






'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_ta import TaTokenizer
else:
    lowerCamelCase_												=					None


lowerCamelCase_												=					logging.get_logger(__name__)

lowerCamelCase_												=					{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_												=					{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase_												=					{
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						VOCAB_FILES_NAMES
   snake_case    				=						PRETRAINED_VOCAB_FILES_MAP
   snake_case    				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case    				=						['''input_ids''', '''attention_mask''']
   snake_case    				=						TaTokenizer

   snake_case    				=						[]

   def __init__(		self :	Union[str, Any]      ,      __UpperCAmelCase :	Optional[int]=None      ,      __UpperCAmelCase :	List[str]=None      ,      __UpperCAmelCase :	List[str]="</s>"      ,      __UpperCAmelCase :	Any="<unk>"      ,      __UpperCAmelCase :	int="<pad>"      ,      __UpperCAmelCase :	Optional[Any]=100      ,      __UpperCAmelCase :	str=None      ,      **__UpperCAmelCase :	Tuple      ,      ):





         '''simple docstring'''





         if extra_ids > 0 and additional_special_tokens is None:
               _A			     =	[f'''<extra_id_{i}>''' for i in range(__UpperCAmelCase		)]
         elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra special tokens
               _A			     =	len(set(filter(lambda __UpperCAmelCase		: bool("extra_id_" in str(__UpperCAmelCase		)		)      ,      __UpperCAmelCase		)		)		)
               if extra_tokens != extra_ids:
                     raise ValueError(
                         f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                         " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                         " tokens"		)

         super().__init__(
             __UpperCAmelCase      ,      tokenizer_file=__UpperCAmelCase      ,      eos_token=__UpperCAmelCase      ,      unk_token=__UpperCAmelCase      ,      pad_token=__UpperCAmelCase      ,      extra_ids=__UpperCAmelCase      ,      additional_special_tokens=__UpperCAmelCase      ,      **__UpperCAmelCase      ,      )

         _A			     =	vocab_file
         _A			     =	False if not self.vocab_file else True
         _A			     =	extra_ids

   @staticmethod
   def 						lowerCAmelCase (		__UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	Tuple		):





         '''simple docstring'''





         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
               _A			     =	TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
               if init_max_model_length is not None and init_max_model_length != max_model_length:
                     return init_max_model_length
               elif init_max_model_length is None:
                     warnings.warn(
                         "This tokenizer was incorrectly instantiated with a model max length of"
                         f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                         " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                         " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                         f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                         f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                         f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                         " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                         " instantiate this tokenizer with `model_max_length` set to your preferred value."      ,      __UpperCAmelCase      ,      )

         return max_model_length

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Optional[str] = None		):





         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

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

         if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCAmelCase		):
               copyfile(self.vocab_file      ,      __UpperCAmelCase		)
               logger.info(f'''Copy vocab file to {out_vocab_file}'''		)

         return (out_vocab_file,)

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	List[int]      ,      __UpperCAmelCase :	Optional[List[int]] = None		):





         '''simple docstring'''





         _A			     =	token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
               return self.prefix_tokens + token_ids_a
         else:
               _A			     =	token_ids_a + [self.eos_token_id]
               return self.prefix_tokens + token_ids_a + token_ids_a

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	List[int]      ,      __UpperCAmelCase :	Optional[List[int]] = None		):





         '''simple docstring'''





         _A			     =	[self.eos_token_id]

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

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         return list(
             set(filter(lambda __UpperCAmelCase		: bool(re.search(R"<extra_id_\d+>"      ,      __UpperCAmelCase		)		) is not None      ,      self.additional_special_tokens		)		)		)

   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         return [self.convert_tokens_to_ids(__UpperCAmelCase		) for token in self.get_sentinel_tokens()]







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  MvpTokenizer
				lowercase			    =  MvpTokenizerFast
				lowercase			    =  True
				lowercase			    =  filter_roberta_detectors






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().setUp()
							lowerCAmelCase__      :      Dict         =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCAmelCase__      :      Any         =		dict(zip(a    ,     range(len(a   )   )   )   )
							lowerCAmelCase__      :      Union[str, Any]         =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCAmelCase__      :      Any         =		{'unk_token': '<unk>'}

							lowerCAmelCase__      :      int         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Optional[int]         =		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 							_lowerCamelCase		(						self					:      str    ,     **a					:      Tuple   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     **a					:      Optional[int]   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict   ):






							'''simple docstring'''







							return "lower newer", "lower newer"






				@cached_property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'   )






				@cached_property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							lowerCAmelCase__      :      List[Any]         =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      int         =		tokenizer(a    ,     max_length=len(a   )    ,     padding=a    ,     return_tensors='pt'   )
										self.assertIsInstance(a    ,     a   )

										self.assertEqual((2, 9)    ,     batch.input_ids.shape   )
										self.assertEqual((2, 9)    ,     batch.attention_mask.shape   )
										lowerCAmelCase__      :      List[Any]         =		batch.input_ids.tolist()[0]
										self.assertListEqual(a    ,     a   )
										# Test that special tokens are reset






				@require_torch
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      Any         =		tokenizer(a    ,     padding=a    ,     return_tensors='pt'   )
										# check if input_ids are returned and no labels
										self.assertIn('input_ids'    ,     a   )
										self.assertIn('attention_mask'    ,     a   )
										self.assertNotIn('labels'    ,     a   )
										self.assertNotIn('decoder_attention_mask'    ,     a   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		[
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      Tuple         =		tokenizer(text_target=a    ,     max_length=32    ,     padding='max_length'    ,     return_tensors='pt'   )
										self.assertEqual(32    ,     targets['input_ids'].shape[1]   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      str         =		tokenizer(
										    ['I am a small frog' * 1_024, 'I am a small frog']    ,     padding=a    ,     truncation=a    ,     return_tensors='pt'   )
										self.assertIsInstance(a    ,     a   )
										self.assertEqual(batch.input_ids.shape    ,     (2, 1_024)   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		['A long paragraph for summarization.']
							lowerCAmelCase__      :      Any         =		[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      List[Any]         =		tokenizer(a    ,     text_target=a    ,     return_tensors='pt'   )
										lowerCAmelCase__      :      Optional[int]         =		inputs['input_ids']
										lowerCAmelCase__      :      str         =		inputs['labels']
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      str         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      List[str]         =		self.tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      Optional[int]         =		'A, <mask> AllenNLP sentence.'
													lowerCAmelCase__      :      int         =		tokenizer_r.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )
													lowerCAmelCase__      :      Optional[int]         =		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']   )    ,     )

													lowerCAmelCase__      :      List[Any]         =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
													lowerCAmelCase__      :      Dict         =		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>']   )