
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


_a:    Tuple 							=			logging.get_logger(__name__)


_a:    List[str] 							=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_a:    str 							=			{
    """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""",
    },
}

_a:    Any 							=			{
    """allenai/led-base-16384""": 1_6384,
}

class 						__UpperCamelCase    (					lowercase__						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__    				=     LEDTokenizer
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						Any=None		,	lowerCAmelCase	:						Optional[Any]=None		,	lowerCAmelCase	:						str="replace"		,	lowerCAmelCase	:						Dict="<s>"		,	lowerCAmelCase	:						List[str]="</s>"		,	lowerCAmelCase	:						List[str]="</s>"		,	lowerCAmelCase	:						Tuple="<s>"		,	lowerCAmelCase	:						Any="<unk>"		,	lowerCAmelCase	:						int="<pad>"		,	lowerCAmelCase	:						str="<mask>"		,	lowerCAmelCase	:						Optional[int]=False		,	lowerCAmelCase	:						Optional[int]=True		,	**lowerCAmelCase	:						Dict		,	):


		'''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		,	)

		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	:						Tuple							):


		'''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       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else value
		UpperCAmelCase_       				=			value




	def       __A					(		self	:						Any		,	*lowerCAmelCase	:						int		,	**lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		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	:						Dict		,	*lowerCAmelCase	:						Dict		,	**lowerCAmelCase	:						int							):


		'''simple docstring'''







		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	:						Any		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						int = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self._tokenizer.model.save(lowerCAmelCase		,	name=lowerCAmelCase							)
		return tuple(lowerCAmelCase							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						List[Any]=None							):


		'''simple docstring'''







		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	:						Tuple		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[int] = None							):


		'''simple docstring'''







		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	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						List[str] = None		,	lowerCAmelCase	:						Optional[Any] = PaddingStrategy.DO_NOT_PAD		,	lowerCAmelCase	:						Optional[int] = None		,	lowerCAmelCase	:						int = None		,	):


		'''simple docstring'''







		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 __future__ import annotations

class 						__UpperCamelCase    :




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			text, pattern
		UpperCAmelCase_				,   UpperCAmelCase_       				=			len(lowerCAmelCase							), len(lowerCAmelCase							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if char == self.pattern[i]:
				return i
		return -1




	def       __A					(		self	:						int		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if self.pattern[i] != self.text[current_pos + i]:
				return current_pos + i
		return -1




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for i in range(self.textLen - self.patLen + 1							):
			UpperCAmelCase_       				=			self.mismatch_in_text(lowerCAmelCase							)
			if mismatch_index == -1:
				positions.append(lowerCAmelCase							)
			else:
				UpperCAmelCase_       				=			self.match_in_pattern(self.text[mismatch_index]							)
				UpperCAmelCase_       				=			(
				    mismatch_index - match_index
				)  # shifting index lgtm [py/multiple-definition]
		return positions


_a:    Dict 							=			"""ABAABA"""
_a:    Union[str, Any] 							=			"""AB"""
_a:    str 							=			BoyerMooreSearch(text, pattern)
_a:    Dict 							=			bms.bad_character_heuristic()

if len(positions) == 0:
				print("""No match found""")
else:
				print("""Pattern found in following positions: """)
				print(positions)