
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


_a:    Optional[int] 							=			logging.get_logger(__name__)

_a:    Optional[int] 							=			{"""tokenizer_file""": """tokenizer.json"""}

_a:    Tuple 							=			{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}

class 						__UpperCamelCase    (					snake_case_						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     ["""input_ids""", """attention_mask"""]
	SCREAMING_SNAKE_CASE__    				=     None




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


		'''simple docstring'''







		super().__init__(
		    lowerCAmelCase		,	lowerCAmelCase		,	tokenizer_file=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	bos_token=lowerCAmelCase		,	eos_token=lowerCAmelCase		,	pad_token=lowerCAmelCase		,	add_prefix_space=lowerCAmelCase		,	clean_up_tokenization_spaces=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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			kwargs.get("is_split_into_words"		,	lowerCAmelCase							)
		if not (self.add_prefix_space or not is_split_into_words):
			raise Exception(
			    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	:						Union[str, Any]		,	*lowerCAmelCase	:						Optional[int]		,	**lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			kwargs.get("is_split_into_words"		,	lowerCAmelCase							)

		if not (self.add_prefix_space or not is_split_into_words):
			raise Exception(
			    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	:						Tuple		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[Any] = None							):


		'''simple docstring'''







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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							) + [self.eos_token_id]							)

		if len(lowerCAmelCase							) > self.model_max_length:
			UpperCAmelCase_       				=			input_ids[-self.model_max_length :]
		return input_ids


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)