



import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    MegatronBertForCausalLM,
							    MegatronBertForMaskedLM,
							    MegatronBertForMultipleChoice,
							    MegatronBertForNextSentencePrediction,
							    MegatronBertForPreTraining,
							    MegatronBertForQuestionAnswering,
							    MegatronBertForSequenceClassification,
							    MegatronBertForTokenClassification,
							    MegatronBertModel,
							)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=7	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=99	,__lowerCamelCase=64	,__lowerCamelCase=32	,__lowerCamelCase=5	,__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	,)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  parent
						lowerCAmelCase__  :       int				=  batch_size
						lowerCAmelCase__  :       Union[str, Any]				=  seq_length
						lowerCAmelCase__  :       Any				=  is_training
						lowerCAmelCase__  :       List[str]				=  use_input_mask
						lowerCAmelCase__  :       Optional[int]				=  use_token_type_ids
						lowerCAmelCase__  :       Dict				=  use_labels
						lowerCAmelCase__  :       List[Any]				=  vocab_size
						lowerCAmelCase__  :       List[Any]				=  hidden_size
						lowerCAmelCase__  :       List[Any]				=  embedding_size
						lowerCAmelCase__  :       str				=  num_hidden_layers
						lowerCAmelCase__  :       List[str]				=  num_attention_heads
						lowerCAmelCase__  :       Optional[Any]				=  intermediate_size
						lowerCAmelCase__  :       Optional[int]				=  hidden_act
						lowerCAmelCase__  :       int				=  hidden_dropout_prob
						lowerCAmelCase__  :       Union[str, Any]				=  attention_probs_dropout_prob
						lowerCAmelCase__  :       Tuple				=  max_position_embeddings
						lowerCAmelCase__  :       List[str]				=  type_vocab_size
						lowerCAmelCase__  :       int				=  type_sequence_label_size
						lowerCAmelCase__  :       List[Any]				=  initializer_range
						lowerCAmelCase__  :       Any				=  num_labels
						lowerCAmelCase__  :       Optional[int]				=  num_choices
						lowerCAmelCase__  :       Optional[Any]				=  scope



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






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

						lowerCAmelCase__  :       int				=  None
						if self.use_input_mask:
										lowerCAmelCase__  :       Union[str, Any]				=  random_attention_mask([self.batch_size, self.seq_length]				)

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

						lowerCAmelCase__  :       List[str]				=  None
						lowerCAmelCase__  :       List[Any]				=  None
						lowerCAmelCase__  :       Union[str, Any]				=  None
						if self.use_labels:
										lowerCAmelCase__  :       Any				=  ids_tensor([self.batch_size]	,self.type_sequence_label_size				)
										lowerCAmelCase__  :       Tuple				=  ids_tensor([self.batch_size, self.seq_length]	,self.num_labels				)
										lowerCAmelCase__  :       List[Any]				=  ids_tensor([self.batch_size]	,self.num_choices				)

						lowerCAmelCase__  :       Dict				=  self.get_config()

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



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

						"""simple docstring"""






						return MegatronBertConfig(
						    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	,embedding_size=self.embedding_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	,is_decoder=__lowerCamelCase	,initializer_range=self.initializer_range	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  MegatronBertModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       str				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase	,token_type_ids=__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  model(__lowerCamelCase				)

