


'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__snake_case							   =  logging.get_logger(__name__)


__snake_case							   =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
__snake_case							   =  {
    '''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''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

__snake_case							   =  {
    '''RUCAIBox/mvp''': 1024,
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       VOCAB_FILES_NAMES
		_a									=       PRETRAINED_VOCAB_FILES_MAP
		_a									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a									=       ['input_ids', 'attention_mask']
		_a									=       MvpTokenizer




		def __init__(	self					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     UpperCamelCase_="replace"					,     UpperCamelCase_="<s>"					,     UpperCamelCase_="</s>"					,     UpperCamelCase_="</s>"					,     UpperCamelCase_="<s>"					,     UpperCamelCase_="<unk>"					,     UpperCamelCase_="<pad>"					,     UpperCamelCase_="<mask>"					,     UpperCamelCase_=False					,     UpperCamelCase_=True					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(
							    UpperCamelCase_					,     UpperCamelCase_					,     tokenizer_file=UpperCamelCase_					,     errors=UpperCamelCase_					,     bos_token=UpperCamelCase_					,     eos_token=UpperCamelCase_					,     sep_token=UpperCamelCase_					,     cls_token=UpperCamelCase_					,     unk_token=UpperCamelCase_					,     pad_token=UpperCamelCase_					,     mask_token=UpperCamelCase_					,     add_prefix_space=UpperCamelCase_					,     trim_offsets=UpperCamelCase_					,     **UpperCamelCase_					,     )

							UpperCamelCase__     :Optional[int]							      =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('''add_prefix_space'''					,     UpperCamelCase_							) != add_prefix_space:
												UpperCamelCase__     :List[str]							      =				getattr(UpperCamelCase_					,     pre_tok_state.pop('''type'''							)							)
												UpperCamelCase__     :Dict							      =				add_prefix_space
												UpperCamelCase__     :List[str]							      =				pre_tok_class(**UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCamelCase__     :Any							      =				'''post_processor'''
							UpperCamelCase__     :Optional[Any]							      =				getattr(self.backend_tokenizer					,     UpperCamelCase_					,     UpperCamelCase_							)
							if tokenizer_component_instance:
												UpperCamelCase__     :Optional[Any]							      =				json.loads(tokenizer_component_instance.__getstate__()							)

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
																	UpperCamelCase__     :Union[str, Any]							      =				tuple(state['''sep''']							)
												if "cls" in state:
																	UpperCamelCase__     :Tuple							      =				tuple(state['''cls''']							)

												UpperCamelCase__     :List[Any]							      =				False

												if state.get('''add_prefix_space'''					,     UpperCamelCase_							) != add_prefix_space:
																	UpperCamelCase__     :List[Any]							      =				add_prefix_space
																	UpperCamelCase__     :List[Any]							      =				True

												if state.get('''trim_offsets'''					,     UpperCamelCase_							) != trim_offsets:
																	UpperCamelCase__     :Tuple							      =				trim_offsets
																	UpperCamelCase__     :Union[str, Any]							      =				True

												if changes_to_apply:
																	UpperCamelCase__     :str							      =				getattr(UpperCamelCase_					,     state.pop('''type'''							)							)
																	UpperCamelCase__     :str							      =				component_class(**UpperCamelCase_							)
																	setattr(self.backend_tokenizer					,     UpperCamelCase_					,     UpperCamelCase_							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if self._mask_token is None:
												if self.verbose:
																	logger.error('''Using mask_token, but it is not set yet.'''							)
												return None
							return str(self._mask_token							)




		@mask_token.setter
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				AddedToken(UpperCamelCase_					,     lstrip=UpperCamelCase_					,     rstrip=UpperCamelCase_							) if isinstance(UpperCamelCase_					,     UpperCamelCase_							) else value
							UpperCamelCase__     :int							      =				value




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				kwargs.get('''is_split_into_words'''					,     UpperCamelCase_							)

							if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
												    '''to use it with pretokenized inputs.'''							)

							return super()._batch_encode_plus(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				kwargs.get('''is_split_into_words'''					,     UpperCamelCase_							)

							if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
												    '''to use it with pretokenized inputs.'''							)

							return super()._encode_plus(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self._tokenizer.model.save(UpperCamelCase_					,     name=UpperCamelCase_							)
							return tuple(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				[self.sep_token_id]
							UpperCamelCase__     :Tuple							      =				[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]



'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)