



"""simple docstring"""


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
snake_case_				     =       logging.get_logger()







@dataclass
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :Any   ,  lowercase_ :Tensor   ,  lowercase_ :Tensor					)				->			Union[str, Any]:
												UpperCAmelCase             = len(list(m.modules()					)					) == 1 or isinstance(lowercase_   ,  nn.Convad					) or isinstance(lowercase_   ,  nn.BatchNormad					)
												if has_not_submodules:
																		self.traced.append(lowercase_					)






						def __call__(					self :Dict   ,  lowercase_ :Tensor					)				->			List[Any]:
												for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook					)					)
												self.module(lowercase_					)
												[x.remove() for x in self.handles]
												return self






						@property
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Any:
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda lowercase_					: len(list(x.state_dict().keys()					)					) > 0   ,  self.traced					)					)







@dataclass
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							1
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)
						__UpperCamelCase						=							True






						def __call__(					self :Tuple   ,  lowercase_ :Tensor					)				->			Dict:
												UpperCAmelCase             = Tracker(self.dest					)(lowercase_					).parametrized
												UpperCAmelCase             = Tracker(self.src					)(lowercase_					).parametrized

												UpperCAmelCase             = list(filter(lambda lowercase_					: type(lowercase_					) not in self.src_skip   ,  lowercase_					)					)
												UpperCAmelCase             = list(filter(lambda lowercase_					: type(lowercase_					) not in self.dest_skip   ,  lowercase_					)					)

												if len(lowercase_					) != len(lowercase_					) and self.raise_if_mismatch:
																		raise Exception(
																		    f"""Numbers of operations are different. Source module has {len(lowercase_					)} operations while"""
																		    f""" destination module has {len(lowercase_					)}."""					)

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







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :List[Any]   ,  lowercase_ :nn.Module					)				->			Union[str, Any]:
												super().__init__()

												UpperCAmelCase             = []
												# - 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}"""
																		UpperCAmelCase             = len(lowercase_					) + 1
																		feature_blocks.append((f"""res{block_index}""", v)					)

												UpperCAmelCase             = nn.ModuleDict(lowercase_					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :Tensor					)				->			Tuple:
												return get_trunk_forward_outputs(
												    lowercase_   ,  out_feat_keys=lowercase_   ,  feature_blocks=self._feature_blocks   ,  )







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :str					)				->			str:
												UpperCAmelCase             = x.split('-'					)
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)






						def __getitem__(					self :Optional[int]   ,  lowercase_ :str					)				->			Callable[[], Tuple[nn.Module, Dict]]:
												# default to timm!
												if x not in self:
																		UpperCAmelCase             = self.convert_name_to_timm(lowercase_					)
																		UpperCAmelCase             = partial(lambda: (timm.create_model(lowercase_   ,  pretrained=lowercase_					).eval(), None)					)

												else:
																		UpperCAmelCase             = super().__getitem__(lowercase_					)

												return val







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __getitem__(					self :Tuple   ,  lowercase_ :str					)				->			Callable[[], nn.Module]:
												if "seer" in x and "in1k" not in x:
																		UpperCAmelCase             = RegNetModel
												else:
																		UpperCAmelCase             = RegNetForImageClassification
												return val


def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						for from_key, to_key in keys:
												UpperCAmelCase             = from_state_dict[from_key].clone()
												print(F"""Copied key={from_key} to={to_key}"""						)
						return to_state_dict

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_		,	lowercase_		,	lowercase_		,	lowercase_ = True		,	):
						print(F"""Converting {name}..."""						)
						with torch.no_grad():
												UpperCAmelCase      ,    UpperCAmelCase             = from_model_func()
												UpperCAmelCase             = our_model_func(lowercase_						).eval()
												UpperCAmelCase             = ModuleTransfer(src=lowercase_		,	dest=lowercase_		,	raise_if_mismatch=lowercase_						)
												UpperCAmelCase             = torch.randn((1, 3, 224, 224)						)
												module_transfer(lowercase_						)

						if from_state_dict is not None:
												UpperCAmelCase             = []
												# for seer - in1k finetuned we have to manually copy the head
												if "seer" in name and "in1k" in name:
																		UpperCAmelCase             = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
												UpperCAmelCase             = manually_copy_vissl_head(lowercase_		,	our_model.state_dict()		,	lowercase_						)
												our_model.load_state_dict(lowercase_						)

