





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_												=     {
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_												=     ["EfficientNetImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_												=     [
						    "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "EfficientNetForImageClassification",
						    "EfficientNetModel",
						    "EfficientNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_efficientnet import (
						    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    EfficientNetConfig,
						    EfficientNetOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_efficientnet import EfficientNetImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_efficientnet import (
												    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EfficientNetForImageClassification,
												    EfficientNetModel,
												    EfficientNetPreTrainedModel,
												)

else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure)








from __future__ import annotations

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       text, pattern
										lowerCamelCase__    ,		lowerCamelCase__       =       len(_lowerCAmelCase	), len(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):

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

A_												=     "ABAABA"
A_												=     "AB"
A_												=     BoyerMooreSearch(text, pattern)
A_												=     bms.bad_character_heuristic()

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

