






'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import LEDConfig, 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 TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class        __SCREAMING_SNAKE_CASE :
      snake_case_												=							LEDConfig
      snake_case_												=							{}
      snake_case_												=							"""gelu"""




      def __init__( self		:							Tuple   , __lowercase		:							Union[str, Any]   , __lowercase		:							List[str]=13   , __lowercase		:							Union[str, Any]=7   , __lowercase		:							Optional[int]=True   , __lowercase		:							Dict=False   , __lowercase		:							Dict=99   , __lowercase		:							List[str]=32   , __lowercase		:							List[Any]=2   , __lowercase		:							Any=4   , __lowercase		:							Any=37   , __lowercase		:							int=0.1   , __lowercase		:							Tuple=0.1   , __lowercase		:							Optional[int]=20   , __lowercase		:							str=2   , __lowercase		:							Union[str, Any]=1   , __lowercase		:							Dict=0   , __lowercase		:							Optional[int]=4   , )		->  int:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=parent
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=batch_size
            SCREAMING_SNAKE_CASE__       :			int													=seq_length
            SCREAMING_SNAKE_CASE__       :			List[str]													=is_training
            SCREAMING_SNAKE_CASE__       :			List[str]													=use_labels
            SCREAMING_SNAKE_CASE__       :			Tuple													=vocab_size
            SCREAMING_SNAKE_CASE__       :			Any													=hidden_size
            SCREAMING_SNAKE_CASE__       :			List[Any]													=num_hidden_layers
            SCREAMING_SNAKE_CASE__       :			Any													=num_attention_heads
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=intermediate_size

            SCREAMING_SNAKE_CASE__       :			Any													=hidden_dropout_prob
            SCREAMING_SNAKE_CASE__       :			List[Any]													=attention_probs_dropout_prob
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
            SCREAMING_SNAKE_CASE__       :			Any													=eos_token_id
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=pad_token_id
            SCREAMING_SNAKE_CASE__       :			Any													=bos_token_id
            SCREAMING_SNAKE_CASE__       :			str													=attention_window

            # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
            # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
            # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
            # because its local attention only attends to `self.attention_window` and one before and one after
            SCREAMING_SNAKE_CASE__       :			Any													=self.attention_window + 2

            # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
            # the `test_attention_outputs` and `test_hidden_states_output` tests
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=(
                self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
            )




      def        __magic_name__		( self		:							Any )		->  Union[str, Any]:
            SCREAMING_SNAKE_CASE__       :			str													=ids_tensor([self.batch_size, self.seq_length - 1]   , self.vocab_size )
            SCREAMING_SNAKE_CASE__       :			Tuple													=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )   , 1 )
            SCREAMING_SNAKE_CASE__       :			Dict													=tf.concat([input_ids, eos_tensor]   , axis=1 )

            SCREAMING_SNAKE_CASE__       :			Any													=ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size )

            SCREAMING_SNAKE_CASE__       :			Any													=self.config_cls(
                vocab_size=self.vocab_size   , d_model=self.hidden_size   , encoder_layers=self.num_hidden_layers   , decoder_layers=self.num_hidden_layers   , encoder_attention_heads=self.num_attention_heads   , decoder_attention_heads=self.num_attention_heads   , encoder_ffn_dim=self.intermediate_size   , decoder_ffn_dim=self.intermediate_size   , dropout=self.hidden_dropout_prob   , attention_dropout=self.attention_probs_dropout_prob   , max_position_embeddings=self.max_position_embeddings   , eos_token_ids=[2]   , bos_token_id=self.bos_token_id   , pad_token_id=self.pad_token_id   , decoder_start_token_id=self.pad_token_id   , attention_window=self.attention_window   , **self.config_updates   , )
            SCREAMING_SNAKE_CASE__       :			Tuple													=prepare_led_inputs_dict(__a   , __a   , __a )
            SCREAMING_SNAKE_CASE__       :			List[str]													=tf.concat(
                [tf.zeros_like(__a )[:, :-1], tf.ones_like(__a )[:, -1:]]   , axis=-1   , )
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=global_attention_mask
            return config, inputs_dict




      def        __magic_name__		( self		:							List[str]   , __lowercase		:							str   , __lowercase		:							Optional[int] )		->  Optional[int]:
            SCREAMING_SNAKE_CASE__       :			List[str]													=TFLEDModel(config=__a ).get_decoder()
            SCREAMING_SNAKE_CASE__       :			int													=inputs_dict['''input_ids''']

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=input_ids[:1, :]
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=inputs_dict['''attention_mask'''][:1, :]
            SCREAMING_SNAKE_CASE__       :			str													=1