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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MegatronBertForMaskedLM(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MegatronBertForCausalLM(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  MegatronBertForNextSentencePrediction(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       List[str]				=  model(
						    __lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase	,)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, 2)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  MegatronBertForPreTraining(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       List[Any]				=  model(
						    __lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase	,next_sentence_label=__lowerCamelCase	,)
						self.parent.assertEqual(result.prediction_logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)				)
						self.parent.assertEqual(result.seq_relationship_logits.shape	,(self.batch_size, 2)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  MegatronBertForQuestionAnswering(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       str				=  model(
						    __lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,start_positions=__lowerCamelCase	,end_positions=__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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.num_labels
						lowerCAmelCase__  :       Tuple				=  MegatronBertForSequenceClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.num_labels
						lowerCAmelCase__  :       str				=  MegatronBertForTokenClassification(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.num_labels)				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.num_choices
						lowerCAmelCase__  :       Union[str, Any]				=  MegatronBertForMultipleChoice(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[int]				=  input_ids.unsqueeze(1				).expand(-1	,self.num_choices	,-1				).contiguous()
						lowerCAmelCase__  :       Any				=  token_type_ids.unsqueeze(1				).expand(-1	,self.num_choices	,-1				).contiguous()
						lowerCAmelCase__  :       Optional[Any]				=  input_mask.unsqueeze(1				).expand(-1	,self.num_choices	,-1				).contiguous()
						lowerCAmelCase__  :       Any				=  model(
						    __lowerCamelCase	,attention_mask=__lowerCamelCase	,token_type_ids=__lowerCamelCase	,labels=__lowerCamelCase	,)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_choices)				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)		,	(
						    lowerCAmelCase__
						)		,	(
						    lowerCAmelCase__
						)		,	(
						    lowerCAmelCase__
						)		,	(
						    lowerCAmelCase__
						)		,	(
						    lowerCAmelCase__
						)		,	(
						    lowerCAmelCase__
						)		,	
						)  :       Optional[Any]				=  config_and_inputs
						lowerCAmelCase__  :       Tuple				=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(
		    (
		        MegatronBertModel,
		        MegatronBertForMaskedLM,
		        MegatronBertForCausalLM,
		        MegatronBertForMultipleChoice,
		        MegatronBertForNextSentencePrediction,
		        MegatronBertForPreTraining,
		        MegatronBertForQuestionAnswering,
		        MegatronBertForSequenceClassification,
		        MegatronBertForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		snake_case_			=(
		    {
		        """feature-extraction""": MegatronBertModel,
		        """fill-mask""": MegatronBertForMaskedLM,
		        """question-answering""": MegatronBertForQuestionAnswering,
		        """text-classification""": MegatronBertForSequenceClassification,
		        """text-generation""": MegatronBertForCausalLM,
		        """token-classification""": MegatronBertForTokenClassification,
		        """zero-shot""": MegatronBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		snake_case_			=True
		# test_resize_embeddings = False
		snake_case_			=False



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=False				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  super()._prepare_for_class(__lowerCamelCase	,__lowerCamelCase	,return_labels=__lowerCamelCase				)

						if return_labels:
										if model_class in get_values(__lowerCamelCase				):
														lowerCAmelCase__  :       int				=  torch.zeros(
														    (self.model_tester.batch_size, self.model_tester.seq_length)	,dtype=torch.long	,device=__lowerCamelCase				)
														lowerCAmelCase__  :       str				=  torch.zeros(
														    self.model_tester.batch_size	,dtype=torch.long	,device=__lowerCamelCase				)
						return inputs_dict



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  MegatronBertModelTester(self				)
						lowerCAmelCase__  :       Union[str, Any]				=  ConfigTester(self	,config_class=__lowerCamelCase	,hidden_size=37				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowerCamelCase				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowerCamelCase				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowerCamelCase				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowerCamelCase				)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict):





				'''simple docstring'''

				return torch.tensor(
				    lowerCamelCase_ ,dtype=torch.long ,device=lowerCamelCase_ ,)


__snake_case       :	str            =1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		@slow
		@unittest.skip('''Model is not available.'''				)
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  '''nvidia/megatron-bert-uncased-345m'''
						if "MYDIR" in os.environ:
										lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(os.environ['''MYDIR''']	,__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  MegatronBertModel.from_pretrained(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.half()
						lowerCAmelCase__  :       Union[str, Any]				=  _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]				)
						with torch.no_grad():
										lowerCAmelCase__  :       str				=  model(__lowerCamelCase				)[0]
						lowerCAmelCase__  :       Optional[Any]				=  torch.Size((1, 9, 10_24)				)
						self.assertEqual(output.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
						for ii in range(3				):
										for jj in range(3				):
														lowerCAmelCase__  :       Optional[Any]				=  output[0, ii, jj]
														lowerCAmelCase__  :       Tuple				=  expected[3 * ii + jj]
														lowerCAmelCase__  :       List[str]				=  '''ii={} jj={} a={} b={}'''.format(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
														self.assertTrue(math.isclose(__lowerCamelCase	,__lowerCamelCase	,rel_tol=__lowerCamelCase	,abs_tol=__lowerCamelCase				)	,msg=__lowerCamelCase				)






