





'''simple docstring'''

from collections import defaultdict


class 					a_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			total  # total no of tasks (N)

        # DP table will have a dimension of (2^M)*N
        # initially all values are set to -1
        _SCREAMING_SNAKE_CASE									=			[
            [-1 for i in range(total + 1       )] for j in range(2 ** len(A       )       )
        ]

        _SCREAMING_SNAKE_CASE									=			defaultdict(A       )  # stores the list of persons for each task

        # final_mask is used to check if all persons are included by setting all bits
        # to 1
        _SCREAMING_SNAKE_CASE									=			(1 << len(A       )) - 1


    def 			snake_case_(		self ,		A ,		A       )		->   List[str]:
        # if mask == self.finalmask all persons are distributed tasks, return 1
        if mask == self.final_mask:
            return 1

        # if not everyone gets the task and no more tasks are available, return 0
        if task_no > self.total_tasks:
            return 0

        # if case already considered
        if self.dp[mask][task_no] != -1:
            return self.dp[mask][task_no]

        # Number of ways when we don't this task in the arrangement
        _SCREAMING_SNAKE_CASE									=			self.count_ways_until(A ,		task_no + 1       )

        # now assign the tasks one by one to all possible persons and recursively
        # assign for the remaining tasks.
        if task_no in self.task:
            for p in self.task[task_no]:
                # if p is already given a task
                if mask & (1 << p):
                    continue

                # assign this task to p and change the mask value. And recursively
                # assign tasks with the new mask value.
                total_ways_util += self.count_ways_until(mask | (1 << p) ,		task_no + 1       )

        # save the value.
        _SCREAMING_SNAKE_CASE									=			total_ways_util

        return self.dp[mask][task_no]





    def 			snake_case_(		self ,		A       )		->   int:
        # Store the list of persons for each task
        for i in range(len(A       )       ):
            for j in task_performed[i]:
                self.task[j].append(A       )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
        return self.count_ways_until(0 ,		1       )


if __name__ == "__main__":
      lowercase_					= 5  # total no of tasks (the value of N)

      # the list of tasks that can be done by M persons.
      lowercase_					= [[1, 3, 4], [1, 2, 5], [3, 4]]
      print(
          AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
              task_performed
          )
      )










'''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()
lowercase_					= logging.get_logger()


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			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       )		->   str:
        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       )		->   str:
        # 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 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 0
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def __call__(		self ,		A       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			Tracker(self.dest       )(A       ).parametrized
        _SCREAMING_SNAKE_CASE									=			Tracker(self.src       )(A       ).parametrized

        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.src_skip ,		A       )       )
        _SCREAMING_SNAKE_CASE									=			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						(     __lowerCamelCase :    str					,       __lowerCamelCase :    ResNetConfig					,       __lowerCamelCase :    Path					,       __lowerCamelCase :    bool = True    )						->int:
    print(F'Converting {name}...'    )
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE									=			timm.create_model(__lowerCamelCase					,       pretrained=__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ResNetForImageClassification(__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ModuleTransfer(src=__lowerCamelCase					,       dest=__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			torch.randn((1, 3, 224, 224)    )
        module_transfer(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			F'resnet{"-".join(name.split("resnet"    )    )}'
    print(__lowerCamelCase    )

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

        # we can use the convnext one
        _SCREAMING_SNAKE_CASE									=			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=__lowerCamelCase					,       )

        print(F'Pushed {checkpoint_name}'    )





def lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    str = None					,       __lowerCamelCase :    bool = True    )						->Any:
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			1000
    _SCREAMING_SNAKE_CASE									=			(1, num_labels)

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

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

    _SCREAMING_SNAKE_CASE									=			partial(__lowerCamelCase					,       num_labels=__lowerCamelCase					,       idalabel=__lowerCamelCase					,       labelaid=__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			{
        """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(__lowerCamelCase					,       names_to_config[model_name]					,       __lowerCamelCase					,       __lowerCamelCase    )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    return config, expected_shape


if __name__ == "__main__":
      lowercase_					= 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.""",
      )

      lowercase_					= parser.parse_args()
      lowercase_					= 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)



