
"""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        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=7							,						__a=True							,						__a=True							,						__a=True							,						__a=True							,						__a=99							,						__a=32							,						__a=2							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_12							,						__a=16							,						__a=2							,						__a=0.02							,						__a=3							,						__a=4							,						__a=None							,						)			->			List[Any]:






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



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






							'''simple docstring'''
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size)

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

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

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

							_UpperCamelCase									=    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=__a							,						)

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



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






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerModel(config=__a)
							_UpperCamelCase									=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

							_UpperCamelCase									=    [input_ids, input_mask]
							_UpperCamelCase									=    model(__a)

							_UpperCamelCase									=    model(__a)

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



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






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



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			int:






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



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    TFRoFormerForSequenceClassification(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Optional[int]:






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



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






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



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerForQuestionAnswering(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							_UpperCamelCase									=    model(__a)
							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 		UpperCAmelCase				(   self)			->			Dict:






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






@require_tf
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        TFRoFormerModel,
		        TFRoFormerForCausalLM,
		        TFRoFormerForMaskedLM,
		        TFRoFormerForQuestionAnswering,
		        TFRoFormerForSequenceClassification,
		        TFRoFormerForTokenClassification,
		        TFRoFormerForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__         = (
		    {
		        '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 {}
		)

		lowercase__         = False
		lowercase__         = False



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






							'''simple docstring'''
							if pipeline_test_casse_name == "TextGenerationPipelineTests":
												return True

							return False



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__a)




		@slow
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''')
							self.assertIsNotNone(__a)






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):



		@slow
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''')
							_UpperCamelCase									=    tf.constant([[0, 1, 2, 3, 4, 5]])
							_UpperCamelCase									=    model(__a)[0]

							# TODO Replace vocab size
							_UpperCamelCase									=    5_00_00

							_UpperCamelCase									=    [1, 6, vocab_size]
							self.assertEqual(output.shape							,						__a)

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

							# TODO Replace values below with what was printed above.
							_UpperCamelCase									=    tf.constant(
							    [
							        [
							            [-0.1205_3341, -1.026_4901, 0.2922_1946],
							            [-1.513_3783, 0.19_7433, 0.1519_0607],
							            [-5.013_5403, -3.90_0256, -0.8403_8764],
							        ]
							    ])
							tf.debugging.assert_near(output[:, :3, :3]							,						__a							,						atol=1e-4)






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):
		lowercase__         = 1E-4



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






							'''simple docstring'''
							_UpperCamelCase									=    tf.constant([[4, 10]])
							_UpperCamelCase									=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,						embedding_dim=6)

							_UpperCamelCase									=    emba(input_ids.shape)
							_UpperCamelCase									=    tf.constant(
							    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]])

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




		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    tf.constant(
							    [
							        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
							        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
							        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
							    ])
							_UpperCamelCase									=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12							,						embedding_dim=5_12)
							emba([2, 16, 5_12])
							_UpperCamelCase									=    emba.weight[:3, :5]

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






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):
		lowercase__         = 1E-4



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






							'''simple docstring'''
							# 2,12,16,64
							_UpperCamelCase									=    tf.reshape(tf.range(2 * 12 * 16 * 64							,						dtype=tf.floataa)							,						shape=(2, 12, 16, 64)) / 1_00

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

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

							_UpperCamelCase					,    _UpperCamelCase									=    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
							    __a							,						__a							,						__a)

							_UpperCamelCase									=    tf.constant(
							    [
							        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
							        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
							        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
							        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
							        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
							        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
							    ])
							_UpperCamelCase									=    tf.constant(
							    [
							        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
							        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
							        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
							        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
							        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
							        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
							    ])

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











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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)


UpperCAmelCase_		 =					{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

UpperCAmelCase_		 =					"""</w>"""
UpperCAmelCase_		 =					"""@@ """

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[str]:
   _A      =       set()
   _A      =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      _A      =       char
   return pairs


# Speech2Text2 has no max input length
UpperCAmelCase_		 =					{"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Dict =  VOCAB_FILES_NAMES
 a__					: str =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[Any] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: List[Any] =  ["input_ids", "attention_mask"]

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[str]="<s>"			,						__lowerCAmelCase					:					Tuple="<pad>"			,						__lowerCAmelCase					:					Optional[Any]="</s>"			,						__lowerCAmelCase					:					Dict="<unk>"			,						__lowerCAmelCase					:					Union[str, Any]=False			,						__lowerCAmelCase					:					Optional[int]=None			,						**__lowerCAmelCase					:					str			,						)   ->	Dict:
    super().__init__(
        unk_token=__lowerCAmelCase			,						bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						do_lower_case=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

    _A      =       do_lower_case

    with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as vocab_handle:
       _A      =       json.load(__lowerCAmelCase      )
    _A      =       {v: k for k, v in self.encoder.items()}

    if merges_file is None:
       logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''      )

       _A      =       None
       _A      =       None
    else:
       with open(__lowerCAmelCase			,						encoding='''utf-8'''      ) as merges_handle:
          _A      =       merges_handle.read().split('''\n'''      )[:-1]

       _A      =       [tuple(merge.split()[:2]      ) for merge in merges]
       _A      =       dict(zip(__lowerCAmelCase			,						range(len(__lowerCAmelCase      )      )      )      )
       _A      =       {}

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	int:
    return len(self.decoder      )

 def     snake_case_      (    self					:					Dict      )   ->	Dict:
    return dict(self.encoder			,						**self.added_tokens_encoder      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
    if token in self.cache:
       return self.cache[token]
    _A      =       get_pairs(__lowerCAmelCase      )

    if not pairs:
       return token

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

    if word.endswith(__lowerCAmelCase      ):
       _A      =       word.replace(__lowerCAmelCase			,						''''''      )

    _A      =       word.replace(''' '''			,						__lowerCAmelCase      )
    _A      =       word
    return word

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Optional[int]:

    if self.bpe_ranks is None:
       raise ValueError(
           '''This tokenizer was instantiated without a `merges.txt` file, so'''
           ''' that it can only be used for decoding, not for encoding.'''
           '''Make sure to provide `merges.txt` file at instantiation to enable '''
           '''encoding.'''      )

    if self.do_lower_case:
       _A      =       text.lower()

    _A      =       text.split()

    _A      =       []
    for token in text:
       if token:
          split_tokens.extend(list(self.bpe(__lowerCAmelCase      ).split(''' '''      )      )      )

    return split_tokens

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str      )   ->	int:
    return self.encoder.get(__lowerCAmelCase			,						self.encoder.get(self.unk_token      )      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					int      )   ->	str:
    _A      =       self.decoder.get(__lowerCAmelCase			,						self.unk_token      )
    return result

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					List[str]      )   ->	str:
    _A      =       ''' '''.join(__lowerCAmelCase      )

    # make sure @@ tokens are concatenated
    _A      =       ''''''.join(string.split(__lowerCAmelCase      )      )

    return string

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       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'''      )

    _A      =       0
    if self.bpe_ranks is None:
       return (vocab_file,)

    with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as writer:
       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 {merges_file}: BPE merge indices are not consecutive.'''
                 ''' Please check that the tokenizer is not corrupted!'''      )
             _A      =       token_index
          writer.write(''' '''.join(__lowerCAmelCase      ) + '''\n'''      )
          index += 1

    return (vocab_file, merges_file)



