import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__			    =				logging.get_logger()




@dataclass
class 					UpperCamelCase	:
					__UpperCamelCase						=   42
					__UpperCamelCase						=   field(default_factory=lowerCAmelCase_      )
					__UpperCamelCase						=   field(default_factory=lowerCAmelCase_      )
					__UpperCamelCase						=   field(default_factory=lowerCAmelCase_      )




					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Optional[int]						):


							"""simple docstring"""




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




					def __call__( self :					List[str]      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook      ,name=__a						)						)						)
							self.module(__a						)
							[x.remove() for x in self.handles]
							return self







					@property
					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}




class 					UpperCamelCase	(     nn.Module      ):




					def __init__( self :					str      ,_lowerCAmelCase :					List[str]						):


							"""simple docstring"""




							super().__init__()

							__snake_case		       =     []
							# - get the stem
							feature_blocks.append(("conv1", model.stem)						)
							# - get all the feature blocks
							for k, v in model.trunk_output.named_children():
									assert k.startswith("block"						), F"""Unexpected layer name {k}"""
									__snake_case		       =     len(__a						) + 1
									feature_blocks.append((F"""res{block_index}""", v)						)

							__snake_case		       =     nn.ModuleDict(__a						)







					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							return get_trunk_forward_outputs(
							    __a      ,out_feat_keys=__a      ,feature_blocks=self._feature_blocks      ,)




class 					UpperCamelCase	(     lowerCAmelCase_      ):




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def 							_lowerCamelCase(				__snake_case	)  ->   Any:
		__snake_case		       =     RegNetConfig(depths=[2, 7, 17, 1]						,						hidden_sizes=[8, 8, 8, 8]						,						groups_width=8	)
		if "in1k" in model_name:
				__snake_case		       =     RegNetForImageClassification(_SCREAMING_SNAKE_CASE	)
		else:
				__snake_case		       =     RegNetModel(_SCREAMING_SNAKE_CASE	)
		# create from model (with small weights)
		__snake_case		       =     FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27						,						group_width=1010						,						w_a=1744						,						w_a=620.83						,						w_m=2.5_2	)	)	)

		with torch.no_grad():
				__snake_case		       =     from_model.eval()
				__snake_case		       =     our_model.eval()

				__snake_case		       =     torch.randn((1, 3, 32, 32)	)
				# trace both
				__snake_case		       =     Tracker(_SCREAMING_SNAKE_CASE	)
				__snake_case		       =     dest_tracker(_SCREAMING_SNAKE_CASE	).parametrized

				pprint(dest_tracker.nameamodule	)
				__snake_case		       =     Tracker(_SCREAMING_SNAKE_CASE	)
				__snake_case		       =     src_tracker(_SCREAMING_SNAKE_CASE	).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(__snake_case	):
				__snake_case		       =     OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								__snake_case		       =     param
				return params_dict

		__snake_case		       =     {}

		__snake_case		       =     to_params_dict(_SCREAMING_SNAKE_CASE	)
		__snake_case		       =     to_params_dict(_SCREAMING_SNAKE_CASE	)

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,						dst_state_dict.items()	):
				__snake_case		       =     dest_key
				logger.info(f"""{src_key} -> {dest_key}"""	)
		# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
		if "in1k" in model_name:
				__snake_case		       =     'classifier.1.weight'
				__snake_case		       =     'classifier.1.bias'

		return from_to_ours_keys



def 							_lowerCamelCase(				__snake_case						,						__snake_case = None						,						__snake_case = True	)  ->   str:
		__snake_case		       =     'imagenet-1k-id2label.json'
		__snake_case		       =     1000

		__snake_case		       =     'huggingface/label-files'
		__snake_case		       =     num_labels
		__snake_case		       =     json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						repo_type="dataset"	)	)						,						"r"	)	)
		__snake_case		       =     {int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}

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

		__snake_case		       =     partial(_SCREAMING_SNAKE_CASE						,						num_labels=_SCREAMING_SNAKE_CASE						,						idalabel=_SCREAMING_SNAKE_CASE						,						labelaid=_SCREAMING_SNAKE_CASE	)

		__snake_case		       =     {
		    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]						,						hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,						groups_width=1010	),
		    # finetuned on imagenet
		    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]						,						hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,						groups_width=1010	),
		}

		# add seer weights logic
		def load_using_classy_vision(__snake_case	) -> Tuple[Dict, Dict]:
				__snake_case		       =     torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,						model_dir=str(_SCREAMING_SNAKE_CASE	)						,						map_location="cpu"	)
				# check if we have a head, if yes add it
				__snake_case		       =     files['classy_state_dict']['base_model']['model']
				return model_state_dict["trunk"], model_state_dict["heads"]

		__snake_case		       =     {
		    'regnet-y-10b-seer': partial(
		        _SCREAMING_SNAKE_CASE						,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"						,						),
		    'regnet-y-10b-seer-in1k': partial(
		        _SCREAMING_SNAKE_CASE						,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"						,						),
		}

