

'''simple docstring'''

import unittest
from unittest.util import safe_repr

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

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


if is_torch_available():
						import torch

						from transformers import (
						    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RwkvForCausalLM,
						    RwkvModel,
						)
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
						lowerCAmelCase_     =      False







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=14,						lowercase_=7,						lowercase_=True,						lowercase_=False,						lowercase_=True,						lowercase_=True,						lowercase_=True,						lowercase_=99,						lowercase_=32,						lowercase_=5,						lowercase_=37,						lowercase_="gelu",						lowercase_=0.1,						lowercase_=0.1,						lowercase_=512,						lowercase_=16,						lowercase_=2,						lowercase_=3,						lowercase_=4,						lowercase_=None,						)    ->							str:
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					seq_length
					snake_case							=					is_training
					snake_case							=					use_token_type_ids
					snake_case							=					use_input_mask
					snake_case							=					use_labels
					snake_case							=					use_mc_token_ids
					snake_case							=					vocab_size
					snake_case							=					hidden_size
					snake_case							=					num_hidden_layers
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					hidden_dropout_prob
					snake_case							=					attention_probs_dropout_prob
					snake_case							=					max_position_embeddings
					snake_case							=					type_vocab_size
					snake_case							=					type_sequence_label_size
					snake_case							=					num_labels
					snake_case							=					num_choices
					snake_case							=					scope
					snake_case							=					vocab_size - 1
					snake_case							=					vocab_size - 1
					snake_case							=					vocab_size - 1

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b' )

				def 					_lowerCamelCase    (       self,						lowercase_=False,						lowercase_=False,						lowercase_=False )    ->							Any:
					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
					if self.use_mc_token_ids:
						snake_case							=					ids_tensor([self.batch_size, self.num_choices],						self.seq_length )

					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							=					self.get_config(
					    gradient_checkpointing=lowercase_,						scale_attn_by_inverse_layer_idx=lowercase_,						reorder_and_upcast_attn=lowercase_,						)

					return (
					    config,
					    input_ids,
					    input_mask,
					    None,
					    token_type_ids,
					    mc_token_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)

				def 					_lowerCamelCase    (       self,						lowercase_=False,						lowercase_=False,						lowercase_=False )    ->							Optional[Any]:
					return RwkvConfig(
					    vocab_size=self.vocab_size,						hidden_size=self.hidden_size,						num_hidden_layers=self.num_hidden_layers,						intermediate_size=self.intermediate_size,						activation_function=self.hidden_act,						resid_pdrop=self.hidden_dropout_prob,						attn_pdrop=self.attention_probs_dropout_prob,						n_positions=self.max_position_embeddings,						type_vocab_size=self.type_vocab_size,						use_cache=lowercase_,						bos_token_id=self.bos_token_id,						eos_token_id=self.eos_token_id,						pad_token_id=self.pad_token_id,						gradient_checkpointing=lowercase_,						scale_attn_by_inverse_layer_idx=lowercase_,						reorder_and_upcast_attn=lowercase_,						)

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					self.get_config()
					snake_case							=					300
					return config

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					(
					    (
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			
					)							=					self.prepare_config_and_inputs()

					snake_case							=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					snake_case							=					ids_tensor([self.batch_size, self.seq_length],						vocab_size=2 )

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					    token_type_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							int:
					snake_case							=					True
					snake_case							=					RwkvModel(config=lowercase_ )
					model.to(lowercase_ )
					model.eval()

					snake_case							=					model(lowercase_ )

					self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size) )
					self.parent.assertEqual(len(result.hidden_states ),						config.num_hidden_layers + 1 )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Dict:
					snake_case							=					RwkvForCausalLM(lowercase_ )
					model.to(lowercase_ )
					model.eval()

					snake_case							=					model(lowercase_,						labels=lowercase_ )
					self.parent.assertEqual(result.loss.shape,						() )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.vocab_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							int:
					snake_case							=					RwkvModel(config=lowercase_ )
					model.to(lowercase_ )
					model.eval()

					snake_case							=					model(lowercase_ )
					snake_case							=					outputs.last_hidden_state

					snake_case							=					model(input_ids[:, :2] )
					snake_case							=					outputs.last_hidden_state

