





"""simple docstring"""
import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import 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 DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
		from transformers.models.dpr.modeling_dpr import (
		    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)






class       _SCREAMING_SNAKE_CASE:




							def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=13	,SCREAMING_SNAKE_CASE__=7	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=99	,SCREAMING_SNAKE_CASE__=32	,SCREAMING_SNAKE_CASE__=5	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=37	,SCREAMING_SNAKE_CASE__="gelu"	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=5_12	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=2	,SCREAMING_SNAKE_CASE__=0.0_2	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=0	,)    ->   List[str]:





													"""simple docstring"""






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




							def 					_UpperCamelCase  (		self							)    ->   Tuple:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Tuple	       =						ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size							)

													__SCREAMING_SNAKE_CASE     :int	       =						None
													if self.use_input_mask:
																			__SCREAMING_SNAKE_CASE     :List[Any]	       =						random_attention_mask([self.batch_size, self.seq_length]							)

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

													__SCREAMING_SNAKE_CASE     :str	       =						None
													__SCREAMING_SNAKE_CASE     :List[str]	       =						None
													__SCREAMING_SNAKE_CASE     :Dict	       =						None
													if self.use_labels:
																			__SCREAMING_SNAKE_CASE     :int	       =						ids_tensor([self.batch_size]	,self.type_sequence_label_size							)
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						ids_tensor([self.batch_size, self.seq_length]	,self.num_labels							)
																			__SCREAMING_SNAKE_CASE     :Dict	       =						ids_tensor([self.batch_size]	,self.num_choices							)

													__SCREAMING_SNAKE_CASE     :int	       =						self.get_config()

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




							def 					_UpperCamelCase  (		self							)    ->   Tuple:





													"""simple docstring"""






													return DPRConfig(
													    projection_dim=self.projection_dim	,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	,)




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   str:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Dict	       =						DPRContextEncoder(config=SCREAMING_SNAKE_CASE__							)
													model.to(SCREAMING_SNAKE_CASE__							)
													model.eval()
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						model(SCREAMING_SNAKE_CASE__	,attention_mask=SCREAMING_SNAKE_CASE__	,token_type_ids=SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :Dict	       =						model(SCREAMING_SNAKE_CASE__	,token_type_ids=SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :str	       =						model(SCREAMING_SNAKE_CASE__							)
													self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.projection_dim or self.hidden_size)							)




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :int	       =						DPRQuestionEncoder(config=SCREAMING_SNAKE_CASE__							)
													model.to(SCREAMING_SNAKE_CASE__							)
													model.eval()
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						model(SCREAMING_SNAKE_CASE__	,attention_mask=SCREAMING_SNAKE_CASE__	,token_type_ids=SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :Dict	       =						model(SCREAMING_SNAKE_CASE__	,token_type_ids=SCREAMING_SNAKE_CASE__							)
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model(SCREAMING_SNAKE_CASE__							)
													self.parent.assertEqual(result.pooler_output.shape	,(self.batch_size, self.projection_dim or self.hidden_size)							)




							def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Optional[int]:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :List[str]	       =						DPRReader(config=SCREAMING_SNAKE_CASE__							)
													model.to(SCREAMING_SNAKE_CASE__							)
													model.eval()
													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(
													    SCREAMING_SNAKE_CASE__	,attention_mask=SCREAMING_SNAKE_CASE__	,)

													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)							)
													self.parent.assertEqual(result.relevance_logits.shape	,(self.batch_size,)							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :List[str]	       =						self.prepare_config_and_inputs()
													(
													    __SCREAMING_SNAKE_CASE
													)     :Union[str, Any]	       =						config_and_inputs
													__SCREAMING_SNAKE_CASE     :List[str]	       =						{'''input_ids''': input_ids}
													return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE(     A					,			A					,			unittest.TestCase						):
							SCREAMING_SNAKE_CASE_     :   Any					=						(
							    (
							        DPRContextEncoder,
							        DPRQuestionEncoder,
							        DPRReader,
							    )
							    if is_torch_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE_     :   List[Any]					=						{'''feature-extraction''': DPRQuestionEncoder} if is_torch_available() else {}