		__snake_case		       =     get_from_to_our_keys(_SCREAMING_SNAKE_CASE	)

		if not (save_directory / f"""{model_name}.pth""").exists():
				logger.info("Loading original state_dict."	)
				__snake_case		       =     names_to_from_model[model_name]()
				__snake_case		       =     from_state_dict_trunk
				if "in1k" in model_name:
						# add the head
						__snake_case		       =     {**from_state_dict_trunk, **from_state_dict_head}
				logger.info("Done!"	)

				__snake_case		       =     {}

				__snake_case		       =     list(from_state_dict.keys()	)
				__snake_case		       =     r'\.block.-part.'
				# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
				for key in from_state_dict.keys():
						# remove the weird "block[0,1]-part" from the key
						__snake_case		       =     re.sub(_SCREAMING_SNAKE_CASE						,						""						,						_SCREAMING_SNAKE_CASE	)
						# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
						__snake_case		       =     from_to_ours_keys[src_key]
						# store the parameter with our key
						__snake_case		       =     from_state_dict[key]
						not_used_keys.remove(_SCREAMING_SNAKE_CASE	)
				# check that all keys have been updated
				assert len(_SCREAMING_SNAKE_CASE	) == 0, f"""Some keys where not used {",".join(_SCREAMING_SNAKE_CASE	)}"""

				logger.info(f"""The following keys were not used: {",".join(_SCREAMING_SNAKE_CASE	)}"""	)

				# save our state dict to disk
				torch.save(_SCREAMING_SNAKE_CASE						,						save_directory / f"""{model_name}.pth"""	)

				del converted_state_dict
		else:
				logger.info("The state_dict was already stored on disk."	)
		if push_to_hub:
				logger.info(f"""Token is {os.environ["HF_TOKEN"]}"""	)
				logger.info("Loading our model."	)
				# create our model
				__snake_case		       =     names_to_config[model_name]
				__snake_case		       =     RegNetModel
				if "in1k" in model_name:
						__snake_case		       =     RegNetForImageClassification
				__snake_case		       =     our_model_func(_SCREAMING_SNAKE_CASE	)
				# place our model to the meta device (so remove all the weights)
				our_model.to(torch.device("meta"	)	)
				logger.info("Loading state_dict in our model."	)
				# load state dict
				__snake_case		       =     our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    _SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						[save_directory / f"""{model_name}.pth"""]	)
				logger.info("Finally, pushing!"	)
				# push it to hub
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / model_name						,						commit_message="Add model"						,						output_dir=save_directory / model_name						,						)
				__snake_case		       =     384
				# we can use the convnext one
				__snake_case		       =     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						,						size=_SCREAMING_SNAKE_CASE	)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / model_name						,						commit_message="Add image processor"						,						output_dir=save_directory / model_name						,						)


if __name__ == "__main__":
		lowerCamelCase__			    =				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 regnet* architecture,'
		        ' currently: regnetx-*, regnety-*. 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__			    =				parser.parse_args()

		lowerCamelCase__			    =				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 random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
					__UpperCamelCase						=   PaintByExamplePipeline
					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							torch.manual_seed(0						)
							__snake_case		       =     UNetaDConditionModel(
							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
							torch.manual_seed(0						)
							__snake_case		       =     AutoencoderKL(
							    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,latent_channels=4      ,)
							torch.manual_seed(0						)
							__snake_case		       =     CLIPVisionConfig(
							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)

							__snake_case		       =     {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components




					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     np.array(image.convert("RGB"						)						)
							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
							return image




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):


							"""simple docstring"""




							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)

							if str(_lowerCAmelCase						).startswith("mps"						):
									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
							else:
									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
							__snake_case		       =     {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
							__snake_case		       =     pipe.to("cpu"						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     self.get_dummy_inputs()
							__snake_case		       =     pipe(**_lowerCAmelCase						)
							__snake_case		       =     output.images

							__snake_case		       =     image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     "cpu"
							__snake_case		       =     self.get_dummy_inputs()
							inputs.pop("mask_image"						)
							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
							__snake_case		       =     image.clamp(0      ,1						) / 2

							# make sure here that pndm scheduler skips prk
							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
							__snake_case		       =     pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
							__snake_case		       =     output.images

							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]

							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)

							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
							__snake_case		       =     output.images

							assert out_a.shape == (1, 64, 64, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2







					def 							UpperCamelCase_						( self :					Union[str, Any]						):


							"""simple docstring"""




							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




@slow
@require_torch_gpu
class 					UpperCamelCase	(     unittest.TestCase      ):




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							__snake_case		       =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"						)
							__snake_case		       =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"						)
							__snake_case		       =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"						)

							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
							__snake_case		       =     pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     torch.manual_seed(321						)
							__snake_case		       =     pipe(
							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)

							__snake_case		       =     output.images

							__snake_case		       =     image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2





