



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


SCREAMING_SNAKE_CASE_:Any 			=		logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE_: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''',
    },
}

SCREAMING_SNAKE_CASE_:Dict 			=		{
    '''allenai/led-base-16384''': 16_384,
}






class SCREAMING_SNAKE_CASE__	(						_lowercase       ):




    '''simple docstring'''






    __lowerCamelCase     :					str   = VOCAB_FILES_NAMES
    __lowerCamelCase     :					Union[str, Any]   = PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase     :					Optional[int]   = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase     :					Dict   = LEDTokenizer
    __lowerCamelCase     :					List[Any]   = ['''input_ids''', '''attention_mask''']






    def __init__(			self,						lowerCamelCase__=None,						lowerCamelCase__=None,						lowerCamelCase__=None,						lowerCamelCase__="replace",						lowerCamelCase__="<s>",						lowerCamelCase__="</s>",						lowerCamelCase__="</s>",						lowerCamelCase__="<s>",						lowerCamelCase__="<unk>",						lowerCamelCase__="<pad>",						lowerCamelCase__="<mask>",						lowerCamelCase__=False,						lowerCamelCase__=True,						**lowerCamelCase__,						):
        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					:			List[Any]		=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("""add_prefix_space""",						__A						) != add_prefix_space:
            A					:			int		=  getattr(__A,						pre_tok_state.pop("""type"""						)						)
            A					:			Optional[Any]		=  add_prefix_space
            A					:			Optional[int]		=  pre_tok_class(**__A						)

        A					:			Optional[Any]		=  add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A					:			Optional[int]		=  """post_processor"""
        A					:			Tuple		=  getattr(self.backend_tokenizer,						__A,						__A						)
        if tokenizer_component_instance:
            A					:			Tuple		=  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					:			Dict		=  tuple(state["""sep"""]						)
            if "cls" in state:
                A					:			Optional[Any]		=  tuple(state["""cls"""]						)

            A					:			Optional[Any]		=  False

            if state.get("""add_prefix_space""",						__A						) != add_prefix_space:
                A					:			Tuple		=  add_prefix_space
                A					:			Union[str, Any]		=  True

            if state.get("""trim_offsets""",						__A						) != trim_offsets:
                A					:			int		=  trim_offsets
                A					:			Optional[Any]		=  True

            if changes_to_apply:
                A					:			Union[str, Any]		=  getattr(__A,						state.pop("""type"""						)						)
                A					:			Union[str, Any]		=  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      _lowerCAmelCase   (			self						):
        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,						lowerCamelCase__						):
        A					:			Optional[Any]		=  AddedToken(__A,						lstrip=__A,						rstrip=__A						) if isinstance(__A,						__A						) else value
        A					:			List[Any]		=  value






    def      _lowerCAmelCase   (			self,						*lowerCamelCase__,						**lowerCamelCase__						):
        A					:			Any		=  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      _lowerCAmelCase   (			self,						*lowerCamelCase__,						**lowerCamelCase__						):
        A					:			Tuple		=  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      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__ = None						):
        A					:			List[Any]		=  self._tokenizer.model.save(__A,						name=__A						)
        return tuple(__A						)






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__=None						):
        A					:			Optional[int]		=  [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,						lowerCamelCase__,						lowerCamelCase__ = None						):
        A					:			Dict		=  [self.sep_token_id]
        A					:			List[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,						lowerCamelCase__,						lowerCamelCase__ = None,						lowerCamelCase__ = PaddingStrategy.DO_NOT_PAD,						lowerCamelCase__ = None,						lowerCamelCase__ = None,						):
        A					:			Optional[Any]		=  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					:			Dict		=  """attention_mask""" in self.model_input_names

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

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

        return encoded_inputs





from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





