



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()
a_			=					logging.get_logger()



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  field(default_factory=lowerCAmelCase__   )
 lowerCAmelCase__       					=  field(default_factory=lowerCAmelCase__   )




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    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	,				__UpperCAmelCase				):



    '''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        lowerCamelCase      (						self				):



    '''simple docstring'''
    # check the len of the state_dict keys to see if we have learnable params
    return list(filter(lambda __UpperCAmelCase				: len(list(x.state_dict().keys()				)				) > 0	,				self.traced				)				)



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  0
 lowerCAmelCase__       					=  field(default_factory=lowerCAmelCase__   )
 lowerCAmelCase__       					=  field(default_factory=lowerCAmelCase__   )




 def __call__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    Tracker(self.dest				)(__UpperCAmelCase				).parametrized
    __lowerCamelCase													=    Tracker(self.src				)(__UpperCAmelCase				).parametrized

    __lowerCamelCase													=    list(filter(lambda __UpperCAmelCase				: type(__UpperCAmelCase				) not in self.src_skip	,				__UpperCAmelCase				)				)
    __lowerCamelCase													=    list(filter(lambda __UpperCAmelCase				: 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 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: ResNetConfig ,_UpperCamelCase			: Path ,_UpperCamelCase			: bool = True							):
   print(F"""Converting {name}..."""							)
   with torch.no_grad():
      __lowerCamelCase													=    timm.create_model(_UpperCamelCase ,pretrained=_UpperCamelCase							).eval()
      __lowerCamelCase													=    ResNetForImageClassification(_UpperCamelCase							).eval()
      __lowerCamelCase													=    ModuleTransfer(src=_UpperCamelCase ,dest=_UpperCamelCase							)
      __lowerCamelCase													=    torch.randn((1, 3, 2_24, 2_24)							)
      module_transfer(_UpperCamelCase							)

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

   __lowerCamelCase													=    F"""resnet{"-".join(name.split("resnet"							)							)}"""
   print(_UpperCamelCase							)

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

      # we can use the convnext one
      __lowerCamelCase													=    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=_UpperCamelCase ,)

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





def 		a__			(		_UpperCamelCase			: Path ,_UpperCamelCase			: str = None ,_UpperCamelCase			: bool = True							):
   __lowerCamelCase													=    '''imagenet-1k-id2label.json'''
   __lowerCamelCase													=    10_00
   __lowerCamelCase													=    (1, num_labels)

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

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

   __lowerCamelCase													=    partial(_UpperCamelCase ,num_labels=_UpperCamelCase ,idalabel=_UpperCamelCase ,labelaid=_UpperCamelCase							)

   __lowerCamelCase													=    {
       '''resnet18''': ImageNetPreTrainedConfig(
           depths=[2, 2, 2, 2] ,hidden_sizes=[64, 1_28, 2_56, 5_12] ,layer_type='''basic'''							),
       '''resnet26''': ImageNetPreTrainedConfig(
           depths=[2, 2, 2, 2] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck'''							),
       '''resnet34''': ImageNetPreTrainedConfig(
           depths=[3, 4, 6, 3] ,hidden_sizes=[64, 1_28, 2_56, 5_12] ,layer_type='''basic'''							),
       '''resnet50''': ImageNetPreTrainedConfig(
           depths=[3, 4, 6, 3] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck'''							),
       '''resnet101''': ImageNetPreTrainedConfig(
           depths=[3, 4, 23, 3] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck'''							),
       '''resnet152''': ImageNetPreTrainedConfig(
           depths=[3, 8, 36, 3] ,hidden_sizes=[2_56, 5_12, 10_24, 20_48] ,layer_type='''bottleneck'''							),
   }

   if model_name:
      convert_weight_and_push(_UpperCamelCase ,names_to_config[model_name] ,_UpperCamelCase ,_UpperCamelCase							)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   return config, expected_shape


if __name__ == "__main__":
  a_			=					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.""",
  )

  a_			=					parser.parse_args()
  a_			=					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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_			=					False



class  __lowerCAmelCase     (	unittest.TestCase   ):
 pass



@nightly
@require_torch_gpu
class  __lowerCAmelCase     (	unittest.TestCase   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    generator.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''cyberpunk 2077'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''	,				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger '''
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.text_to_image(
        prompt=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

    __lowerCamelCase													=    pipe.image_variation(__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1