							SCREAMING_SNAKE_CASE_     :   int					=						False
							SCREAMING_SNAKE_CASE_     :   Dict					=						False  # why?
							SCREAMING_SNAKE_CASE_     :   List[Any]					=						False
							SCREAMING_SNAKE_CASE_     :   int					=						False




							def 					_UpperCamelCase  (		self							)    ->   Any:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :List[Any]	       =						DPRModelTester(self							)
													__SCREAMING_SNAKE_CASE     :List[str]	       =						ConfigTester(self	,config_class=SCREAMING_SNAKE_CASE__	,hidden_size=37							)




							def 					_UpperCamelCase  (		self							)    ->   Dict:





													"""simple docstring"""






													self.config_tester.run_common_tests()




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_context_encoder(*SCREAMING_SNAKE_CASE__							)




							def 					_UpperCamelCase  (		self							)    ->   Tuple:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :List[Any]	       =						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_question_encoder(*SCREAMING_SNAKE_CASE__							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_reader(*SCREAMING_SNAKE_CASE__							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :int	       =						self.model_tester.prepare_config_and_inputs()[0]

													__SCREAMING_SNAKE_CASE     :str	       =						DPRQuestionEncoder(config=SCREAMING_SNAKE_CASE__							)
													model.to(SCREAMING_SNAKE_CASE__							)
													model.eval()

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			model.save_pretrained(SCREAMING_SNAKE_CASE__							)
																			__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						DPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE__	,projection_dim=5_12							)

													self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




							@slow
							def 					_UpperCamelCase  (		self							)    ->   List[str]:





													"""simple docstring"""






													for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__SCREAMING_SNAKE_CASE     :int	       =						DPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__							)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)

													for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						DPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__							)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)

													for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__SCREAMING_SNAKE_CASE     :Tuple	       =						DPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE__							)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)

													for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__SCREAMING_SNAKE_CASE     :str	       =						DPRReader.from_pretrained(SCREAMING_SNAKE_CASE__							)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






@require_torch
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




							@slow
							def 					_UpperCamelCase  (		self							)    ->   int:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						DPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''	,return_dict=SCREAMING_SNAKE_CASE__							)
													model.to(SCREAMING_SNAKE_CASE__							)

													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.tensor(
													    [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]	,dtype=torch.long	,device=SCREAMING_SNAKE_CASE__							)  # [CLS] hello, is my dog cute? [SEP]
													__SCREAMING_SNAKE_CASE     :Dict	       =						model(SCREAMING_SNAKE_CASE__							)[0]  # embedding shape = (1, 768)
													# compare the actual values for a slice.
													__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.tensor(
													    [
													        [
													            0.0_3_2_3_6_2_5_3,
													            0.1_2_7_5_3_3_3_5,
													            0.1_6_8_1_8_5_0_9,
													            0.0_0_2_7_9_7_8_6,
													            0.3_8_9_6_9_3_3,
													            0.2_4_2_6_4_9_4_5,
													            0.2_1_7_8_9_7_1,
													            -0.0_2_3_3_5_2_2_7,
													            -0.0_8_4_8_1_9_5_9,
													            -0.1_4_3_2_4_1_1_7,
													        ]
													    ]	,dtype=torch.float	,device=SCREAMING_SNAKE_CASE__	,)
													self.assertTrue(torch.allclose(output[:, :10]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)




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





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :str	       =						DPRReaderTokenizer.from_pretrained('''facebook/dpr-reader-single-nq-base'''							)
													__SCREAMING_SNAKE_CASE     :List[str]	       =						DPRReader.from_pretrained('''facebook/dpr-reader-single-nq-base'''							)
													model.to(SCREAMING_SNAKE_CASE__							)

													__SCREAMING_SNAKE_CASE     :List[Any]	       =						tokenizer(
													    questions='''What is love ?'''	,titles='''Haddaway'''	,texts='''What Is Love is a song recorded by the artist Haddaway'''	,padding=SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''	,)
													encoded_inputs.to(SCREAMING_SNAKE_CASE__							)

													__SCREAMING_SNAKE_CASE     :Any	       =						model(**SCREAMING_SNAKE_CASE__							)

													# compare the actual values for a slice.
													__SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor(
													    [[-10.30_05, -10.77_65, -11.48_72, -11.68_41, -11.93_12, -10.30_02, -9.8_5_4_4, -11.73_78, -12.08_21, -10.29_75]]	,dtype=torch.float	,device=SCREAMING_SNAKE_CASE__	,)

