case								=			{
										    'input_ids': multiple_choice_inputs_ids,
										    'attention_mask': multiple_choice_input_mask,
										    'token_type_ids': multiple_choice_token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.num_labels
										snake_case								=			TFRoFormerForTokenClassification(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerForQuestionAnswering(config=lowerCAmelCase    )
										snake_case								=			{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}

										snake_case								=			model(lowerCAmelCase    )
										self.parent.assertEqual(result.start_logits.shape		,    (self.batch_size, self.seq_length)    )
										self.parent.assertEqual(result.end_logits.shape		,    (self.batch_size, self.seq_length)    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.prepare_config_and_inputs()
										(
										    (
										    snake_case					
										)					,(
										    snake_case					
										)					,(
										    snake_case					
										)					,(
										    snake_case					
										)					,(
										    snake_case					
										)					,(
										    snake_case					
										)					,(
										    snake_case					
										)					,					
										)								=			config_and_inputs
										snake_case								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_tf
class   lowerCAmelCase_    (			lowercase_     ,    lowercase_     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						(
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowerCAmelCase     :    List[Any]			  =						(
				    {
				        '''feature-extraction''': TFRoFormerModel,
				        '''fill-mask''': TFRoFormerForMaskedLM,
				        '''question-answering''': TFRoFormerForQuestionAnswering,
				        '''text-classification''': TFRoFormerForSequenceClassification,
				        '''text-generation''': TFRoFormerForCausalLM,
				        '''token-classification''': TFRoFormerForTokenClassification,
				        '''zero-shot''': TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowerCAmelCase     :    Tuple			  =						False
				_lowerCAmelCase     :    Union[str, Any]			  =						False

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										if pipeline_test_casse_name == "TextGenerationPipelineTests":
																return True

										return False

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerModelTester(self    )
										snake_case								=			ConfigTester(self		,    config_class=lowerCAmelCase		,    hidden_size=37    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'    )
										self.assertIsNotNone(lowerCAmelCase    )





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
										snake_case								=			tf.constant([[0, 1, 2, 3, 4, 5]]    )
										snake_case								=			model(lowerCAmelCase    )[0]

										# TODO Replace vocab size
										snake_case								=			5_00_00

										snake_case								=			[1, 6, vocab_size]
										self.assertEqual(output.shape		,    lowerCAmelCase    )

										print(output[:, :3, :3]    )

										# TODO Replace values below with what was printed above.
										snake_case								=			tf.constant(
										    [
										        [
										            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
										            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
										            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
										        ]
										    ]    )
										tf.debugging.assert_near(output[:, :3, :3]		,    lowerCAmelCase		,    atol=1E-4    )





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						1e-4

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tf.constant([[4, 10]]    )
										snake_case								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,    embedding_dim=6    )

										snake_case								=			emba(input_ids.shape    )
										snake_case								=			tf.constant(
										    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]    )

										tf.debugging.assert_near(lowerCAmelCase		,    lowerCAmelCase		,    atol=self.tolerance    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tf.constant(
										    [
										        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
										        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
										        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
										    ]    )
										snake_case								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12		,    embedding_dim=5_12    )
										emba([2, 16, 5_12]    )
										snake_case								=			emba.weight[:3, :5]

										tf.debugging.assert_near(lowerCAmelCase		,    lowerCAmelCase		,    atol=self.tolerance    )





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						1e-4

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tf.reshape(tf.range(2 * 12 * 16 * 64		,    dtype=tf.floataa    )		,    shape=(2, 12, 16, 64)    ) / 1_00

										snake_case								=			-tf.reshape(tf.range(2 * 12 * 16 * 64		,    dtype=tf.floataa    )		,    shape=(2, 12, 16, 64)    ) / 1_00

										snake_case								=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,    embedding_dim=64    )
										snake_case								=			embed_positions([2, 16, 7_68]    )[None, None, :, :]

										snake_case					,snake_case								=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			tf.constant(
										    [
										        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
										        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
										        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
										        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
										        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
										        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
										    ]    )
										snake_case								=			tf.constant(
										    [
										        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
										        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
										        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
										        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
										        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
										        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
										    ]    )

										tf.debugging.assert_near(query_layer[0, 0, :6, :8]		,    lowerCAmelCase		,    atol=self.tolerance    )
										tf.debugging.assert_near(key_layer[0, 0, :6, :8]		,    lowerCAmelCase		,    atol=self.tolerance    )



"""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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__      						=  {
    "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",
    },
}

SCREAMING_SNAKE_CASE__      						=  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}





@lru_cache()
def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				List[str]:





						"""simple docstring"""






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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						VOCAB_FILES_NAMES
				_lowerCAmelCase     :    str			  =						PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase     :    int			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase     :    str			  =						["""input_ids""", """attention_mask"""]

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase="replace"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="<unk>"		,    lowerCAmelCase="<pad>"		,    lowerCAmelCase="<mask>"		,    lowerCAmelCase=False		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else bos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else eos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else sep_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else cls_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else unk_token
										snake_case								=			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
										snake_case								=			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:
																snake_case								=			json.load(lowerCAmelCase    )
										snake_case								=			{v: k for k, v in self.encoder.items()}
										snake_case								=			errors  # how to handle errors in decoding
										snake_case								=			bytes_to_unicode()
										snake_case								=			{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase		,    encoding='utf-8'    ) as merges_handle:
																snake_case								=			merges_handle.read().split('\n'    )[1:-1]
										snake_case								=			[tuple(merge.split()    ) for merge in bpe_merges]
										snake_case								=			dict(zip(lowerCAmelCase		,    range(len(lowerCAmelCase    )    )    )    )
										snake_case								=			{}
										snake_case								=			add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case								=			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    ):






										"""simple docstring"""

										return len(self.encoder    )

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if token in self.cache:
																return self.cache[token]
										snake_case								=			tuple(lowerCAmelCase    )
										snake_case								=			get_pairs(lowerCAmelCase    )

										if not pairs:
																return token

										while True:
																snake_case								=			min(lowerCAmelCase		,    key=lambda lowerCAmelCase    : self.bpe_ranks.get(lowerCAmelCase		,    float('inf'    )    )    )
																if bigram not in self.bpe_ranks:
																						break
																snake_case					,snake_case								=			bigram
																snake_case								=			[]
																snake_case								=			0
																while i < len(lowerCAmelCase    ):
																						try:
																												snake_case								=			word.index(lowerCAmelCase		,    lowerCAmelCase    )
																						except ValueError:
																												new_word.extend(word[i:]    )
																												break
																						else:
																												new_word.extend(word[i:j]    )
																												snake_case								=			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
																snake_case								=			tuple(lowerCAmelCase    )
																snake_case								=			new_word
																if len(lowerCAmelCase    ) == 1:
																						break
																else:
																						snake_case								=			get_pairs(lowerCAmelCase    )
										snake_case								=			' '.join(lowerCAmelCase    )
										snake_case								=			word
										return word

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			[]
										for token in re.findall(self.pat		,    lowerCAmelCase    ):
																snake_case								=			''.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 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.decoder.get(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

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

										snake_case								=			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!'    )
																												snake_case								=			token_index
																						writer.write(' '.join(lowerCAmelCase    ) + '\n'    )
																						index += 1

										return vocab_file, merge_file

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = False    ):






										"""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 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			[self.sep_token_id]
										snake_case								=			[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		,    lowerCAmelCase		,    lowerCAmelCase=False		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			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()):
																snake_case								=			' ' + text
										return (text, kwargs)