						UpperCAmelCase             = our_model(lowercase_		,	output_hidden_states=lowercase_						)
						UpperCAmelCase             = (
						    our_outputs.logits if isinstance(lowercase_		,	lowercase_						) else our_outputs.last_hidden_state
						)

						UpperCAmelCase             = from_model(lowercase_						)
						UpperCAmelCase             = from_output[-1] if type(lowercase_						) is list else from_output

						# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
						if "seer" in name and "in1k" in name:
												UpperCAmelCase             = our_outputs.hidden_states[-1]

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

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

												UpperCAmelCase             = 224 if 'seer' not in name else 384
												# we can use the convnext one
												UpperCAmelCase             = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'		,	size=lowercase_						)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / name		,	commit_message='Add image processor'		,	use_temp_dir=lowercase_		,	)

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

def  _lowerCAmelCase      (   lowercase_		,	lowercase_ = None		,	lowercase_ = True						):
						UpperCAmelCase             = 'imagenet-1k-id2label.json'
						UpperCAmelCase             = 1000
						UpperCAmelCase             = (1, num_labels)

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

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

						UpperCAmelCase             = partial(lowercase_		,	num_labels=lowercase_		,	idalabel=lowercase_		,	labelaid=lowercase_						)

						UpperCAmelCase             = {
						    'regnet-x-002': ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7]		,	hidden_sizes=[24, 56, 152, 368]		,	groups_width=8		,	layer_type='x'						),
						    'regnet-x-004': ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 12]		,	hidden_sizes=[32, 64, 160, 384]		,	groups_width=16		,	layer_type='x'						),
						    'regnet-x-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7]		,	hidden_sizes=[48, 96, 240, 528]		,	groups_width=24		,	layer_type='x'						),
						    'regnet-x-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5]		,	hidden_sizes=[64, 128, 288, 672]		,	groups_width=16		,	layer_type='x'						),
						    'regnet-x-016': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 2]		,	hidden_sizes=[72, 168, 408, 912]		,	groups_width=24		,	layer_type='x'						),
						    'regnet-x-032': ImageNetPreTrainedConfig(
						        depths=[2, 6, 15, 2]		,	hidden_sizes=[96, 192, 432, 1008]		,	groups_width=48		,	layer_type='x'						),
						    'regnet-x-040': ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]		,	hidden_sizes=[80, 240, 560, 1360]		,	groups_width=40		,	layer_type='x'						),
						    'regnet-x-064': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]		,	hidden_sizes=[168, 392, 784, 1624]		,	groups_width=56		,	layer_type='x'						),
						    'regnet-x-080': ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]		,	hidden_sizes=[80, 240, 720, 1920]		,	groups_width=120		,	layer_type='x'						),
						    'regnet-x-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]		,	hidden_sizes=[224, 448, 896, 2240]		,	groups_width=112		,	layer_type='x'						),
						    'regnet-x-160': ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]		,	hidden_sizes=[256, 512, 896, 2048]		,	groups_width=128		,	layer_type='x'						),
						    'regnet-x-320': ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]		,	hidden_sizes=[336, 672, 1344, 2520]		,	groups_width=168		,	layer_type='x'						),
						    # y variant
						    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]		,	hidden_sizes=[24, 56, 152, 368]		,	groups_width=8						),
						    'regnet-y-004': ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6]		,	hidden_sizes=[48, 104, 208, 440]		,	groups_width=8						),
						    'regnet-y-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4]		,	hidden_sizes=[48, 112, 256, 608]		,	groups_width=16						),
						    'regnet-y-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2]		,	hidden_sizes=[64, 128, 320, 768]		,	groups_width=16						),
						    'regnet-y-016': ImageNetPreTrainedConfig(
						        depths=[2, 6, 17, 2]		,	hidden_sizes=[48, 120, 336, 888]		,	groups_width=24						),
						    'regnet-y-032': ImageNetPreTrainedConfig(
						        depths=[2, 5, 13, 1]		,	hidden_sizes=[72, 216, 576, 1512]		,	groups_width=24						),
						    'regnet-y-040': ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]		,	hidden_sizes=[128, 192, 512, 1088]		,	groups_width=64						),
						    'regnet-y-064': ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]		,	hidden_sizes=[144, 288, 576, 1296]		,	groups_width=72						),
						    'regnet-y-080': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]		,	hidden_sizes=[168, 448, 896, 2016]		,	groups_width=56						),
						    'regnet-y-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]		,	hidden_sizes=[224, 448, 896, 2240]		,	groups_width=112						),
						    'regnet-y-160': ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]		,	hidden_sizes=[224, 448, 1232, 3024]		,	groups_width=112						),
						    'regnet-y-320': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]		,	hidden_sizes=[232, 696, 1392, 3712]		,	groups_width=232						),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]		,	hidden_sizes=[232, 696, 1392, 3712]		,	groups_width=232						),
						    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]		,	hidden_sizes=[328, 984, 1968, 4920]		,	groups_width=328						),
						    'regnet-y-1280-seer': RegNetConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[528, 1056, 2904, 7392]		,	groups_width=264						),
						    'regnet-y-2560-seer': RegNetConfig(
						        depths=[3, 7, 16, 1]		,	hidden_sizes=[640, 1696, 2544, 5088]		,	groups_width=640						),
						    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[2020, 4040, 11110, 28280]		,	groups_width=1010						),
						    # finetuned on imagenet
						    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]		,	hidden_sizes=[232, 696, 1392, 3712]		,	groups_width=232						),
						    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]		,	hidden_sizes=[328, 984, 1968, 4920]		,	groups_width=328						),
						    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[528, 1056, 2904, 7392]		,	groups_width=264						),
						    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]		,	hidden_sizes=[640, 1696, 2544, 5088]		,	groups_width=640						),
						    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[2020, 4040, 11110, 28280]		,	groups_width=1010						),
						}