					# Using the state computed on the first inputs, we will get the same output
					snake_case							=					model(input_ids[:, 2:],						state=outputs.state )
					snake_case							=					outputs.last_hidden_state

					self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two],						dim=1 ),						lowercase_,						atol=1E-5 ) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_,						lowercase_=False )    ->							List[str]:
					snake_case							=					RwkvForCausalLM(lowercase_ )
					model.to(lowercase_ )
					if gradient_checkpointing:
						model.gradient_checkpointing_enable()

					snake_case							=					model(lowercase_,						labels=lowercase_ )
					self.parent.assertEqual(result.loss.shape,						() )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.vocab_size) )
					result.loss.backward()






				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.prepare_config_and_inputs()

					(
					    (
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			
					)							=					config_and_inputs

					snake_case							=					{'input_ids': input_ids}

					return config, inputs_dict







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0				,				reason='''See https://github.com/huggingface/transformers/pull/24204'''				)
@require_torch
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
				snake_case_							     = (
				    {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
				)
				# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False  # Rwkv does not support head masking

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					RwkvModelTester(self )
					snake_case							=					ConfigTester(
					    self,						config_class=lowercase_,						n_embd=37,						common_properties=['hidden_size', 'num_hidden_layers'] )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_=None )    ->							Union[str, Any]:
					if isinstance(lowercase_,						torch.Tensor ):
						snake_case			,			snake_case							=					member.max().item(), member.min().item()
					elif isinstance(lowercase_,						lowercase_ ) or isinstance(lowercase_,						lowercase_ ):
						snake_case			,			snake_case							=					max(lowercase_ ), min(lowercase_ )

					if not isinstance(lowercase_,						lowercase_ ):
						raise TypeError('container should be a list or tuple' )
					elif len(lowercase_ ) != 2:
						raise ValueError('container should have 2 elements' )

					snake_case			,			snake_case							=					container

					snake_case							=					(min_value >= expected_min) and (max_value <= expected_max)

					if not is_inside_interval:
						snake_case							=					'%s not found in %s' % (safe_repr(lowercase_ ), safe_repr(lowercase_ ))
						self.fail(self._formatMessage(lowercase_,						lowercase_ ) )

				def 					_lowerCamelCase    (       self )    ->							Any:
					self.config_tester.run_common_tests()

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_rwkv_model(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_causl_lm(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_state_equivalency(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						snake_case							=					model_class(config=lowercase_ )
						for name, param in model.named_parameters():
							if "time_decay" in name:
								if param.requires_grad:
									self.assertTrue(param.data.max().item() == 3.0 )
									self.assertTrue(param.data.min().item() == -5.0 )
							elif "time_first" in name:
								if param.requires_grad:
									# check if it's a ones like
									self.assertTrue(torch.allclose(param.data,						torch.ones_like(param.data ),						atol=1E-5,						rtol=1E-5 ) )
							elif any(x in name for x in ['time_mix_key', 'time_mix_receptance'] ):
								if param.requires_grad:
									self.assertInterval(
									    param.data,						[0.0, 1.0],						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',						)
							elif "time_mix_value" in name:
								if param.requires_grad:
									self.assertInterval(
									    param.data,						[0.0, 1.3],						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',						)

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							=					True

					snake_case							=					getattr(self.model_tester,						'seq_length',						lowercase_ )

					for model_class in self.all_model_classes:
						snake_case							=					True
						snake_case							=					False
						snake_case							=					True
						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.eval()

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					inputs['input_ids'].shape[0]
						with torch.no_grad():
							snake_case							=					model(**lowercase_ )
						snake_case							=					outputs.attentions
						self.assertEqual(len(lowercase_ ),						self.model_tester.num_hidden_layers )

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						snake_case							=					True
						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.eval()

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					inputs['input_ids'].shape[0]
						with torch.no_grad():
							snake_case							=					model(**lowercase_ )
						snake_case							=					outputs.attentions
						self.assertEqual(len(lowercase_ ),						self.model_tester.num_hidden_layers )

						self.assertListEqual(
						    list(attentions[0].shape[-3:] ),						[batch_size, seq_len, config.hidden_size],						)
						snake_case							=					len(lowercase_ )

						# Check attention is always last and order is fine
						snake_case							=					True
						snake_case							=					True
						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.eval()

