

'''simple docstring'''




from collections.abc import Generator
from math import sin


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : bytes    ):
  if len(SCREAMING_SNAKE_CASE    ) != 32:
    raise ValueError('Input must be of length 32'    )

  UpperCAmelCase    				=						B''
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int    ):
  if i < 0:
    raise ValueError('Input must be non-negative'    )

  UpperCAmelCase    				=						format(SCREAMING_SNAKE_CASE							, '08x'    )[-8:]
  UpperCAmelCase    				=						B''
  for i in [3, 2, 1, 0]:
    little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'    )
  return little_endian_hex


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : bytes    ):
  UpperCAmelCase    				=						B''
  for char in message:
    bit_string += format(SCREAMING_SNAKE_CASE							, '08b'    ).encode('utf-8'    )
  UpperCAmelCase    				=						format(len(SCREAMING_SNAKE_CASE    )							, '064b'    ).encode('utf-8'    )

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(SCREAMING_SNAKE_CASE    ) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]    ) + to_little_endian(start_len[:32]    )

  return bit_string


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : bytes    ):
  if len(SCREAMING_SNAKE_CASE    ) % 512 != 0:
    raise ValueError('Input must have length that\'s a multiple of 512'    )

  for pos in range(0							, len(SCREAMING_SNAKE_CASE    )							, 512    ):
    UpperCAmelCase    				=						bit_string[pos : pos + 512]
    UpperCAmelCase    				=						[]
    for i in range(0							, 512							, 32    ):
      block_words.append(int(to_little_endian(block[i : i + 32]    )							, 2    )    )
    yield block_words


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int    ):
  if i < 0:
    raise ValueError('Input must be non-negative'    )

  UpperCAmelCase    				=						format(SCREAMING_SNAKE_CASE							, '032b'    )
  UpperCAmelCase    				=						''
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(SCREAMING_SNAKE_CASE							, 2    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : int    ):
  return (a + b) % 2**32


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : int    ):
  if i < 0:
    raise ValueError('Input must be non-negative'    )
  if shift < 0:
    raise ValueError('Shift must be non-negative'    )
  return ((i << shift) ^ (i >> (32 - shift))) % 2**32


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : bytes    ):
  UpperCAmelCase    				=						preprocess(SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						[int(2**32 * abs(sin(i + 1    )    )    ) for i in range(64    )]

  # Starting states
  UpperCAmelCase    				=						0X67_452_301
  UpperCAmelCase    				=						0XEF_CDA_B89
  UpperCAmelCase    				=						0X98_BAD_CFE
  UpperCAmelCase    				=						0X10_325_476

  UpperCAmelCase    				=						[
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
  ]

  # Process bit string in chunks, each with 16 32-char words
  for block_words in get_block_words(SCREAMING_SNAKE_CASE    ):
    UpperCAmelCase    				=						aa
    UpperCAmelCase    				=						ba
    UpperCAmelCase    				=						ca
    UpperCAmelCase    				=						da

    # Hash current chunk
    for i in range(64    ):
      if i <= 15:
        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
        UpperCAmelCase    				=						d ^ (b & (c ^ d))
        UpperCAmelCase    				=						i
      elif i <= 31:
        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
        UpperCAmelCase    				=						c ^ (d & (b ^ c))
        UpperCAmelCase    				=						(5 * i + 1) % 16
      elif i <= 47:
        UpperCAmelCase    				=						b ^ c ^ d
        UpperCAmelCase    				=						(3 * i + 5) % 16
      else:
        UpperCAmelCase    				=						c ^ (b | not_aa(SCREAMING_SNAKE_CASE    ))
        UpperCAmelCase    				=						(7 * i) % 16
      UpperCAmelCase    				=						(f + a + added_consts[i] + block_words[g]) % 2**32
      UpperCAmelCase    				=						d
      UpperCAmelCase    				=						c
      UpperCAmelCase    				=						b
      UpperCAmelCase    				=						sum_aa(SCREAMING_SNAKE_CASE							, left_rotate_aa(SCREAMING_SNAKE_CASE							, shift_amounts[i]    )    )

    # Add hashed chunk to running total
    UpperCAmelCase    				=						sum_aa(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						sum_aa(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						sum_aa(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						sum_aa(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						reformat_hex(SCREAMING_SNAKE_CASE    ) + reformat_hex(SCREAMING_SNAKE_CASE    ) + reformat_hex(SCREAMING_SNAKE_CASE    ) + reformat_hex(SCREAMING_SNAKE_CASE    )
  return digest


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''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()
_a					:			List[Any]							=    logging.get_logger()



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			List[nn.Module]     	= field(default_factory=a			)
  __lowerCAmelCase       :			list     	= field(default_factory=a			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						len(list(m.modules()  )  ) == 1 or isinstance(a_ ,			nn.Convad  ) or isinstance(a_ ,			nn.BatchNormad  )
    if has_not_submodules:
      self.traced.append(a_  )
  def __call__(  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook  )  )
    self.module(a_  )
    [x.remove() for x in self.handles]
    return self





  @property
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    # check the len of the state_dict keys to see if we have learnable params
    return list(filter(lambda a_  : len(list(x.state_dict().keys()  )  ) > 0 ,			self.traced  )  )




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			int     	= 0
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  def __call__(  self ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						Tracker(self.dest  )(a_  ).parametrized
    UpperCAmelCase    				=						Tracker(self.src  )(a_  ).parametrized

    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.src_skip ,			a_  )  )
    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.dest_skip ,			a_  )  )

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

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




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : ResNetConfig							, SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : bool = True    ):
  print(f'''Converting {name}...'''    )
  with torch.no_grad():
    UpperCAmelCase    				=						timm.create_model(SCREAMING_SNAKE_CASE							, pretrained=SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ResNetForImageClassification(SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ModuleTransfer(src=SCREAMING_SNAKE_CASE							, dest=SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						torch.randn((1, 3, 224, 224)    )
    module_transfer(SCREAMING_SNAKE_CASE    )

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

  UpperCAmelCase    				=						f'''resnet{'-'.join(name.split('resnet'    )    )}'''
  print(SCREAMING_SNAKE_CASE    )

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

    # 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=SCREAMING_SNAKE_CASE							, )

    print(f'''Pushed {checkpoint_name}'''    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : str = None							, SCREAMING_SNAKE_CASE   : bool = True    ):
  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(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, repo_type='dataset'    )							, 'r'    )    )
  UpperCAmelCase    				=						{int(SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}

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

  UpperCAmelCase    				=						partial(SCREAMING_SNAKE_CASE							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid=SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
  _a					:			List[str]							=    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.',
  )

  _a					:			Tuple							=    parser.parse_args()
  _a					:			Path							=    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)


