



'''simple docstring'''

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()
UpperCAmelCase			=    logging.get_logger()

@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:    nn.Module
						UpperCAmelCase							:    List[nn.Module]           =		field(default_factory=_lowerCAmelCase					)
						UpperCAmelCase							:    list           =		field(default_factory=_lowerCAmelCase					)
						UpperCAmelCase							:    Dict[str, nn.Module]           =		field(default_factory=_lowerCAmelCase					)


						def 				__snake_case    (					self	,       A_	,       A_	,       A_	,       A_      )	->	List[Any]:
							lowerCAmelCase    =	len(list(m.modules()      )      ) == 1 or isinstance(A_	,       nn.Convad      ) or isinstance(A_	,       nn.BatchNormad      )
							if has_not_submodules:
								self.traced.append(A_      )
								lowerCAmelCase    =	m


						def __call__(					self	,       A_      )	->	Optional[Any]:
							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 				__snake_case    (					self      )	->	Dict:
							# check the len of the state_dict keys to see if we have learnable params
							return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()      )      ) > 0}







class __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	Any:
							super().__init__()

							lowerCAmelCase    =	[]
							# - 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}'
								lowerCAmelCase    =	len(A_      ) + 1
								feature_blocks.append((f'res{block_index}', v)      )

							lowerCAmelCase    =	nn.ModuleDict(A_      )




						def 				__snake_case    (					self	,       A_      )	->	int:
							return get_trunk_forward_outputs(
							    A_	,       out_feat_keys=A_	,       feature_blocks=self._feature_blocks	,       )







class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	int:
							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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str							)			->						Dict[str, str]:







	"""simple docstring"""
	lowerCAmelCase    =	RegNetConfig(depths=[2, 7, 17, 1]							,		hidden_sizes=[8, 8, 8, 8]							,		groups_width=8							)
	if "in1k" in model_name:
		lowerCAmelCase    =	RegNetForImageClassification(_SCREAMING_SNAKE_CASE							)
	else:
		lowerCAmelCase    =	RegNetModel(_SCREAMING_SNAKE_CASE							)
	# create from model (with small weights)
	lowerCAmelCase    =	FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=27							,		group_width=1_010							,		w_a=1_744							,		w_a=620.83							,		w_m=2.52							)							)							)

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

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

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

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

	lowerCAmelCase    =	{}

	lowerCAmelCase    =	to_params_dict(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	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()							):
		lowerCAmelCase    =	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:
		lowerCAmelCase    =	"""classifier.1.weight"""
		lowerCAmelCase    =	"""classifier.1.bias"""

	return from_to_ours_keys






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Path							,		_SCREAMING_SNAKE_CASE     :						str = None							,		_SCREAMING_SNAKE_CASE     :						bool = True							)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	"""imagenet-1k-id2label.json"""
	lowerCAmelCase    =	1_000

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

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

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

	lowerCAmelCase    =	{
	    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,		hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,		groups_width=1_010							),
	    # finetuned on imagenet
	    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,		hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,		groups_width=1_010							),
	}

	# add seer weights logic
	def load_using_classy_vision(_SCREAMING_SNAKE_CASE     :						str							) -> Tuple[Dict, Dict]:
		lowerCAmelCase    =	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
		lowerCAmelCase    =	files["""classy_state_dict"""]["""base_model"""]["""model"""]
		return model_state_dict["trunk"], model_state_dict["heads"]

	lowerCAmelCase    =	{
	    """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"""							,		),
	}

	lowerCAmelCase    =	get_from_to_our_keys(_SCREAMING_SNAKE_CASE							)

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

		lowerCAmelCase    =	{}

		lowerCAmelCase    =	list(from_state_dict.keys()							)
		lowerCAmelCase    =	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
			lowerCAmelCase    =	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
			lowerCAmelCase    =	from_to_ours_keys[src_key]
			# store the parameter with our key
			lowerCAmelCase    =	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
		lowerCAmelCase    =	names_to_config[model_name]
		lowerCAmelCase    =	RegNetModel
		if "in1k" in model_name:
			lowerCAmelCase    =	RegNetForImageClassification
		lowerCAmelCase    =	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
		lowerCAmelCase    =	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							,		)
		lowerCAmelCase    =	384
		# we can use the convnext one
		lowerCAmelCase    =	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__":
				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 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.',
				)

				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)




'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Optional[Any]           =		StableDiffusionModelEditingPipeline
						UpperCAmelCase							:    List[str]           =		TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS


						def 				__snake_case    (					self      )	->	Optional[Any]:
							torch.manual_seed(0      )
							lowerCAmelCase    =	UNetaDConditionModel(
							    block_out_channels=(32, 64)	,       layers_per_block=2	,       sample_size=32	,       in_channels=4	,       out_channels=4	,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,       cross_attention_dim=32	,       )
							lowerCAmelCase    =	DDIMScheduler()
							torch.manual_seed(0      )
							lowerCAmelCase    =	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      )
							lowerCAmelCase    =	CLIPTextConfig(
							    bos_token_id=0	,       eos_token_id=2	,       hidden_size=32	,       intermediate_size=37	,       layer_norm_eps=1e-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1000	,       )
							lowerCAmelCase    =	CLIPTextModel(A_      )
							lowerCAmelCase    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							lowerCAmelCase    =	{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components


						def 				__snake_case    (					self	,       A_	,       A_=0      )	->	Optional[int]:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    # Setting height and width to None to prevent OOMs on CPU.
							    """height""": None,
							    """width""": None,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	List[str]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]      )

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


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	"""french fries"""
							lowerCAmelCase    =	sd_pipe(**A_	,       negative_prompt=A_      )
							lowerCAmelCase    =	output.images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]      )

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


						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0_0_8_5	,       beta_end=0.0_1_2	,       beta_schedule="""scaled_linear"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]      )

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


						def 				__snake_case    (					self      )	->	Optional[Any]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	PNDMScheduler()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							# the pipeline does not expect pndm so test if it raises error.
							with self.assertRaises(A_      ):
								lowerCAmelCase    =	sd_pipe(**A_      ).images


						def 				__snake_case    (					self      )	->	str:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3      )




						def 				__snake_case    (					self      )	->	Any:
							super().test_attention_slicing_forward_pass(expected_max_diff=5e-3      )







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


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	int:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


						def 				__snake_case    (					self	,       A_=0      )	->	Dict:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(A_	,       safety_checker=A_      )
							pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)

							lowerCAmelCase    =	np.array(
							    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]      )

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

							# make sure image changes after editing
							pipe.edit_model("""A pack of roses"""	,       """A pack of blue roses"""      )

							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(expected_slice - image_slice      ).max() > 1e-1




						def 				__snake_case    (					self      )	->	str:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	DDIMScheduler.from_pretrained(A_	,       subfolder="""scheduler"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(
							    A_	,       scheduler=A_	,       safety_checker=A_      )
							lowerCAmelCase    =	pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing(1      )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      )

							lowerCAmelCase    =	torch.cuda.max_memory_allocated()
							# make sure that less than 4.4 GB is allocated
							assert mem_bytes < 4.4 * 10**9