						UpperCAmelCase             = NameToOurModelFuncMap()
						UpperCAmelCase             = NameToFromModelFuncMap()
						# add seer weights logic

						def load_using_classy_vision(lowercase_		,	lowercase_						) -> Tuple[nn.Module, Dict]:
												UpperCAmelCase             = torch.hub.load_state_dict_from_url(lowercase_		,	model_dir=str(lowercase_						)		,	map_location='cpu'						)
												UpperCAmelCase             = model_func()
												# check if we have a head, if yes add it
												UpperCAmelCase             = files['classy_state_dict']['base_model']['model']
												UpperCAmelCase             = model_state_dict['trunk']
												model.load_state_dict(lowercase_						)
												return model.eval(), model_state_dict["heads"]

						# pretrained
						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'		,	lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27		,	group_width=1010		,	w_a=1744		,	w_a=6_2_0.8_3		,	w_m=2.5_2						)						)						)		,	)

						# IN1K finetuned
						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'		,	lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27		,	group_width=1010		,	w_a=1744		,	w_a=6_2_0.8_3		,	w_m=2.5_2						)						)						)		,	)

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


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

				snake_case_				     =       parser.parse_args()

				snake_case_				     =       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 os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class   UpperCAmelCase_    :







							def __init__(   self,			__a = "cpu",			__a = "openai/clip-vit-large-patch14"):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    device
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizerFast.from_pretrained(__a)
														_lowerCAmelCase     :							Any    =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
														_lowerCAmelCase     :							Union[str, Any]    =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
														_lowerCAmelCase     :							Tuple    =    torchvision.transforms.Normalize(self.image_mean,			self.image_std)
														_lowerCAmelCase     :							Optional[int]    =    torchvision.transforms.Resize(224)
														_lowerCAmelCase     :							Dict    =    torchvision.transforms.CenterCrop(224)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.resize(__a)
														_lowerCAmelCase     :							List[str]    =    self.center_crop(__a)
														_lowerCAmelCase     :							Optional[Any]    =    self.normalize(__a)
														return images







							def __call__(   self,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.tokenizer(text=__a,			**__a)
														_lowerCAmelCase     :							List[str]    =    self.preprocess_img(__a)
														_lowerCAmelCase     :							Tuple    =    {key: value.to(self.device) for (key, value) in encoding.items()}
														return encoding



class   UpperCAmelCase_    (       nn.Module):







							def __init__(   self,			__a=10,			__a=0.01,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=False,			__a=True,			__a="image",			__a=True,			__a=False,			__a=False,			__a=False,			):

