


"""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()
UpperCAmelCase   		=      logging.get_logger()
@dataclass
class        __magic_name__		:
       __A      :   Any   	= 42
       __A      :   Optional[int]   	= field(default_factory=a_  )
       __A      :   List[str]   	= field(default_factory=a_  )






       def 			__snake_case    ( self	:     List[Any]	,  snake_case__	:     Dict	,  snake_case__	:     Union[str, Any]	,  snake_case__	:     Optional[int]					):

              '''simple docstring'''






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






       def __call__( self	:     Optional[Any]	,  snake_case__	:     Optional[int]					):

              '''simple docstring'''






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







       @property
       def 			__snake_case    ( self	:     Optional[int]					):

              '''simple docstring'''






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







@dataclass
class        __magic_name__		:
       __A      :   Union[str, Any]   	= 42
       __A      :   Dict   	= 42
       __A      :   int   	= 0
       __A      :   int   	= field(default_factory=a_  )
       __A      :   str   	= field(default_factory=a_  )






       def __call__( self	:     List[str]	,  snake_case__	:     Dict					):

              '''simple docstring'''






              lowercase		:Union[str, Any]											=					Tracker(self.dest					)(__UpperCamelCase					).parametrized
              lowercase		:Any											=					Tracker(self.src					)(__UpperCamelCase					).parametrized

              lowercase		:Optional[int]											=					list(filter(lambda snake_case__					: type(__UpperCamelCase					) not in self.src_skip	,  __UpperCamelCase					)					)
              lowercase		:List[Any]											=					list(filter(lambda snake_case__					: type(__UpperCamelCase					) not in self.dest_skip	,  __UpperCamelCase					)					)

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

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







def    lowerCamelCase       (a_    :Dict					,					a_    :List[str]					,					a_    :str					,					a_    :Any = True)  ->				Optional[int]:
       print(F"""Converting {name}...""")
       with torch.no_grad():
              lowercase		:List[str]											=					timm.create_model(__snake_case					,					pretrained=__snake_case).eval()
              lowercase		:Optional[int]											=					ResNetForImageClassification(__snake_case).eval()
              lowercase		:Optional[Any]											=					ModuleTransfer(src=__snake_case					,					dest=__snake_case)
              lowercase		:Any											=					torch.randn((1, 3, 224, 224))
              module_transfer(__snake_case)

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

       lowercase		:List[Any]											=					F"""resnet{'-'.join(name.split('resnet'))}"""
       print(__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=__snake_case					,					)

              # we can use the convnext one
              lowercase		:str											=					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=__snake_case					,					)

              print(F"""Pushed {checkpoint_name}""")






def    lowerCamelCase       (a_    :Dict					,					a_    :Union[str, Any] = None					,					a_    :Tuple = True)  ->				Union[str, Any]:
       lowercase		:Tuple											=					'''imagenet-1k-id2label.json'''
       lowercase		:List[Any]											=					1000
       lowercase		:List[Any]											=					(1, num_labels)

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

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

       lowercase		:Any											=					partial(__snake_case					,					num_labels=__snake_case					,					idalabel=__snake_case					,					labelaid=__snake_case)

       lowercase		:str											=					{
           '''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(__snake_case					,					names_to_config[model_name]					,					__snake_case					,					__snake_case)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(__snake_case					,					__snake_case					,					__snake_case					,					__snake_case)
       return config, expected_shape


if __name__ == "__main__":
 UpperCAmelCase   		=      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.''',
 )

 UpperCAmelCase   		=      parser.parse_args()
 UpperCAmelCase   		=      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)









# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase      :       Dict    =						abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_terminal_summary_main

     __lowerCAmelCase             =       terminalreporter.config.getoption("--make-reports"							)
     if make_reports:
          pytest_terminal_summary_main(__snake_case       ,			id=__snake_case							)






