



import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case    :						Any  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Optional[int]						    = MobileNetVaConfig(layer_norm_eps=0.001       )

						if "_quant" in model_name:
												raise ValueError("Quantized models are not supported."       )

						A__	:							Dict						    = re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"							,     _lowerCamelCase       )
						if matches:
												A__	:							int						    = float(matches[1]       )
												A__	:							Optional[int]						    = int(matches[2]       )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						A__	:							Any						    = 1_0_0_1
						A__	:							Tuple						    = "imagenet-1k-id2label.json"
						A__	:							Dict						    = "huggingface/label-files"
						A__	:							str						    = json.load(open(hf_hub_download(_lowerCamelCase							,     _lowerCamelCase							,     repo_type="dataset"       )							,     "r"       )       )
						A__	:							List[str]						    = {int(_lowerCamelCase       ) + 1: v for k, v in idalabel.items()}
						A__	:							int						    = "background"
						A__	:							List[Any]						    = idalabel
						A__	:							Any						    = {v: k for k, v in idalabel.items()}

						return config
def 	snake_case__       (					)		->		Any:







						"""simple docstring"""

						A__	:							Any						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__	:							Tuple						    = Image.open(requests.get(_lowerCamelCase							,     stream=_lowerCamelCase       ).raw       )
						return im
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase=False       )		->		Any:







						"""simple docstring"""

						A__	:							Tuple						    = get_mobilenet_va_config(_lowerCamelCase       )

						# Load 🤗 model
						A__	:							Any						    = MobileNetVaForImageClassification(_lowerCamelCase       ).eval()

						# Load weights from TensorFlow checkpoint
						load_tf_weights_in_mobilenet_va(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase       )

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						A__	:							Tuple						    = MobileNetVaImageProcessor(
						    crop_size={"width": config.image_size, "height": config.image_size}							,     size={"shortest_edge": config.image_size + 3_2}							,     )
						A__	:							Tuple						    = image_processor(images=prepare_img()							,     return_tensors="pt"       )
						A__	:							List[Any]						    = model(**_lowerCamelCase       )
						A__	:							List[str]						    = outputs.logits

						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v1_1.0_224":
												A__	:							Union[str, Any]						    = torch.tensor([-4.1739, -1.1233, 3.1205]       )
						elif model_name == "mobilenet_v1_0.75_192":
												A__	:							Optional[int]						    = torch.tensor([-3.9440, -2.3141, -0.3333]       )
						else:
												A__	:							Optional[int]						    = None

						if expected_logits is not None:
												assert torch.allclose(logits[0, :3]							,     _lowerCamelCase							,     atol=1E-4       )

						Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
						print(F'Saving model {model_name} to {pytorch_dump_folder_path}'       )
						model.save_pretrained(_lowerCamelCase       )
						print(F'Saving image processor to {pytorch_dump_folder_path}'       )
						image_processor.save_pretrained(_lowerCamelCase       )

						if push_to_hub:
												print("Pushing to the hub..."       )
												A__	:							Dict						    = "google/" + model_name
												image_processor.push_to_hub(_lowerCamelCase       )
												model.push_to_hub(_lowerCamelCase       )


if __name__ == "__main__":
		snake_case    :						Any  =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='mobilenet_v1_1.0_224',
		    type=str,
		    help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
		)
		parser.add_argument(
		    '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		snake_case    :						Union[str, Any]  =						parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							List[str]						    = checkpoint

						A__	:							Dict						    = {}

						A__	:							Union[str, Any]						    = vae_state_dict["encoder.conv_in.weight"]
						A__	:							int						    = vae_state_dict["encoder.conv_in.bias"]
						A__	:							List[Any]						    = vae_state_dict["encoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.conv_out.bias"]
						A__	:							int						    = vae_state_dict["encoder.norm_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.norm_out.bias"]

						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_in.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.conv_in.bias"]
						A__	:							str						    = vae_state_dict["decoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_out.bias"]
						A__	:							Union[str, Any]						    = vae_state_dict["decoder.norm_out.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.norm_out.bias"]

						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.bias"]
						A__	:							Dict						    = vae_state_dict["post_quant_conv.weight"]
						A__	:							Union[str, Any]						    = vae_state_dict["post_quant_conv.bias"]

						# Retrieves the keys for the encoder down blocks only
						A__	:							str						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "encoder.down" in layer}       )
						A__	:							Optional[int]						    = {
						    layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						# Retrieves the keys for the decoder up blocks only
						A__	:							Any						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "decoder.up" in layer}       )
						A__	:							Any						    = {
						    layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						for i in range(__lowercase       ):
												A__	:							Any						    = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

												if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
																		A__	:							Any						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.weight'       )
																		A__	:							Optional[int]						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.bias'       )

												A__	:							Any						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "encoder.mid.block" in key]
						A__	:							Any						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Optional[int]						    = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

												A__	:							Optional[Any]						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							str						    = [key for key in vae_state_dict if "encoder.mid.attn" in key]
						A__	:							List[Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							Optional[int]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )

						for i in range(__lowercase       ):
												A__	:							Dict						    = num_up_blocks - 1 - i
												A__	:							int						    = [
												    key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
												]

												if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
																		A__	:							Tuple						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.weight'
																		]
																		A__	:							List[Any]						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.bias'
																		]

												A__	:							str						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							List[str]						    = {"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "decoder.mid.block" in key]
						A__	:							Optional[int]						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Union[str, Any]						    = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

												A__	:							Dict						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							Optional[Any]						    = [key for key in vae_state_dict if "decoder.mid.attn" in key]
						A__	:							Union[str, Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							List[str]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )
						return new_checkpoint
def 	snake_case__       (					__lowercase							,     __lowercase							,     )		->		Tuple:







						"""simple docstring"""

						A__	:							List[str]						    = requests.get(
						    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"       )
						A__	:							Dict						    = io.BytesIO(r.content       )

						A__	:							str						    = OmegaConf.load(__lowercase       )
						A__	:							Any						    = 5_1_2
						A__	:							Optional[Any]						    = "cuda" if torch.cuda.is_available() else "cpu"
						if checkpoint_path.endswith("safetensors"       ):
												from safetensors import safe_open

												A__	:							str						    = {}
												with safe_open(__lowercase							,     framework="pt"							,     device="cpu"       ) as f:
																		for key in f.keys():
																								A__	:							List[str]						    = f.get_tensor(__lowercase       )
						else:
												A__	:							str						    = torch.load(__lowercase							,     map_location=__lowercase       )["state_dict"]

						# Convert the VAE model.
						A__	:							List[str]						    = create_vae_diffusers_config(__lowercase							,     image_size=__lowercase       )
						A__	:							List[Any]						    = custom_convert_ldm_vae_checkpoint(__lowercase							,     __lowercase       )

						A__	:							Dict						    = AutoencoderKL(**__lowercase       )
						vae.load_state_dict(__lowercase       )
						vae.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						List[Any]  =						argparse.ArgumentParser()

		parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

		snake_case    :						int  =						parser.parse_args()

		vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)