

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_        =			{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''LlamaTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''LlamaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			[
           '''LlamaForCausalLM''',
           '''LlamaModel''',
           '''LlamaPreTrainedModel''',
           '''LlamaForSequenceClassification''',
       ]


if TYPE_CHECKING:
       from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_llama import LlamaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_llama_fast import LlamaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
       import sys

       snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




from __future__ import annotations


class 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self						,       a						,       a):
         lowercase__				,			lowercase__   :       Dict				=	text, pattern
         lowercase__				,			lowercase__   :       Any				=	len(a), len(a)





    def 				snake_case_    (				self						,       a):

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





    def 				snake_case_    (				self						,       a):

         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 				snake_case_    (				self):
         # searches pattern in text and returns index positions
         lowercase__   :       Any				=	[]
         for i in range(self.textLen - self.patLen + 1):
              lowercase__   :       Optional[Any]				=	self.mismatch_in_text(a)
              if mismatch_index == -1:
                   positions.append(a)
              else:
                   lowercase__   :       Optional[int]				=	self.match_in_pattern(self.text[mismatch_index])
                   lowercase__   :       Optional[Any]				=	(
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions



snake_case_        =			'''ABAABA'''
snake_case_        =			'''AB'''
snake_case_        =			BoyerMooreSearch(text, pattern)
snake_case_        =			bms.bad_character_heuristic()

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

