

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"""





    lowerCamelCase			=							42
    lowerCamelCase			=							field(default_factory=_UpperCAmelCase )
    lowerCamelCase			=							field(default_factory=_UpperCAmelCase )


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Tensor,lowercase_ :       Tensor		)->    List[Any]:





        '''simple docstring'''







        A__          =   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,lowercase_ :       Tensor		)->    Tuple:





        '''simple docstring'''







        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		)->    Optional[Any]:





        '''simple docstring'''







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




@dataclass
class  A						:







    """simple docstring"""





    lowerCamelCase			=							42
    lowerCamelCase			=							42
    lowerCamelCase			=							0
    lowerCamelCase			=							field(default_factory=_UpperCAmelCase )
    lowerCamelCase			=							field(default_factory=_UpperCAmelCase )


    def __call__(      self :       List[str],lowercase_ :       Tensor		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   Tracker(self.dest		)(lowercase_		).parametrized
        A__          =   Tracker(self.src		)(lowercase_		).parametrized

        A__          =   list(filter(lambda lowercase_		: type(lowercase_		) not in self.src_skip,lowercase_		)		)
        A__          =   list(filter(lambda lowercase_		: 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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	ResNetConfig    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	bool = True      )							->      int:





    '''simple docstring'''






    print(f'Converting {name}...'      )
    with torch.no_grad():
        A__          =   timm.create_model(SCREAMING_SNAKE_CASE__    ,			pretrained=SCREAMING_SNAKE_CASE__      ).eval()
        A__          =   ResNetForImageClassification(SCREAMING_SNAKE_CASE__      ).eval()
        A__          =   ModuleTransfer(src=SCREAMING_SNAKE_CASE__    ,			dest=SCREAMING_SNAKE_CASE__      )
        A__          =   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."

    A__          =   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
        A__          =   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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	str = None    ,			SCREAMING_SNAKE_CASE__			:	bool = True      )							->      Dict:





    '''simple docstring'''






    A__          =   'imagenet-1k-id2label.json'
    A__          =   1000
    A__          =   (1, num_labels)

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

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

    A__          =   partial(SCREAMING_SNAKE_CASE__    ,			num_labels=SCREAMING_SNAKE_CASE__    ,			idalabel=SCREAMING_SNAKE_CASE__    ,			labelaid=SCREAMING_SNAKE_CASE__      )

    A__          =   {
        '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__":
     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)




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

