"""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


UpperCAmelCase__			=    logging.get_logger(__name__)


UpperCAmelCase__			=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase__			=    {
    """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""",
    },
}

UpperCAmelCase__			=    {
    """allenai/led-base-16384""": 1_6_3_8_4,
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				VOCAB_FILES_NAMES
    _snake_case  :	List[str]  				=				PRETRAINED_VOCAB_FILES_MAP
    _snake_case  :	Union[str, Any]  				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case  :	List[Any]  				=				LEDTokenizer
    _snake_case  :	Tuple  				=				['input_ids', 'attention_mask']


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	int=None ,					__lowerCAmelCase						:	int=None ,					__lowerCAmelCase						:	int=None ,					__lowerCAmelCase						:	str="replace" ,					__lowerCAmelCase						:	Any="<s>" ,					__lowerCAmelCase						:	Dict="</s>" ,					__lowerCAmelCase						:	Tuple="</s>" ,					__lowerCAmelCase						:	Any="<s>" ,					__lowerCAmelCase						:	str="<unk>" ,					__lowerCAmelCase						:	Tuple="<pad>" ,					__lowerCAmelCase						:	int="<mask>" ,					__lowerCAmelCase						:	int=False ,					__lowerCAmelCase						:	Dict=True ,					**__lowerCAmelCase						:	Dict ,					):
      super().__init__(
          __lowerCAmelCase ,					__lowerCAmelCase ,					tokenizer_file=__lowerCAmelCase ,					errors=__lowerCAmelCase ,					bos_token=__lowerCAmelCase ,					eos_token=__lowerCAmelCase ,					sep_token=__lowerCAmelCase ,					cls_token=__lowerCAmelCase ,					unk_token=__lowerCAmelCase ,					pad_token=__lowerCAmelCase ,					mask_token=__lowerCAmelCase ,					add_prefix_space=__lowerCAmelCase ,					trim_offsets=__lowerCAmelCase ,					**__lowerCAmelCase ,					)

      _UpperCAmelCase    			=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get("""add_prefix_space""" ,					__lowerCAmelCase       ) != add_prefix_space:
        _UpperCAmelCase    			=   getattr(__lowerCAmelCase ,					pre_tok_state.pop("""type"""       )       )
        _UpperCAmelCase    			=   add_prefix_space
        _UpperCAmelCase    			=   pre_tok_class(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   add_prefix_space

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

        _UpperCAmelCase    			=   False

        if state.get("""add_prefix_space""" ,					__lowerCAmelCase       ) != add_prefix_space:
          _UpperCAmelCase    			=   add_prefix_space
          _UpperCAmelCase    			=   True

        if state.get("""trim_offsets""" ,					__lowerCAmelCase       ) != trim_offsets:
          _UpperCAmelCase    			=   trim_offsets
          _UpperCAmelCase    			=   True

        if changes_to_apply:
          _UpperCAmelCase    			=   getattr(__lowerCAmelCase ,					state.pop("""type"""       )       )
          _UpperCAmelCase    			=   component_class(**__lowerCAmelCase       )
          setattr(self.backend_tokenizer ,					__lowerCAmelCase ,					__lowerCAmelCase       )


    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def   lowerCAmelCase_	(       self						:	int       ):
      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						:	str ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   AddedToken(__lowerCAmelCase ,					lstrip=__lowerCAmelCase ,					rstrip=__lowerCAmelCase       ) if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) else value
      _UpperCAmelCase    			=   value


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					*__lowerCAmelCase						:	Optional[int] ,					**__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   kwargs.get("""is_split_into_words""" ,					__lowerCAmelCase       )

      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(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					*__lowerCAmelCase						:	Tuple ,					**__lowerCAmelCase						:	Any       ):
      _UpperCAmelCase    			=   kwargs.get("""is_split_into_words""" ,					__lowerCAmelCase       )

      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(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[str] = None       ):
      _UpperCAmelCase    			=   self._tokenizer.model.save(__lowerCAmelCase ,					name=__lowerCAmelCase       )
      return tuple(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Dict=None       ):
      _UpperCAmelCase    			=   [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						:	Any ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None       ):
      _UpperCAmelCase    			=   [self.sep_token_id]
      _UpperCAmelCase    			=   [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						:	Optional[Any] ,					__lowerCAmelCase						:	Union[Dict[str, EncodedInput], BatchEncoding] ,					__lowerCAmelCase						:	Optional[int] = None ,					__lowerCAmelCase						:	PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,					__lowerCAmelCase						:	Optional[int] = None ,					__lowerCAmelCase						:	Optional[bool] = None ,					):
      _UpperCAmelCase    			=   super()._pad(
          encoded_inputs=__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding_strategy=__lowerCAmelCase ,					pad_to_multiple_of=__lowerCAmelCase ,					return_attention_mask=__lowerCAmelCase ,					)

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

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

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

      return encoded_inputs

"""simple docstring"""


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    """kwargs, expected""" ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(lowercase ,i + 1		) for i in range(10		)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4		), range(4 ,7		), range(7 ,10		)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1		), range(1 ,2		), range(2 ,3		)]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _distribute_shards(**lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected""" ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _split_gen_kwargs(lowercase ,lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected""" ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if expected is RuntimeError:
    with pytest.raises(lowercase		):
      _number_of_shards_in_gen_kwargs(lowercase		)
  else:
    _UpperCAmelCase    			=   _number_of_shards_in_gen_kwargs(lowercase		)
    assert out == expected