            # first forward pass
            SCREAMING_SNAKE_CASE__       :			Tuple													=model(__a   , attention_mask=__a   , use_cache=__a )

            SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            SCREAMING_SNAKE_CASE__       :			int													=ids_tensor((self.batch_size, 3)   , config.vocab_size )
            SCREAMING_SNAKE_CASE__       :			Dict													=tf.cast(ids_tensor((self.batch_size, 3)   , 2 )   , tf.inta )

            # append to next input_ids and
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tf.concat([input_ids, next_tokens]   , axis=-1 )
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tf.concat([attention_mask, next_attn_mask]   , axis=-1 )

            SCREAMING_SNAKE_CASE__       :			str													=model(__a   , attention_mask=__a )[0]
            SCREAMING_SNAKE_CASE__       :			List[str]													=model(__a   , attention_mask=__a   , past_key_values=__a )[0]

            self.parent.assertEqual(next_tokens.shape[1]   , output_from_past.shape[1] )

            # select random slice
            SCREAMING_SNAKE_CASE__       :			Dict													=int(ids_tensor((1,)   , output_from_past.shape[-1] ) )
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output_from_no_past[:, -3:, random_slice_idx]
            SCREAMING_SNAKE_CASE__       :			Tuple													=output_from_past[:, :, random_slice_idx]

            # test that outputs are equal for slice
            tf.debugging.assert_near(__a   , __a   , rtol=1e-3 )





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Union[str, Any]=None,		UpperCamelCase__			:  Dict=None,		UpperCamelCase__			:  List[str]=None,		UpperCamelCase__			:  Tuple=None,		):




      '''simple docstring'''






      if attention_mask is None:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tf.cast(tf.math.not_equal(snake_case_,		config.pad_token_id							),		tf.inta							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__       :			List[str]													=tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape,		dtype=tf.inta							),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],		config.pad_token_id							),		tf.inta							),
                ],		axis=-1,		)
      if head_mask is None:
            SCREAMING_SNAKE_CASE__       :			List[str]													=tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
      }






@require_tf
class        __SCREAMING_SNAKE_CASE (     UpperCamelCase__		,			UpperCamelCase__		,			unittest.TestCase			):
      snake_case_												=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      snake_case_												=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
      snake_case_												=							(
          {
              """conversational""": TFLEDForConditionalGeneration,
              """feature-extraction""": TFLEDModel,
              """summarization""": TFLEDForConditionalGeneration,
              """text2text-generation""": TFLEDForConditionalGeneration,
              """translation""": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      snake_case_												=							True
      snake_case_												=							False
      snake_case_												=							False
      snake_case_												=							False




      def        __magic_name__		( self		:							Union[str, Any] )		->  int:
            SCREAMING_SNAKE_CASE__       :			str													=TFLEDModelTester(self )
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=ConfigTester(self   , config_class=__a )




      def        __magic_name__		( self		:							str )		->  Union[str, Any]:
            self.config_tester.run_common_tests()




      def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
            SCREAMING_SNAKE_CASE__       :			str													=self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*__a )




      def        __magic_name__		( self		:							Optional[int] )		->  Any:
            SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			str													=self.model_tester.prepare_config_and_inputs_for_common()
            SCREAMING_SNAKE_CASE__       :			Any													=tf.zeros_like(inputs_dict['''attention_mask'''] )
            SCREAMING_SNAKE_CASE__       :			List[str]													=2
            SCREAMING_SNAKE_CASE__       :			Any													=tf.where(
                tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices   , 1   , inputs_dict['''global_attention_mask''']   , )

            SCREAMING_SNAKE_CASE__       :			str													=True
            SCREAMING_SNAKE_CASE__       :			int													=self.model_tester.seq_length
            SCREAMING_SNAKE_CASE__       :			Any													=self.model_tester.encoder_seq_length

            def check_decoder_attentions_output(__lowercase		:							Optional[int] ):
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=outputs.decoder_attentions
                  self.assertEqual(len(__a )   , self.model_tester.num_hidden_layers )
                  self.assertListEqual(
                      list(decoder_attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, seq_length, seq_length]   , )

            def check_encoder_attentions_output(__lowercase		:							Dict ):
                  SCREAMING_SNAKE_CASE__       :			int													=[t.numpy() for t in outputs.encoder_attentions]
                  SCREAMING_SNAKE_CASE__       :			Tuple													=[t.numpy() for t in outputs.encoder_global_attentions]
                  self.assertEqual(len(__a )   , self.model_tester.num_hidden_layers )
                  self.assertEqual(len(__a )   , self.model_tester.num_hidden_layers )
                  self.assertListEqual(
                      list(attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, seq_length, seq_length]   , )
                  self.assertListEqual(
                      list(global_attentions[0].shape[-3:] )   , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]   , )

