






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[str]										=   logging.get_logger()







@dataclass
class  _lowercase   :
      _a							:						nn.Module
      _a							:						List[nn.Module]										=							field(default_factory=_A						)
      _a							:						list										=							field(default_factory=_A						)
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Optional[Any]							       =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   ):
       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 						lowercase__					(     self   ):
       # 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   :
      _a							:						nn.Module
      _a							:						nn.Module
      _a							:						int										=							0
      _a							:						List										=							field(default_factory=_A						)
      _a							:						List										=							field(default_factory=_A						)
      def __call__(     self				,			a   ):
       snake_case__  :    Any							       =Tracker(self.dest   )(a   ).parametrized
       snake_case__  :    Any							       =Tracker(self.src   )(a   ).parametrized

       snake_case__  :    Optional[int]							       =list(filter(lambda a   : type(a   ) not in self.src_skip				,			a   )   )
       snake_case__  :    str							       =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 			A__							(      _a  :					str		,  _a  :					ResNetConfig		,  _a  :					Path		,  _a  :					bool = True	):





 '''simple docstring'''







 print(f"Converting {name}..."	)
 with torch.no_grad():
  snake_case__  :    Any							       =timm.create_model(_a		,  pretrained=_a	).eval()
  snake_case__  :    List[str]							       =ResNetForImageClassification(_a	).eval()
  snake_case__  :    List[Any]							       =ModuleTransfer(src=_a		,  dest=_a	)
  snake_case__  :    List[str]							       =torch.randn((1, 3, 224, 224)	)
  module_transfer(_a	)

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

 snake_case__  :    Tuple							       =f"resnet{'-'.join(name.split('resnet'	)	)}"
 print(_a	)

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

  # we can use the convnext one
  snake_case__  :    Optional[Any]							       =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=_a		,  )

  print(f"Pushed {checkpoint_name}"	)







def 			A__							(      _a  :					Path		,  _a  :					str = None		,  _a  :					bool = True	):





 '''simple docstring'''







 snake_case__  :    List[Any]							       ="""imagenet-1k-id2label.json"""
 snake_case__  :    Dict							       =1000
 snake_case__  :    int							       =(1, num_labels)

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

 snake_case__  :    Any							       =idalabel
 snake_case__  :    Dict							       ={v: k for k, v in idalabel.items()}

 snake_case__  :    Dict							       =partial(_a		,  num_labels=_a		,  idalabel=_a		,  labelaid=_a	)

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


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[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	:		Optional[int]										=   parser.parse_args()
       __lowerCamelCase	:		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)













import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       __lowerCamelCase	:		List[str]										=   argparse.ArgumentParser()

       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
       )
       # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
       parser.add_argument(
           """--original_config_file""",
           default=None,
           type=str,
           help="""The YAML config file corresponding to the original architecture.""",
       )
       parser.add_argument(
           """--num_in_channels""",
           default=None,
           type=int,
           help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
       )
       parser.add_argument(
           """--scheduler_type""",
           default="""pndm""",
           type=str,
           help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
       )
       parser.add_argument(
           """--pipeline_type""",
           default=None,
           type=str,
           help=(
               """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
               """. If `None` pipeline will be automatically inferred."""
           ),
       )
       parser.add_argument(
           """--image_size""",
           default=None,
           type=int,
           help=(
               """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
               """ Base. Use 768 for Stable Diffusion v2."""
           ),
       )
       parser.add_argument(
           """--prediction_type""",
           default=None,
           type=str,
           help=(
               """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
               """ Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
           ),
       )
       parser.add_argument(
           """--extract_ema""",
           action="""store_true""",
           help=(
               """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
               """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
               """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
           ),
       )
       parser.add_argument(
           """--upcast_attention""",
           action="""store_true""",
           help=(
               """Whether the attention computation should always be upcasted. This is necessary when running stable"""
               """ diffusion 2.1."""
           ),
       )
       parser.add_argument(
           """--from_safetensors""",
           action="""store_true""",
           help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
       )
       parser.add_argument(
           """--to_safetensors""",
           action="""store_true""",
           help="""Whether to store pipeline in safetensors format or not.""",
       )
       parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
       parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
       parser.add_argument(
           """--stable_unclip""",
           type=str,
           default=None,
           required=False,
           help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
       )
       parser.add_argument(
           """--stable_unclip_prior""",
           type=str,
           default=None,
           required=False,
           help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
       )
       parser.add_argument(
           """--clip_stats_path""",
           type=str,
           help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
           required=False,
       )
       parser.add_argument(
           """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
       )
       parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
       parser.add_argument(
           """--vae_path""",
           type=str,
           default=None,
           required=False,
           help="""Set to a path, hub id to an already converted vae to not convert it again.""",
       )
       __lowerCamelCase	:		Optional[Any]										=   parser.parse_args()

       __lowerCamelCase	:		Optional[int]										=   download_from_original_stable_diffusion_ckpt(
           checkpoint_path=args.checkpoint_path,
           original_config_file=args.original_config_file,
           image_size=args.image_size,
           prediction_type=args.prediction_type,
           model_type=args.pipeline_type,
           extract_ema=args.extract_ema,
           scheduler_type=args.scheduler_type,
           num_in_channels=args.num_in_channels,
           upcast_attention=args.upcast_attention,
           from_safetensors=args.from_safetensors,
           device=args.device,
           stable_unclip=args.stable_unclip,
           stable_unclip_prior=args.stable_unclip_prior,
           clip_stats_path=args.clip_stats_path,
           controlnet=args.controlnet,
           vae_path=args.vae_path,
       )

       if args.half:
              pipe.to(torch_dtype=torch.floataa)

       if args.controlnet:
              # only save the controlnet model
              pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
       else:
              pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





