




'''simple docstring'''





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCamelCase			:	Any							 =     logging.get_logger(__name__)


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

_lowerCamelCase			:	Optional[int]							 =     {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

_lowerCamelCase			:	List[Any]							 =     {
    '''allenai/led-base-16384''': 16384,
}


class        UpperCamelCase_					(				snake_case__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   LEDTokenizer
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			List[Any]=None      ,				UpperCAmelCase__   :			Optional[int]=None      ,				UpperCAmelCase__   :			Optional[Any]=None      ,				UpperCAmelCase__   :			str="replace"      ,				UpperCAmelCase__   :			Any="<s>"      ,				UpperCAmelCase__   :			Any="</s>"      ,				UpperCAmelCase__   :			str="</s>"      ,				UpperCAmelCase__   :			str="<s>"      ,				UpperCAmelCase__   :			Tuple="<unk>"      ,				UpperCAmelCase__   :			str="<pad>"      ,				UpperCAmelCase__   :			Optional[Any]="<mask>"      ,				UpperCAmelCase__   :			int=False      ,				UpperCAmelCase__   :			List[Any]=True      ,				**UpperCAmelCase__   :			str      ,				)		->List[str]:





           '''simple docstring'''

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

           A__   				=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('''add_prefix_space'''      ,				_A) != add_prefix_space:
                  A__   				=					getattr(_A      ,				pre_tok_state.pop('''type'''))
                  A__   				=					add_prefix_space
                  A__   				=					pre_tok_class(**_A)

           A__   				=					add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           A__   				=					'post_processor'
           A__   				=					getattr(self.backend_tokenizer      ,				_A      ,				_A)
           if tokenizer_component_instance:
                  A__   				=					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:
                         A__   				=					tuple(state['''sep'''])
                  if "cls" in state:
                         A__   				=					tuple(state['''cls'''])

                  A__   				=					False

                  if state.get('''add_prefix_space'''      ,				_A) != add_prefix_space:
                         A__   				=					add_prefix_space
                         A__   				=					True

                  if state.get('''trim_offsets'''      ,				_A) != trim_offsets:
                         A__   				=					trim_offsets
                         A__   				=					True

                  if changes_to_apply:
                         A__   				=					getattr(_A      ,				state.pop('''type'''))
                         A__   				=					component_class(**_A)
                         setattr(self.backend_tokenizer      ,				_A      ,				_A)


    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->int:





           '''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  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			Dict)		->List[str]:





           '''simple docstring'''

           A__   				=					AddedToken(_A      ,				lstrip=_A      ,				rstrip=_A) if isinstance(_A      ,				_A) else value
           A__   				=					value


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				*UpperCAmelCase__   :			Union[str, Any]      ,				**UpperCAmelCase__   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					kwargs.get('''is_split_into_words'''      ,				_A)

           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(*_A      ,				**_A)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				*UpperCAmelCase__   :			str      ,				**UpperCAmelCase__   :			Any)		->Tuple:





           '''simple docstring'''

           A__   				=					kwargs.get('''is_split_into_words'''      ,				_A)

           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(*_A      ,				**_A)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Union[str, Any] = None)		->Optional[int]:





           '''simple docstring'''

           A__   				=					self._tokenizer.model.save(_A      ,				name=_A)
           return tuple(_A)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[int]=None)		->List[str]:





           '''simple docstring'''

           A__   				=					[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  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			List[Any] = None)		->Dict:





           '''simple docstring'''

           A__   				=					[self.sep_token_id]
           A__   				=					[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  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Any = None      ,				UpperCAmelCase__   :			Optional[int] = PaddingStrategy.DO_NOT_PAD      ,				UpperCAmelCase__   :			Any = None      ,				UpperCAmelCase__   :			Any = None      ,				)		->str:





           '''simple docstring'''

           A__   				=					super()._pad(
               encoded_inputs=_A      ,				max_length=_A      ,				padding_strategy=_A      ,				pad_to_multiple_of=_A      ,				return_attention_mask=_A      ,				)

           # Load from model defaults
           if return_attention_mask is None:
                  A__   				=					'attention_mask' in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  A__   				=					encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  A__   				=					len(encoded_inputs['''global_attention_mask''']) != len(_A)

                  if needs_to_be_padded:
                         A__   				=					len(_A) - len(encoded_inputs['''global_attention_mask'''])

                         if self.padding_side == "right":
                                # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                                A__   				=					(
                                    encoded_inputs['global_attention_mask'] + [-1] * difference
                                )
                         elif self.padding_side == "left":
                                A__   				=					[-1] * difference + encoded_inputs[
                                    'global_attention_mask'
                                ]
                         else:
                                raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

           return encoded_inputs








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


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

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