													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.tensor(
													    [[-11.06_84, -11.70_41, -11.53_97, -10.34_65, -10.87_91, -6.8_4_4_3, -11.99_59, -11.03_64, -10.00_96, -6.8_4_0_5]]	,dtype=torch.float	,device=SCREAMING_SNAKE_CASE__	,)
													self.assertTrue(torch.allclose(outputs.start_logits[:, :10]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)
													self.assertTrue(torch.allclose(outputs.end_logits[:, :10]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)






"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowerCamelCase_          =	logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_          =	{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

lowerCamelCase_          =	{
    "RUCAIBox/mvp": 1_0_2_4,
}



@lru_cache()
def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(
          list(range(ord('''!'''							)				,     ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,     ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,     ord('''ÿ'''							) + 1							)							)
      )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						bs[:]
      __SCREAMING_SNAKE_CASE     :str	       =						0
      for b in range(2**8							):
            if b not in bs:
                  bs.append(a_							)
                  cs.append(2**8 + n							)
                  n += 1
      __SCREAMING_SNAKE_CASE     :int	       =						[chr(a_							) for n in cs]
      return dict(zip(a_				,     a_							)							)



def   __lowerCamelCase     (							a_  :	Dict							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						set()
      __SCREAMING_SNAKE_CASE     :Dict	       =						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            __SCREAMING_SNAKE_CASE     :List[str]	       =						char
      return pairs







class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   str					=						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_     :   Tuple					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_     :   List[Any]					=						['''input_ids''', '''attention_mask''']




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else bos_token
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else eos_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else sep_token
             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else cls_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else unk_token
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else mask_token

             super().__init__(
                 errors=SCREAMING_SNAKE_CASE__	,bos_token=SCREAMING_SNAKE_CASE__	,eos_token=SCREAMING_SNAKE_CASE__	,unk_token=SCREAMING_SNAKE_CASE__	,sep_token=SCREAMING_SNAKE_CASE__	,cls_token=SCREAMING_SNAKE_CASE__	,pad_token=SCREAMING_SNAKE_CASE__	,mask_token=SCREAMING_SNAKE_CASE__	,add_prefix_space=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__	,)
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as vocab_handle:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						json.load(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{v: k for k, v in self.encoder.items()}
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						errors  # how to handle errors in decoding
             __SCREAMING_SNAKE_CASE     :Dict	       =						bytes_to_unicode()
             __SCREAMING_SNAKE_CASE     :Dict	       =						{v: k for k, v in self.byte_encoder.items()}
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as merges_handle:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						merges_handle.read().split('''\n'''							)[1:-1]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[tuple(merge.split()							) for merge in bpe_merges]
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						dict(zip(SCREAMING_SNAKE_CASE__	,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{}
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             __SCREAMING_SNAKE_CASE     :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 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return len(self.encoder							)




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             if token in self.cache:
                   return self.cache[token]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						tuple(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)

             if not pairs:
                   return token

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

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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						[]
             for token in re.findall(self.pat	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :List[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(SCREAMING_SNAKE_CASE__							).split(''' '''							)							)
             return bpe_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return self.decoder.get(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''''''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''	,errors=self.errors							)
             return text




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   Tuple[str]:





             """simple docstring"""






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

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

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						0
             with open(SCREAMING_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 SCREAMING_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!'''							)
                               __SCREAMING_SNAKE_CASE     :int	       =						token_index
                         writer.write(''' '''.join(SCREAMING_SNAKE_CASE__							) + '''\n'''							)
                         index += 1

             return vocab_file, merge_file




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[self.cls_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False							)    ->   List[int]:





             """simple docstring"""






             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=SCREAMING_SNAKE_CASE__	,token_ids_a=SCREAMING_SNAKE_CASE__	,already_has_special_tokens=SCREAMING_SNAKE_CASE__							)

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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[Any]	       =						[self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :str	       =						[self.cls_token_id]

             if token_ids_a is None:
                   return len(cls + token_ids_a + sep							) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						kwargs.pop('''add_prefix_space'''	,self.add_prefix_space							)
             if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__							) > 0 and not text[0].isspace()):
                   __SCREAMING_SNAKE_CASE     :Any	       =						''' ''' + text
             return (text, kwargs)