
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						LongformerTokenizer
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						LongformerTokenizerFast
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    Optional[Any]:
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              SCREAMING_SNAKE_CASE_							:				Any		=[
                  'l',
                  'o',
                  'w',
                  'e',
                  'r',
                  's',
                  't',
                  'i',
                  'd',
                  'n',
                  '\u0120',
                  '\u0120l',
                  '\u0120n',
                  '\u0120lo',
                  '\u0120low',
                  'er',
                  '\u0120lowest',
                  '\u0120newer',
                  '\u0120wider',
                  '<unk>',
              ]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=dict(zip(__A						,       range(len(__A				)				)				)				)
              SCREAMING_SNAKE_CASE_							:				str		=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
              SCREAMING_SNAKE_CASE_							:				Tuple		={'unk_token': '<unk>'}

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''vocab_file''']				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''merges_file''']				)
              with open(self.vocab_file						,       '''w'''						,       encoding='''utf-8'''				) as fp:
                     fp.write(json.dumps(__A				) + '''\n'''				)
              with open(self.merges_file						,       '''w'''						,       encoding='''utf-8'''				) as fp:
                     fp.write('''\n'''.join(__A				)				)


       def 	_snake_case				(    self						,       **__A				)						->    Optional[Any]:
              kwargs.update(self.special_tokens_map				)
              return self.tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              kwargs.update(self.special_tokens_map				)
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       __A				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				int		='lower newer'
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='lower newer'
              return input_text, output_text


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.tokenizer_class(self.vocab_file						,       self.merges_file						,       **self.special_tokens_map				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		='lower newer'
              SCREAMING_SNAKE_CASE_							:				List[str]		=['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize(__A				)  # , add_prefix_space=True)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokens + [tokenizer.unk_token]
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__A				)						,       __A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_tokenizer()

              self.assertListEqual(tokenizer.encode('''Hello world!'''						,       add_special_tokens=__A				)						,       [0, 31_414, 232, 328, 2]				)
              self.assertListEqual(
                  tokenizer.encode('''Hello world! cécé herlolip 418'''						,       add_special_tokens=__A				)						,       [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]						,       )


       @slow
       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=tokenizer.encode('''sequence builders'''						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode('''multi-sequence build'''						,       add_special_tokens=__A				)

              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.encode(
                  '''sequence builders'''						,       add_special_tokens=__A						,       add_prefix_space=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.encode(
                  '''sequence builders'''						,       '''multi-sequence build'''						,       add_special_tokens=__A						,       add_prefix_space=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.build_inputs_with_special_tokens(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer.build_inputs_with_special_tokens(__A						,       __A				)

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_tokenizer()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		='Encode this sequence.'
              SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer.byte_encoder[' '.encode('''utf-8'''				)[0]]

              # Testing encoder arguments
              SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer.encode(__A						,       add_special_tokens=__A						,       add_prefix_space=__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
              self.assertNotEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.encode(__A						,       add_special_tokens=__A						,       add_prefix_space=__A				)
              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
              self.assertEqual(__A						,       __A				)

              tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer.encode(__A						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
              self.assertNotEqual(__A						,       __A				)

              # Testing spaces after special tokens
              SCREAMING_SNAKE_CASE_							:				Optional[int]		='<mask>'
              tokenizer.add_special_tokens(
                  {'''mask_token''': AddedToken(__A						,       lstrip=__A						,       rstrip=__A				)}				)  # mask token has a left space
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.convert_tokens_to_ids(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		='Encode <mask> sequence'
              SCREAMING_SNAKE_CASE_							:				List[str]		='Encode <mask>sequence'

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=encoded.index(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
              self.assertEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=encoded.index(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
              self.assertNotEqual(__A						,       __A				)


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


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







              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.rust_tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=self.tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		='A, <mask> AllenNLP sentence.'
                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_r.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer_p.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)

                            # token_type_ids should put 0 everywhere
                            self.assertEqual(sum(tokens_r['''token_type_ids''']				)						,       sum(tokens_p['''token_type_ids''']				)				)

                            # attention_mask should put 1 everywhere, so sum over length should be 1
                            self.assertEqual(
                                sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)						,       sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)						,       )

                            SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

                            # Rust correctly handles the space before the mask while python doesnt
                            self.assertSequenceEqual(tokens_p['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
                            self.assertSequenceEqual(tokens_r['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

                            self.assertSequenceEqual(
                                __A						,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                            self.assertSequenceEqual(
                                __A						,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)


       def 	_snake_case				(    self				)						->    Any:







              for trim_offsets, add_prefix_space in itertools.product([True, False]						,       repeat=2				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.rust_tokenizer_class.from_pretrained(
                         self.tmpdirname						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

                     self.assertEqual(pre_tokenizer_state['''add_prefix_space''']						,       __A				)

                     self.assertEqual(post_processor_state['''add_prefix_space''']						,       __A				)
                     self.assertEqual(post_processor_state['''trim_offsets''']						,       __A				)





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







              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                            SCREAMING_SNAKE_CASE_							:				str		='hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                            SCREAMING_SNAKE_CASE_							:				Tuple		=F'{text_of_1_token} {text_of_1_token}'

