






'''simple docstring'''

from __future__ import annotations






class     a_						:

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase      = text, pattern
        UpperCamelCase  ,UpperCamelCase      = len(_SCREAMING_SNAKE_CASE				), len(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				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				)  ->				list[int]:



        """simple docstring"""






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


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

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







'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger()






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = len(list(m.modules()				)				) == 1 or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.Convad				) or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.BatchNormad				)
        if has_not_submodules:
              self.traced.append(_SCREAMING_SNAKE_CASE				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook				)				)
        self.module(_SCREAMING_SNAKE_CASE				)
        [x.remove() for x in self.handles]
        return self


  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return list(filter(lambda _SCREAMING_SNAKE_CASE				: len(list(x.state_dict().keys()				)				) > 0      ,		self.traced				)				)






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42
  lowercase							=				0
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
        UpperCamelCase      = Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.src_skip      ,		_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.dest_skip      ,		_SCREAMING_SNAKE_CASE				)				)

        if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
              raise Exception(
                  F"Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE				)} operations while"
                  F" destination module has {len(_SCREAMING_SNAKE_CASE				)}."				)

        for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              dest_m.load_state_dict(src_m.state_dict()				)
              if self.verbose == 1:
                    print(F"Transfered from={src_m} to={dest_m}"				)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = True   )->			Optional[Any]:
      print(F"Converting {name}..."   )
      with torch.no_grad():
            UpperCamelCase      = timm.create_model(__UpperCamelCase		,						pretrained=__UpperCamelCase   ).eval()
            UpperCamelCase      = ResNetForImageClassification(__UpperCamelCase   ).eval()
            UpperCamelCase      = ModuleTransfer(src=__UpperCamelCase		,						dest=__UpperCamelCase   )
            UpperCamelCase      = torch.randn((1, 3, 224, 224)   )
            module_transfer(__UpperCamelCase   )

      assert torch.allclose(from_model(__UpperCamelCase   )		,						our_model(__UpperCamelCase   ).logits   ), "The model logits don't match the original one."

      UpperCamelCase      = F"resnet{'-'.join(name.split('resnet'   )   )}"
      print(__UpperCamelCase   )

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name		,						commit_message="""Add model"""		,						use_temp_dir=__UpperCamelCase		,						)

            # we can use the convnext one
            UpperCamelCase      = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name		,						commit_message="""Add image processor"""		,						use_temp_dir=__UpperCamelCase		,						)

            print(F"Pushed {checkpoint_name}"   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = None		,						__UpperCamelCase = True   )->			str:
      UpperCamelCase      = """imagenet-1k-id2label.json"""
      UpperCamelCase      = 1000
      UpperCamelCase      = (1, num_labels)

      UpperCamelCase      = """huggingface/label-files"""
      UpperCamelCase      = num_labels
      UpperCamelCase      = json.load(open(hf_hub_download(__UpperCamelCase		,						__UpperCamelCase		,						repo_type="""dataset"""   )		,						"""r"""   )   )
      UpperCamelCase      = {int(__UpperCamelCase   ): v for k, v in idalabel.items()}

      UpperCamelCase      = idalabel
      UpperCamelCase      = {v: k for k, v in idalabel.items()}

      UpperCamelCase      = partial(__UpperCamelCase		,						num_labels=__UpperCamelCase		,						idalabel=__UpperCamelCase		,						labelaid=__UpperCamelCase   )

      UpperCamelCase      = {
          """resnet18""": ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type="""basic"""   ),
          """resnet26""": ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet34""": ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type="""basic"""   ),
          """resnet50""": ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet101""": ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet152""": ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
      }

      if model_name:
            convert_weight_and_push(__UpperCamelCase		,						names_to_config[model_name]		,						__UpperCamelCase		,						__UpperCamelCase   )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
      return config, expected_shape


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default=None,
         type=str,
         help=(
             'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
             ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         type=Path,
         required=True,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument(
         '--push_to_hub',
         default=True,
         type=bool,
         required=False,
         help='If True, push model and image processor to the hub.',
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     SCREAMING_SNAKE_CASE__     =       args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