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					inputs['input_ids'].shape[0]
						with torch.no_grad():
							snake_case							=					model(**lowercase_ )

						snake_case							=					1
						self.assertEqual(out_len + added_hidden_states,						len(lowercase_ ) )

						snake_case							=					outputs.attentions

						self.assertEqual(len(lowercase_ ),						self.model_tester.num_hidden_layers )
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:] ),						[batch_size, seq_len, config.hidden_size],						)






				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							=					RwkvModel.from_pretrained(lowercase_ )
						self.assertIsNotNone(lowercase_ )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0				,				reason='''See https://github.com/huggingface/transformers/pull/24204'''				)
@slow
class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					'RWKV/rwkv-4-169m-pile'
					snake_case							=					AutoTokenizer.from_pretrained(self.model_id )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					'Hello my name is Jasmine and I am a newbie to the'
					snake_case							=					RwkvForCausalLM.from_pretrained(self.model_id ).to(lowercase_ )

					snake_case							=					self.tokenizer('Hello my name is',						return_tensors='pt' ).input_ids.to(lowercase_ )
					snake_case							=					model.generate(lowercase_,						max_new_tokens=10 )
					snake_case							=					self.tokenizer.decode(output[0].tolist() )

					self.assertEqual(lowercase_,						lowercase_ )






				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					'Hello my name is Jasmine and I am a newbie to the'

					snake_case							=					self.tokenizer('Hello my name is',						return_tensors='pt' ).input_ids.to(lowercase_ )
					snake_case							=					RwkvForCausalLM.from_pretrained(self.model_id,						torch_dtype=torch.bfloataa ).to(lowercase_ )

					snake_case							=					model.generate(lowercase_,						max_new_tokens=10 )
					snake_case							=					self.tokenizer.decode(output[0].tolist() )

					self.assertEqual(lowercase_,						lowercase_ )









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

import regex as re

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


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__UpperCamelCase					:	Dict          =				logging.get_logger(__name__)

__UpperCamelCase					:	List[str]          =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCamelCase					:	Optional[int]          =				{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

__UpperCamelCase					:	Any          =				{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}
@lru_cache()
def   snake_case_    (			):
   UpperCAmelCase_				:   Union[str, Any]							= (
       list(range(ord('''!'''							)      ,							ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)      ,							ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)      ,							ord('''ÿ'''							) + 1							)							)
   )
   UpperCAmelCase_				:   Optional[int]							= bs[:]
   UpperCAmelCase_				:   Union[str, Any]							= 0
   for b in range(2**8							):
      if b not in bs:
         bs.append(__lowercase							)
         cs.append(2**8 + n							)
         n += 1
   UpperCAmelCase_				:   Dict							= [chr(__lowercase							) for n in cs]
   return dict(zip(__lowercase      ,							__lowercase							)							)
def   snake_case_    (			__lowercase							):
   UpperCAmelCase_				:   List[str]							= set()
   UpperCAmelCase_				:   Optional[Any]							= word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)							)
      UpperCAmelCase_				:   Union[str, Any]							= char
   return pairs





class 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    A_	:  Optional[int]								=			VOCAB_FILES_NAMES
    A_	:  Any								=			PRETRAINED_VOCAB_FILES_MAP
    A_	:  Dict								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A_	:  List[str]								=			['input_ids', 'attention_mask']





    def __init__(						self					: Optional[int]							, __snake_case					: Dict							, __snake_case					: List[Any]							, __snake_case					: List[Any]="replace"							, __snake_case					: Any="<|endoftext|>"							, __snake_case					: Union[str, Any]="<|endoftext|>"							, __snake_case					: List[str]="<|endoftext|>"							, __snake_case					: Any=None							, __snake_case					: List[str]=False							, __snake_case					: Optional[int]=False							, **__snake_case					: str							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Optional[int]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else bos_token
       UpperCAmelCase_				:   List[Any]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else eos_token
       UpperCAmelCase_				:   Optional[int]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else unk_token
       UpperCAmelCase_				:   Tuple							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else pad_token
       super().__init__(
           errors=__snake_case							, unk_token=__snake_case							, bos_token=__snake_case							, eos_token=__snake_case							, pad_token=__snake_case							, add_prefix_space=__snake_case							, add_bos_token=__snake_case							, **__snake_case							, )
       UpperCAmelCase_				:   List[str]							= add_bos_token