                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (len(__A				) + 1, len(__A				) + 1 + len(__A				))						,       )

                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (len(__A				) + 1, len(__A				) + 1 + len(__A				))						,       )

                            SCREAMING_SNAKE_CASE_							:				List[str]		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (len(__A				), len(__A				) + 1 + len(__A				))						,       )

                            SCREAMING_SNAKE_CASE_							:				Any		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (len(__A				), len(__A				) + 1 + len(__A				))						,       )

                            SCREAMING_SNAKE_CASE_							:				Any		=F' {text}'

                            # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                            #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                            # )
                            # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                            # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                            # self.assertEqual(
                            #     encoding.offset_mapping[1],
                            #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                            # )

                            SCREAMING_SNAKE_CASE_							:				str		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (1, 1 + len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (1 + len(__A				) + 1, 1 + len(__A				) + 1 + len(__A				))						,       )

                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				str		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (0, 1 + len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (1 + len(__A				), 1 + len(__A				) + 1 + len(__A				))						,       )

                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.rust_tokenizer_class.from_pretrained(
                                __A						,       use_fast=__A						,       add_prefix_space=__A						,       trim_offsets=__A				)
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer_r(__A						,       return_offsets_mapping=__A						,       add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]						,       (0, 1 + len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]						,       (1 + len(__A				), 1 + len(__A				) + 1 + len(__A				))						,       )






'''simple docstring'''



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Dict        =						'''char'''
       A						:				Any        =						'''bpe'''
       A						:				Dict        =						'''wp'''

UpperCamelCase_    	=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[Any]        =						['''image_processor''', '''char_tokenizer''']
       A						:				int        =						'''ViTImageProcessor'''
       A						:				List[str]        =						'''MgpstrTokenizer'''







       def __init__(      self,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.',							A,							)
                   SCREAMING_SNAKE_CASE   :      str    					=						kwargs.pop('feature_extractor'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError('You need to specify an `image_processor`.'  )
             if tokenizer is None:
                   raise ValueError('You need to specify a `tokenizer`.'  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoTokenizer.from_pretrained('gpt2'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoTokenizer.from_pretrained('bert-base-uncased'  )

             super().__init__(A,							A  )







       def __call__(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if images is None and text is None:
                   raise ValueError('You need to specify either an `images` or `text` input to process.'  )

             if images is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.image_processor(A,							return_tensors=A,							**A  )
             if text is not None:
                   SCREAMING_SNAKE_CASE   :      int    					=						self.char_tokenizer(A,							return_tensors=A,							**A  )

             if text is None:
                   return inputs
             elif images is None:
                   return encodings
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						encodings['input_ids']
                   return inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						sequences
             SCREAMING_SNAKE_CASE   :      List[str]    					=						char_preds.size(0  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						self._decode_helper(A,							'char'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						self._decode_helper(A,							'bpe'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self._decode_helper(A,							'wp'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
             for i in range(A  ):
                   SCREAMING_SNAKE_CASE   :      str    					=						[char_scores[i], bpe_scores[i], wp_scores[i]]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						[char_strs[i], bpe_strs[i], wp_strs[i]]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						scores.index(max(A  )  )
                   final_strs.append(strs[max_score_index]  )
                   final_scores.append(scores[max_score_index]  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      int    					=						final_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						final_scores
             SCREAMING_SNAKE_CASE   :      Dict    					=						char_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						bpe_strs
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						wp_strs
             return out







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             if format == DecodeType.CHARACTER:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.char_decode
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						1
                   SCREAMING_SNAKE_CASE   :      str    					=						'[s]'
             elif format == DecodeType.BPE:
                   SCREAMING_SNAKE_CASE   :      str    					=						self.bpe_decode
                   SCREAMING_SNAKE_CASE   :      str    					=						2
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						'#'
             elif format == DecodeType.WORDPIECE:
                   SCREAMING_SNAKE_CASE   :      Any    					=						self.wp_decode
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						102
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						'[SEP]'
             else:
                   raise ValueError(F"Format {format} is not supported."  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[], []
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_logits.size(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						pred_logits.size(1  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						pred_logits.topk(1,							dim=-1,							largest=A,							sorted=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_index.view(-1,							A  )[:, 1:]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						decoder(A  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.nn.functional.softmax(A,							dim=2  ).max(dim=2  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						preds_max_prob[:, 1:]

             for index in range(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_str[index].find(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						preds_str[index][:pred_eos]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						preds_index[index].cpu().tolist()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_index.index(A  ) if eos_token in pred_index else -1
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_max_prob[index][: pred_eos_index + 1]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
                   dec_strs.append(A  )
                   conf_scores.append(A  )

             return dec_strs, conf_scores







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[seq.replace(' ',							''  ) for seq in self.char_tokenizer.batch_decode(A  )]
             return decode_strs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.bpe_tokenizer.batch_decode(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						[seq.replace(' ',							''  ) for seq in self.wp_tokenizer.batch_decode(A  )]
             return decode_strs


