


"""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_longformer import LongformerTokenizer


lowercase  		=			logging.get_logger(__name__)

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

lowercase  		=			{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase  		=			{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





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




							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__	,					)   ->	List[str]:







								'''simple docstring'''




								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__	,					)

								snake_case__  :						Tuple		=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
								if pre_tok_state.get("add_prefix_space"	,					lowerCamelCase__) != add_prefix_space:
									snake_case__  :						Optional[Any]		=      getattr(lowerCamelCase__	,					pre_tok_state.pop("type"))
									snake_case__  :						Tuple		=      add_prefix_space
									snake_case__  :						Any		=      pre_tok_class(**lowerCamelCase__)

								snake_case__  :						Tuple		=      add_prefix_space

								snake_case__  :						Any		=      "post_processor"
								snake_case__  :						List[Any]		=      getattr(self.backend_tokenizer	,					lowerCamelCase__	,					lowerCamelCase__)
								if tokenizer_component_instance:
									snake_case__  :						List[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:
										snake_case__  :						int		=      tuple(state["sep"])
									if "cls" in state:
										snake_case__  :						str		=      tuple(state["cls"])

									snake_case__  :						Union[str, Any]		=      False

									if state.get("add_prefix_space"	,					lowerCamelCase__) != add_prefix_space:
										snake_case__  :						List[str]		=      add_prefix_space
										snake_case__  :						str		=      True

									if state.get("trim_offsets"	,					lowerCamelCase__) != trim_offsets:
										snake_case__  :						Union[str, Any]		=      trim_offsets
										snake_case__  :						int		=      True

									if changes_to_apply:
										snake_case__  :						Union[str, Any]		=      getattr(lowerCamelCase__	,					state.pop("type"))
										snake_case__  :						List[str]		=      component_class(**lowerCamelCase__)
										setattr(self.backend_tokenizer	,					lowerCamelCase__	,					lowerCamelCase__)




							@property
							def        UpperCAmelCase (						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        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      AddedToken(lowerCamelCase__	,					lstrip=lowerCamelCase__	,					rstrip=lowerCamelCase__) if isinstance(lowerCamelCase__	,					lowerCamelCase__) else value
								snake_case__  :						Tuple		=      value




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	BatchEncoding:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      kwargs.get("is_split_into_words"	,					lowerCamelCase__)
								assert self.add_prefix_space or not is_split_into_words, (
								    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        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	BatchEncoding:







								'''simple docstring'''




								snake_case__  :						str		=      kwargs.get("is_split_into_words"	,					lowerCamelCase__)

								assert self.add_prefix_space or not is_split_into_words, (
								    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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	Tuple[str]:







								'''simple docstring'''




								snake_case__  :						str		=      self._tokenizer.model.save(lowerCamelCase__	,					name=lowerCamelCase__)
								return tuple(lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      [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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	List[int]:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      [self.sep_token_id]
								snake_case__  :						List[str]		=      [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]











import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