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

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





    @property
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return len(self.encoder						)





    def        _lowerCamelCase			(						self					: str						):





       '''simple docstring'''
       return dict(self.encoder							, **self.added_tokens_encoder						)





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Optional[Any]						):





       '''simple docstring'''
       if token in self.cache:
          return self.cache[token]
       UpperCAmelCase_				:   Any							= tuple(__snake_case						)
       UpperCAmelCase_				:   Any							= get_pairs(__snake_case						)

       if not pairs:
          return token

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

             if word[i] == first and i < len(__snake_case						) - 1 and word[i + 1] == second:
                new_word.append(first + second						)
                i += 2
             else:
                new_word.append(word[i]						)
                i += 1
          UpperCAmelCase_				:   Any							= tuple(__snake_case						)
          UpperCAmelCase_				:   Tuple							= new_word
          if len(__snake_case						) == 1:
             break
          else:
             UpperCAmelCase_				:   str							= get_pairs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= ''' '''.join(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= word
       return word





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: Any							, __snake_case					: List[str]=None						):





       '''simple docstring'''
       if self.add_bos_token:
          UpperCAmelCase_				:   List[Any]							= [self.bos_token_id]
       else:
          UpperCAmelCase_				:   Dict							= []

       UpperCAmelCase_				:   Dict							= bos_token_ids + token_ids_a

       if token_ids_a is None:
          return output

       return output + bos_token_ids + token_ids_a





    def        _lowerCamelCase			(						self					: Optional[int]							, __snake_case					: List[int]							, __snake_case					: Optional[List[int]] = None							, __snake_case					: bool = False						):





       '''simple docstring'''
       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__snake_case							, token_ids_a=__snake_case							, already_has_special_tokens=__snake_case						)

       if not self.add_bos_token:
          return super().get_special_tokens_mask(
              token_ids_a=__snake_case							, token_ids_a=__snake_case							, already_has_special_tokens=__snake_case						)

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





    def        _lowerCamelCase			(						self					: str							, __snake_case					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= []
       for token in re.findall(self.pat							, __snake_case						):
          UpperCAmelCase_				:   Any							= ''''''.join(
              self.byte_encoder[b] for b in token.encode('''utf-8'''						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case						).split(''' '''						)						)
       return bpe_tokens





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Optional[int]						):





       '''simple docstring'''
       return self.encoder.get(__snake_case							, self.encoder.get(self.unk_token						)						)





    def        _lowerCamelCase			(						self					: int							, __snake_case					: str						):





       '''simple docstring'''
       return self.decoder.get(__snake_case						)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= ''''''.join(__snake_case						)
       UpperCAmelCase_				:   str							= bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8'''							, errors=self.errors						)
       return text





    def        _lowerCamelCase			(						self					: str							, __snake_case					: str							, __snake_case					: Optional[str] = None						):





       '''simple docstring'''
       if not os.path.isdir(__snake_case						):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
          return
       UpperCAmelCase_				:   List[str]							= os.path.join(
           __snake_case							, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
       UpperCAmelCase_				:   List[Any]							= os.path.join(
           __snake_case							, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']						)

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

       UpperCAmelCase_				:   Union[str, Any]							= 0
       with open(__snake_case							, '''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 __snake_case						: kv[1]						):
             if index != token_index:
                logger.warning(
                    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                    ''' Please check that the tokenizer is not corrupted!'''						)
                UpperCAmelCase_				:   Dict							= token_index
             writer.write(''' '''.join(__snake_case						) + '''\n'''						)
             index += 1

       return vocab_file, merge_file





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: str							, __snake_case					: List[Any]=False							, **__snake_case					: Dict						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= kwargs.pop('''add_prefix_space'''							, self.add_prefix_space						)
       if is_split_into_words or add_prefix_space:
          UpperCAmelCase_				:   str							= ''' ''' + text
       return (text, kwargs)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: "Conversation"						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= []
       for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(__snake_case							, add_special_tokens=__snake_case						) + [self.eos_token_id]						)
       if len(__snake_case						) > self.model_max_length:
          UpperCAmelCase_				:   str							= input_ids[-self.model_max_length :]
       return input_ids