            for model_class in self.all_model_classes:
                  SCREAMING_SNAKE_CASE__       :			int													=True
                  SCREAMING_SNAKE_CASE__       :			str													=False
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=False
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_class(__a )
                  SCREAMING_SNAKE_CASE__       :			List[str]													=model(self._prepare_for_class(__a   , __a ) )
                  SCREAMING_SNAKE_CASE__       :			Tuple													=len(__a )
                  self.assertEqual(config.output_hidden_states   , __a )
                  check_encoder_attentions_output(__a )

                  if self.is_encoder_decoder:
                        SCREAMING_SNAKE_CASE__       :			Tuple													=model_class(__a )
                        SCREAMING_SNAKE_CASE__       :			Dict													=model(self._prepare_for_class(__a   , __a ) )
                        self.assertEqual(config.output_hidden_states   , __a )
                        check_decoder_attentions_output(__a )

                  # Check that output attentions can also be changed via the config
                  del inputs_dict["output_attentions"]
                  SCREAMING_SNAKE_CASE__       :			str													=True
                  SCREAMING_SNAKE_CASE__       :			Dict													=model_class(__a )
                  SCREAMING_SNAKE_CASE__       :			str													=model(self._prepare_for_class(__a   , __a ) )
                  self.assertEqual(config.output_hidden_states   , __a )
                  check_encoder_attentions_output(__a )

                  # Check attention is always last and order is fine
                  SCREAMING_SNAKE_CASE__       :			List[str]													=True
                  SCREAMING_SNAKE_CASE__       :			int													=True
                  SCREAMING_SNAKE_CASE__       :			List[str]													=model_class(__a )
                  SCREAMING_SNAKE_CASE__       :			Optional[int]													=model(self._prepare_for_class(__a   , __a ) )

                  self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)   , len(__a ) )
                  self.assertEqual(model.config.output_hidden_states   , __a )
                  check_encoder_attentions_output(__a )




      @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
      def        __magic_name__		( self		:							str )		->  Tuple:
            pass




      def        __magic_name__		( self		:							Union[str, Any] )		->  int:
            # TODO: Head-masking not yet implement
            pass





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      return tf.constant(snake_case_,		dtype=tf.intaa							)


a_					=	1E-4







@slow
@require_tf
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




      def        __magic_name__		( self		:							str )		->  Dict:
            SCREAMING_SNAKE_CASE__       :			Any													=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led

            # change to intended input here
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
            SCREAMING_SNAKE_CASE__       :			List[str]													=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
            SCREAMING_SNAKE_CASE__       :			List[str]													=prepare_led_inputs_dict(model.config   , __a   , __a )
            SCREAMING_SNAKE_CASE__       :			Dict													=model(**__a )[0]
            SCREAMING_SNAKE_CASE__       :			Any													=(1, 10_24, 7_68)
            self.assertEqual(output.shape   , __a )
            # change to expected output here
            SCREAMING_SNAKE_CASE__       :			int													=tf.convert_to_tensor(
                [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]   , )
            tf.debugging.assert_near(output[:, :3, :3]   , __a   , atol=1e-3 )




      def        __magic_name__		( self		:							Any )		->  Any:
            SCREAMING_SNAKE_CASE__       :			Any													=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )

            # change to intended input here
            SCREAMING_SNAKE_CASE__       :			str													=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
            SCREAMING_SNAKE_CASE__       :			Dict													=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
            SCREAMING_SNAKE_CASE__       :			List[Any]													=prepare_led_inputs_dict(model.config   , __a   , __a )
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model(**__a )[0]
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=(1, 10_24, model.config.vocab_size)
            self.assertEqual(output.shape   , __a )
            # change to expected output here
            SCREAMING_SNAKE_CASE__       :			Tuple													=tf.convert_to_tensor(
                [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]   , )
            tf.debugging.assert_near(output[:, :3, :3]   , __a   , atol=1e-3   , rtol=1e-3 )







'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


a_					=	logging.get_logger(__name__)


a_					=	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_					=	{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

a_					=	{'facebook/blenderbot_small-90M': 5_1_2}





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__       :			Any													=set(UpperCamelCase__							)
      return pairs







