





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()
SCREAMING_SNAKE_CASE__							:      int         =    logging.get_logger()







@dataclass
class 	__lowerCAmelCase      :
   _UpperCamelCase			:    nn.Module
   _UpperCamelCase			:    List[nn.Module]  		=					field(default_factory=_UpperCamelCase	)
   _UpperCamelCase			:    list  		=					field(default_factory=_UpperCamelCase	)





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      a__						:    Optional[Any]   		=	len(list(m.modules()   )   ) == 1 or isinstance(snake_case       ,      nn.Convad   ) or isinstance(snake_case       ,      nn.BatchNormad   )
      if has_not_submodules:
         self.traced.append(snake_case   )





   def __call__(			self       ,      snake_case   )					->	str:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





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







@dataclass
class 	__lowerCAmelCase      :
   _UpperCamelCase			:    nn.Module
   _UpperCamelCase			:    nn.Module
   _UpperCamelCase			:    int  		=					0
   _UpperCamelCase			:    List  		=					field(default_factory=_UpperCamelCase	)
   _UpperCamelCase			:    List  		=					field(default_factory=_UpperCamelCase	)





   def __call__(			self       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Any   		=	Tracker(self.dest   )(snake_case   ).parametrized
      a__						:    Any   		=	Tracker(self.src   )(snake_case   ).parametrized

      a__						:    Dict   		=	list(filter(lambda snake_case   : type(snake_case   ) not in self.src_skip       ,      snake_case   )   )
      a__						:    Dict   		=	list(filter(lambda snake_case   : type(snake_case   ) not in self.dest_skip       ,      snake_case   )   )

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

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







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase = True ):
   print(F"""Converting {name}...""" )
   with torch.no_grad():
      a__						:    Optional[Any]   		=	timm.create_model(lowerCamelCase				,			pretrained=lowerCamelCase ).eval()
      a__						:    str   		=	ResNetForImageClassification(lowerCamelCase ).eval()
      a__						:    List[str]   		=	ModuleTransfer(src=lowerCamelCase				,			dest=lowerCamelCase )
      a__						:    Optional[int]   		=	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."

   a__						:    List[Any]   		=	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
      a__						:    List[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=lowerCamelCase				,			)

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






def    _A	(   lowerCamelCase				,			lowerCamelCase = None				,			lowerCamelCase = True ):
   a__						:    Dict   		=	"imagenet-1k-id2label.json"
   a__						:    int   		=	1000
   a__						:    Any   		=	(1, num_labels)

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

   a__						:    Optional[Any]   		=	idalabel
   a__						:    Tuple   		=	{v: k for k, v in idalabel.items()}

   a__						:    List[str]   		=	partial(lowerCamelCase				,			num_labels=lowerCamelCase				,			idalabel=lowerCamelCase				,			labelaid=lowerCamelCase )

   a__						:    Tuple   		=	{
       "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__":
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    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.""",
  )

  SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()
  SCREAMING_SNAKE_CASE__							:      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 intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[str]         =    argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

SCREAMING_SNAKE_CASE__							:      List[Any]         =    """path-to-your-trained-model"""
SCREAMING_SNAKE_CASE__							:      Tuple         =    StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    pipe.to(device)

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
  SCREAMING_SNAKE_CASE__							:      Dict         =    pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")