														'''simple docstring'''

														super().__init__()
														_lowerCAmelCase     :							List[str]    =    None
														_lowerCAmelCase     :							List[str]    =    device if device else get_device()
														if vqgan:
																					_lowerCAmelCase     :							Union[str, Any]    =    vqgan
														else:
																					_lowerCAmelCase     :							Optional[Any]    =    load_vqgan(self.device,			conf_path=__a,			ckpt_path=__a)
														self.vqgan.eval()
														if clip:
																					_lowerCAmelCase     :							str    =    clip
														else:
																					_lowerCAmelCase     :							int    =    CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
														self.clip.to(self.device)
														_lowerCAmelCase     :							Optional[int]    =    ProcessorGradientFlow(device=self.device)

														_lowerCAmelCase     :							Any    =    iterations
														_lowerCAmelCase     :							List[Any]    =    lr
														_lowerCAmelCase     :							Tuple    =    log
														_lowerCAmelCase     :							List[str]    =    make_grid
														_lowerCAmelCase     :							int    =    return_val
														_lowerCAmelCase     :							Dict    =    quantize
														_lowerCAmelCase     :							Any    =    self.vqgan.decoder.z_shape







							def    snake_case__      (   self,			__a=None,			__a=None,			__a=5,			__a=True):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    []
														if output_path is None:
																					_lowerCAmelCase     :							List[Any]    =    "./animation.gif"
														if input_path is None:
																					_lowerCAmelCase     :							str    =    self.save_path
														_lowerCAmelCase     :							str    =    sorted(glob(input_path + "/*"))
														if not len(__a):
																					raise ValueError(
																					    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
																					    " function?)")
														if len(__a) == 1:
																					print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
														_lowerCAmelCase     :							Optional[int]    =    total_duration / len(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    [frame_duration] * len(__a)
														if extend_frames:
																					_lowerCAmelCase     :							Any    =    1.5
																					_lowerCAmelCase     :							List[str]    =    3
														for file_name in paths:
																					if file_name.endswith(".png"):
																												images.append(imageio.imread(__a))
														imageio.mimsave(__a,			__a,			duration=__a)
														print(f"gif saved to {output_path}")







							def    snake_case__      (   self,			__a=None,			__a=None):

														'''simple docstring'''

														if not (path or img):
																					raise ValueError("Input either path or tensor")
														if img is not None:
																					raise NotImplementedError
														_lowerCAmelCase     :							Dict    =    preprocess(Image.open(__a),			target_image_size=256).to(self.device)
														_lowerCAmelCase     :							Dict    =    preprocess_vqgan(__a)
														_lowerCAmelCase   ,    *_lowerCAmelCase     :							str    =    self.vqgan.encode(__a)
														return z







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.latent.detach().requires_grad_()
														_lowerCAmelCase     :							Dict    =    base_latent + transform_vector
														if self.quantize:
																					_lowerCAmelCase   ,    *_lowerCAmelCase     :							List[Any]    =    self.vqgan.quantize(__a)
														else:
																					_lowerCAmelCase     :							Any    =    trans_latent
														return self.vqgan.decode(__a)







							def    snake_case__      (   self,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.clip_preprocessor(text=__a,			images=__a,			return_tensors="pt",			padding=__a)
														_lowerCAmelCase     :							Optional[int]    =    self.clip(**__a)
														_lowerCAmelCase     :							Any    =    clip_outputs.logits_per_image
														if weights is not None:
																					_lowerCAmelCase     :							Tuple    =    similarity_logits * weights
														return similarity_logits.sum()







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(pos_prompts["prompts"],			__a,			weights=(1 / pos_prompts["weights"]))
														if neg_prompts:
																					_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(neg_prompts["prompts"],			__a,			weights=neg_prompts["weights"])
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    torch.tensor([1],			device=self.device)
														_lowerCAmelCase     :							List[str]    =    -torch.log(__a) + torch.log(__a)
														return loss