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							VOCAB_FILES_NAMES
 snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_												=							["""input_ids""", """attention_mask"""]




 def __init__( self		:							Tuple   , __lowercase		:							int   , __lowercase		:							Optional[int]   , __lowercase		:							List[str]="__start__"   , __lowercase		:							Union[str, Any]="__end__"   , __lowercase		:							str="__unk__"   , __lowercase		:							Union[str, Any]="__null__"   , **__lowercase		:							List[str]   , )		->  Optional[int]:
       super().__init__(unk_token=__lowercase   , bos_token=__lowercase   , eos_token=__lowercase   , pad_token=__lowercase   , **__lowercase )

       with open(__lowercase   , encoding='''utf-8''' ) as vocab_handle:
             SCREAMING_SNAKE_CASE__       :			Any													=json.load(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													={v: k for k, v in self.encoder.items()}
       with open(__lowercase   , encoding='''utf-8''' ) as merges_handle:
             SCREAMING_SNAKE_CASE__       :			int													=merges_handle.read().split('''\n''' )[1:-1]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=[tuple(merge.split() ) for merge in merges]
       SCREAMING_SNAKE_CASE__       :			int													=dict(zip(__lowercase   , range(len(__lowercase ) ) ) )
       SCREAMING_SNAKE_CASE__       :			Dict													={}




 @property
 def        __magic_name__		( self		:							Any )		->  int:
       return len(self.encoder )




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




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							str )		->  str:
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=re.sub('''([.,!?()])'''   , r''' \1'''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=re.sub('''(\')'''   , r''' \1 '''   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=re.sub(r'''\s{2,}'''   , ''' '''   , __lowercase )
       if "\n" in token:
             SCREAMING_SNAKE_CASE__       :			List[str]													=token.replace('''\n'''   , ''' __newln__''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=token.split(''' ''' )
       SCREAMING_SNAKE_CASE__       :			Dict													=[]
       for token in tokens:
             if not len(__lowercase ):
                   continue

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=token.lower()
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tuple(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_pairs(__lowercase )

             if not pairs:
                   words.append(__lowercase )
                   continue

             while True:
                   SCREAMING_SNAKE_CASE__       :			int													=min(__lowercase   , key=lambda __lowercase : self.bpe_ranks.get(__lowercase   , float('''inf''' ) ) )
                   if bigram not in self.bpe_ranks:
                         break
                   SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=bigram
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
                   SCREAMING_SNAKE_CASE__       :			Tuple													=0

                   while i < len(__lowercase ):
                         try:
                               SCREAMING_SNAKE_CASE__       :			Optional[Any]													=word.index(__lowercase   , __lowercase )
                               new_word.extend(word[i:j] )
                               SCREAMING_SNAKE_CASE__       :			Optional[int]													=j
                         except ValueError:
                               new_word.extend(word[i:] )
                               break

                         if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
                               new_word.append(first + second )
                               i += 2
                         else:
                               new_word.append(word[i] )
                               i += 1
                   SCREAMING_SNAKE_CASE__       :			int													=tuple(__lowercase )
                   SCREAMING_SNAKE_CASE__       :			int													=new_word
                   if len(__lowercase ) == 1:
                         break
                   else:
                         SCREAMING_SNAKE_CASE__       :			Any													=get_pairs(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													='''@@ '''.join(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=word[:-4]

             SCREAMING_SNAKE_CASE__       :			str													=word
             words.append(__lowercase )
       return " ".join(__lowercase )




 def        __magic_name__		( self		:							str   , __lowercase		:							str )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=[]

       SCREAMING_SNAKE_CASE__       :			List[Any]													=re.findall(r'''\S+\n?'''   , __lowercase )

       for token in words:
             split_tokens.extend(list(self.bpe(__lowercase ).split(''' ''' ) ) )
       return split_tokens




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  int:
       SCREAMING_SNAKE_CASE__       :			int													=token.lower()
       return self.encoder.get(__lowercase   , self.encoder.get(self.unk_token ) )




 def        __magic_name__		( self		:							int   , __lowercase		:							int )		->  str:
       return self.decoder.get(__lowercase   , self.unk_token )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							List[str] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=''' '''.join(__lowercase ).replace('''@@ '''   , '''''' ).strip()
       return out_string




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							str   , __lowercase		:							Optional[str] = None )		->  Tuple[str]:
       if not os.path.isdir(__lowercase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       SCREAMING_SNAKE_CASE__       :			str													=os.path.join(
           __lowercase   , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       SCREAMING_SNAKE_CASE__       :			int													=os.path.join(
           __lowercase   , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

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

       SCREAMING_SNAKE_CASE__       :			List[str]													=0
       with open(__lowercase   , '''w'''   , encoding='''utf-8''' ) as writer:
             writer.write('''#version: 0.2\n''' )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   , key=lambda __lowercase : kv[1] ):
                   if index != token_index:
                         logger.warning(
                             F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                             ''' Please check that the tokenizer is not corrupted!''' )
                         SCREAMING_SNAKE_CASE__       :			Any													=token_index
                   writer.write(''' '''.join(__lowercase ) + '''\n''' )
                   index += 1

       return vocab_file, merge_file