
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()
__lowerCAmelCase    :	List[Any]						=						logging.get_logger()


@dataclass
class   __lowerCAmelCase		:



      """simple docstring"""







      A__		:	int         =			4_2
      A__		:	Tuple         =			field(default_factory=_UpperCAmelCase					)
      A__		:	str         =			field(default_factory=_UpperCAmelCase					)




      def 						snake_case_	(					self						:				Union[str, Any] ,				_snake_case						:				Any ,				_snake_case						:				Tensor ,				_snake_case						:				Tensor ):
          __lowercase						:	int					=				len(list(m.modules() ) ) == 1 or isinstance(lowercase_ ,				nn.Convad ) or isinstance(lowercase_ ,				nn.BatchNormad )
          if has_not_submodules:
              self.traced.append(lowercase_ )




      def __call__(					self						:				Dict ,				_snake_case						:				Tensor ):







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




      @property
      def 						snake_case_	(					self						:				str ):
          return list(filter(lambda _snake_case : len(list(x.state_dict().keys() ) ) > 0 ,				self.traced ) )






@dataclass
class   __lowerCAmelCase		:



      """simple docstring"""







      A__		:	List[str]         =			4_2
      A__		:	Any         =			4_2
      A__		:	Any         =			0
      A__		:	Optional[Any]         =			field(default_factory=_UpperCAmelCase					)
      A__		:	Union[str, Any]         =			field(default_factory=_UpperCAmelCase					)




      def __call__(					self						:				List[str] ,				_snake_case						:				Tensor ):
          __lowercase						:	List[str]					=				Tracker(self.dest )(lowercase_ ).parametrized
          __lowercase						:	Optional[Any]					=				Tracker(self.src )(lowercase_ ).parametrized

          __lowercase						:	List[Any]					=				list(filter(lambda _snake_case : type(lowercase_ ) not in self.src_skip ,				lowercase_ ) )
          __lowercase						:	Union[str, Any]					=				list(filter(lambda _snake_case : type(lowercase_ ) not in self.dest_skip ,				lowercase_ ) )

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

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






def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = True		)       ->  int:
    print(F'Converting {name}...'		)
    with torch.no_grad():
        __lowercase						:	List[str]					=				timm.create_model(SCREAMING_SNAKE_CASE__		,    pretrained=SCREAMING_SNAKE_CASE__		).eval()
        __lowercase						:	Dict					=				ResNetForImageClassification(SCREAMING_SNAKE_CASE__		).eval()
        __lowercase						:	List[Any]					=				ModuleTransfer(src=SCREAMING_SNAKE_CASE__		,    dest=SCREAMING_SNAKE_CASE__		)
        __lowercase						:	List[Any]					=				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."

    __lowercase						:	Any					=				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
        __lowercase						:	Optional[int]					=				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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase = None		,    __lowerCAmelCase = True		)       ->  Dict:
    __lowercase						:	List[str]					=				'''imagenet-1k-id2label.json'''
    __lowercase						:	str					=				1_000
    __lowercase						:	Optional[Any]					=				(1, num_labels)

    __lowercase						:	List[str]					=				'''huggingface/label-files'''
    __lowercase						:	int					=				num_labels
    __lowercase						:	List[Any]					=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    repo_type='''dataset'''		)		,    '''r'''		)		)
    __lowercase						:	Any					=				{int(SCREAMING_SNAKE_CASE__		): v for k, v in idalabel.items()}

    __lowercase						:	Dict					=				idalabel
    __lowercase						:	List[str]					=				{v: k for k, v in idalabel.items()}

    __lowercase						:	Optional[int]					=				partial(SCREAMING_SNAKE_CASE__		,    num_labels=SCREAMING_SNAKE_CASE__		,    idalabel=SCREAMING_SNAKE_CASE__		,    labelaid=SCREAMING_SNAKE_CASE__		)

    __lowercase						:	Optional[Any]					=				{
        '''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, 1_024, 2_048]		,    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, 1_024, 2_048]		,    layer_type='''bottleneck'''		),
        '''resnet101''': ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]		,    hidden_sizes=[256, 512, 1_024, 2_048]		,    layer_type='''bottleneck'''		),
        '''resnet152''': ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]		,    hidden_sizes=[256, 512, 1_024, 2_048]		,    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__":
 __lowerCAmelCase    :	Any						=						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.",
 )

 __lowerCAmelCase    :	Any						=						parser.parse_args()
 __lowerCAmelCase    :	Any						=						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)





def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