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.randn_like(self.latent,			requires_grad=__a,			device=self.device)
														_lowerCAmelCase     :							Optional[int]    =    torch.optim.Adam([vector],			lr=self.lr)

														for i in range(self.iterations):
																					optim.zero_grad()
																					_lowerCAmelCase     :							Any    =    self._add_vector(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    loop_post_process(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    self._get_CLIP_loss(__a,			__a,			__a)
																					print("CLIP loss",			__a)
																					if self.log:
																												wandb.log({"CLIP Loss": clip_loss})
																					clip_loss.backward(retain_graph=__a)
																					optim.step()
																					if self.return_val == "image":
																												yield custom_to_pil(transformed_img[0])
																					else:
																												yield vector







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														wandb.init(reinit=__a,			project="face-editor")
														wandb.config.update({"Positive Prompts": positive_prompts})
														wandb.config.update({"Negative Prompts": negative_prompts})
														wandb.config.update({"lr": self.lr, "iterations": self.iterations})
														if image_path:
																					_lowerCAmelCase     :							str    =    Image.open(__a)
																					_lowerCAmelCase     :							int    =    image.resize((256, 256))
																					wandb.log("Original Image",			wandb.Image(__a))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if not prompts:
																					return []
														_lowerCAmelCase     :							int    =    []
														_lowerCAmelCase     :							List[str]    =    []
														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							Union[str, Any]    =    [prompt.strip() for prompt in prompts.split("|")]
														for prompt in prompts:
																					if isinstance(__a,			(tuple, list)):
																												_lowerCAmelCase     :							Optional[Any]    =    prompt[0]
																												_lowerCAmelCase     :							Union[str, Any]    =    float(prompt[1])
																					elif ":" in prompt:
																												_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    prompt.split(":")
																												_lowerCAmelCase     :							Optional[Any]    =    float(__a)
																					else:
																												_lowerCAmelCase     :							Optional[int]    =    prompt
																												_lowerCAmelCase     :							List[Any]    =    1.0
																					processed_prompts.append(__a)
																					weights.append(__a)
														return {
														    "prompts": processed_prompts,
														    "weights": torch.tensor(__a,			device=self.device),
														}







							def    snake_case__      (   self,			__a,			__a=None,			__a=None,			__a=True,			__a=False,			__a=True,			__a=True,			__a=None,			):

														'''simple docstring'''

														if image_path:
																					_lowerCAmelCase     :							List[Any]    =    self._get_latent(__a)
														else:
																					_lowerCAmelCase     :							Any    =    torch.randn(self.latent_dim,			device=self.device)
														if self.log:
																					self._init_logging(__a,			__a,			__a)

														assert pos_prompts, "You must provide at least one positive prompt."
														_lowerCAmelCase     :							int    =    self.process_prompts(__a)
														_lowerCAmelCase     :							List[str]    =    self.process_prompts(__a)
														if save_final and save_path is None:
																					_lowerCAmelCase     :							int    =    os.path.join("./outputs/",			"_".join(pos_prompts["prompts"]))
														if not os.path.exists(__a):
																					os.makedirs(__a)
														else:
																					_lowerCAmelCase     :							Tuple    =    save_path + "_" + get_timestamp()
																					os.makedirs(__a)
														_lowerCAmelCase     :							Tuple    =    save_path

														_lowerCAmelCase     :							List[Any]    =    self.vqgan.decode(self.latent)[0]
														if show_intermediate:
																					print("Original Image")
																					show_pil(custom_to_pil(__a))

														_lowerCAmelCase     :							int    =    loop_post_process(__a)
														for iter, transformed_img in enumerate(self._optimize_CLIP(__a,			__a,			__a)):
																					if show_intermediate:
																												show_pil(__a)
																					if save_intermediate:
																												transformed_img.save(os.path.join(self.save_path,			f"iter_{iter:03d}.png"))
																					if self.log:
																												wandb.log({"Image": wandb.Image(__a)})
														if show_final:
																					show_pil(__a)
														if save_final:
																					transformed_img.save(os.path.join(self.save_path,			f"iter_{iter:03d}_final.png"))
