


'''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


lowercase				=logging.get_logger(__name__)


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

lowercase				={
    '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',
    },
}

lowercase				={
    'allenai/led-base-16384': 16384,
}






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =VOCAB_FILES_NAMES
       UpperCAmelCase		 =PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase		 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase		 =LEDTokenizer
       UpperCAmelCase		 =["input_ids", "attention_mask"]






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




          '''simple docstring'''
          super().__init__(
              snake_case    ,				snake_case    ,				tokenizer_file=snake_case    ,				errors=snake_case    ,				bos_token=snake_case    ,				eos_token=snake_case    ,				sep_token=snake_case    ,				cls_token=snake_case    ,				unk_token=snake_case    ,				pad_token=snake_case    ,				mask_token=snake_case    ,				add_prefix_space=snake_case    ,				trim_offsets=snake_case    ,				**snake_case    ,				)

          _UpperCAmelCase  :  Dict				       =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('add_prefix_space'    ,				snake_case) != add_prefix_space:
             _UpperCAmelCase  :  Optional[Any]				       =getattr(snake_case    ,				pre_tok_state.pop('type'))
             _UpperCAmelCase  :  Optional[Any]				       =add_prefix_space
             _UpperCAmelCase  :  Tuple				       =pre_tok_class(**snake_case)

          _UpperCAmelCase  :  Any				       =add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _UpperCAmelCase  :  Dict				       ='post_processor'
          _UpperCAmelCase  :  List[Any]				       =getattr(self.backend_tokenizer    ,				snake_case    ,				snake_case)
          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  :  List[Any]				       =tuple(state['sep'])
             if "cls" in state:
                _UpperCAmelCase  :  List[str]				       =tuple(state['cls'])

             _UpperCAmelCase  :  List[Any]				       =False

             if state.get('add_prefix_space'    ,				snake_case) != add_prefix_space:
                _UpperCAmelCase  :  List[Any]				       =add_prefix_space
                _UpperCAmelCase  :  Union[str, Any]				       =True

             if state.get('trim_offsets'    ,				snake_case) != trim_offsets:
                _UpperCAmelCase  :  Dict				       =trim_offsets
                _UpperCAmelCase  :  List[Any]				       =True

             if changes_to_apply:
                _UpperCAmelCase  :  Optional[int]				       =getattr(snake_case    ,				state.pop('type'))
                _UpperCAmelCase  :  str				       =component_class(**snake_case)
                setattr(self.backend_tokenizer    ,				snake_case    ,				snake_case)






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




          '''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    ,				snake_case)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =AddedToken(snake_case    ,				lstrip=snake_case    ,				rstrip=snake_case) if isinstance(snake_case    ,				snake_case) else value
          _UpperCAmelCase  :  Any				       =value






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    BatchEncoding:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =kwargs.get('is_split_into_words'    ,				snake_case)

          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(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    BatchEncoding:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =kwargs.get('is_split_into_words'    ,				snake_case)

          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(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None)		->    Tuple[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =self._tokenizer.model.save(snake_case    ,				name=snake_case)
          return tuple(snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=None)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, 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    ,				snake_case    ,				snake_case = None)		->    List[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[self.sep_token_id]
          _UpperCAmelCase  :  Union[str, Any]				       =[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      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = PaddingStrategy.DO_NOT_PAD    ,				snake_case = None    ,				snake_case = None    ,				)		->    dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =super()._pad(
              encoded_inputs=snake_case    ,				max_length=snake_case    ,				padding_strategy=snake_case    ,				pad_to_multiple_of=snake_case    ,				return_attention_mask=snake_case    ,				)

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

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

             if needs_to_be_padded:
                _UpperCAmelCase  :  List[Any]				       =len(snake_case) - 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`
                   _UpperCAmelCase  :  Any				       =(
                       encoded_inputs['global_attention_mask'] + [-1] * difference
                   )
                elif self.padding_side == "left":
                   _UpperCAmelCase  :  Dict				       =[-1] * difference + encoded_inputs[
                       'global_attention_mask'
                   ]
                else:
                   raise ValueError('Invalid padding strategy:' + str(self.padding_side))

          return encoded_inputs





'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class    __magic_name__				(						unittest.TestCase							):






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




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






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




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =[
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              # Removed: 'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =[
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              # Removed: 'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(snake_case))






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




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  str				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






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




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =[
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  int				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          # pass variant but use the non-variant filenames
          _UpperCAmelCase  :  Optional[int]				       =[
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          _UpperCAmelCase  :  Optional[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  List[str]				       ='fp16'
          self.assertFalse(is_safetensors_compatible(snake_case    ,				variant=snake_case))






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




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =[
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  Optional[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          # pass variant but use the non-variant filenames
          _UpperCAmelCase  :  Optional[int]				       =[
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
          ]
          _UpperCAmelCase  :  List[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






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




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              # 'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  Tuple				       ='fp16'
          self.assertFalse(is_safetensors_compatible(snake_case    ,				variant=snake_case))