import json
import os
from typing import Optional, Tuple

import regex as re

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


__snake_case       :	int            =logging.get_logger(__name__)

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

__snake_case       :	Any            ={
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

__snake_case       :	Optional[int]            ={
    'ctrl': 2_5_6,
}

__snake_case       :	Optional[int]            ={
    'Pregnancy': 1_6_8_6_2_9,
    'Christianity': 7_6_7_5,
    'Explain': 1_0_6_4_2_3,
    'Fitness': 6_3_4_4_0,
    'Saving': 6_3_1_6_3,
    'Ask': 2_7_1_7_1,
    'Ass': 9_5_9_8_5,
    'Joke': 1_6_3_5_0_9,
    'Questions': 4_5_6_2_2,
    'Thoughts': 4_9_6_0_5,
    'Retail': 5_2_3_4_2,
    'Feminism': 1_6_4_3_3_8,
    'Writing': 1_1_9_9_2,
    'Atheism': 1_9_2_2_6_3,
    'Netflix': 4_8_6_1_6,
    'Computing': 3_9_6_3_9,
    'Opinion': 4_3_2_1_3,
    'Alone': 4_4_9_6_7,
    'Funny': 5_8_9_1_7,
    'Gaming': 4_0_3_5_8,
    'Human': 4_0_8_8,
    'India': 1_3_3_1,
    'Joker': 7_7_1_3_8,
    'Diet': 3_6_2_0_6,
    'Legal': 1_1_8_5_9,
    'Norman': 4_9_3_9,
    'Tip': 7_2_6_8_9,
    'Weight': 5_2_3_4_3,
    'Movies': 4_6_2_7_3,
    'Running': 2_3_4_2_5,
    'Science': 2_0_9_0,
    'Horror': 3_7_7_9_3,
    'Confession': 6_0_5_7_2,
    'Finance': 1_2_2_5_0,
    'Politics': 1_6_3_6_0,
    'Scary': 1_9_1_9_8_5,
    'Support': 1_2_6_5_4,
    'Technologies': 3_2_5_1_6,
    'Teenage': 6_6_1_6_0,
    'Event': 3_2_7_6_9,
    'Learned': 6_7_4_6_0,
    'Notion': 1_8_2_7_7_0,
    'Wikipedia': 3_7_5_8_3,
    'Books': 6_6_6_5,
    'Extract': 7_6_0_5_0,
    'Confessions': 1_0_2_7_0_1,
    'Conspiracy': 7_5_9_3_2,
    'Links': 6_3_6_7_4,
    'Narcissus': 1_5_0_4_2_5,
    'Relationship': 5_4_7_6_6,
    'Relationships': 1_3_4_7_9_6,
    'Reviews': 4_1_6_7_1,
    'News': 4_2_5_6,
    'Translation': 2_6_8_2_0,
    'multilingual': 1_2_8_4_0_6,
}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  set()
				lowerCAmelCase__  :       Tuple				=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char))
								lowerCAmelCase__  :       List[Any]				=  char

				lowerCAmelCase__  :       Dict				=  set(lowerCamelCase_)
				return pairs



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case_			=CONTROL_CODES



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase="<unk>"	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(unk_token=__lowerCamelCase	,**__lowerCamelCase				)

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



		@property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return len(self.encoder				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						if token in self.cache:
										return self.cache[token]
						lowerCAmelCase__  :       Union[str, Any]				=  tuple(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  tuple(list(word[:-1]				) + [word[-1] + '''</w>''']				)
						lowerCAmelCase__  :       Union[str, Any]				=  get_pairs(__lowerCamelCase				)

						if not pairs:
										return token

						while True:
										lowerCAmelCase__  :       Optional[Any]				=  min(__lowerCamelCase	,key=lambda __lowerCamelCase				: self.bpe_ranks.get(__lowerCamelCase	,float('''inf'''				)				)				)
										if bigram not in self.bpe_ranks:
														break
										lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  bigram
										lowerCAmelCase__  :       Optional[int]				=  []
										lowerCAmelCase__  :       int				=  0
										while i < len(__lowerCamelCase				):
														try:
																		lowerCAmelCase__  :       Optional[Any]				=  word.index(__lowerCamelCase	,__lowerCamelCase				)
														except ValueError:
																		new_word.extend(word[i:]				)
																		break
														else:
																		new_word.extend(word[i:j]				)
																		lowerCAmelCase__  :       int				=  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
										lowerCAmelCase__  :       Optional[Any]				=  tuple(__lowerCamelCase				)
										lowerCAmelCase__  :       Any				=  new_word
										if len(__lowerCamelCase				) == 1:
														break
										else:
														lowerCAmelCase__  :       Optional[Any]				=  get_pairs(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  '''@@ '''.join(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  word[:-4]
						lowerCAmelCase__  :       List[Any]				=  word
						return word



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  []

						lowerCAmelCase__  :       int				=  re.findall(R'''\S+\n?'''	,__lowerCamelCase				)

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



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

						"""simple docstring"""






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



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

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  ''' '''.join(__lowerCamelCase				).replace('''@@ '''	,''''''				).strip()
						return out_string



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Tuple[str]:

						"""simple docstring"""






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

						lowerCAmelCase__  :       Optional[Any]				=  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!'''				)
																		lowerCAmelCase__  :       Optional[int]				=  token_index
														writer.write(''' '''.join(__lowerCamelCase				) + '''\n'''				)
														index += 1

						return vocab_file, merge_file

		# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
		#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
		#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
		#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
		#     return ''.join(tokens_generated_so_far)

