



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     				=						logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE     				=						{
    "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     				=						{
    "allenai/led-base-16384": 16384,
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[int]									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    List[str]									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    str									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    str									=  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__(
							    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    A__		(			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    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				AddedToken(lowerCAmelCase    ,	lstrip=lowerCAmelCase    ,	rstrip=lowerCAmelCase     ) if isinstance(lowerCAmelCase    ,	lowerCAmelCase     ) else value
							UpperCAmelCase_						=				value



					def    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							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    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = 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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = PaddingStrategy.DO_NOT_PAD    ,	lowerCAmelCase = None    ,	lowerCAmelCase = 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






from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 768    ,	):
							super().__init__()

							UpperCAmelCase_						=				nn.Parameter(torch.zeros(1    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(1    ,	lowerCAmelCase     )     )



					def    A__		(			self    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	):
							UpperCAmelCase_						=				nn.Parameter(self.mean.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(self.std.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							return self



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds - self.mean) * 1.0 / self.std
							return embeds



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds * self.std) + self.mean
							return embeds

