


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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__   	=				logging.get_logger(__name__)
def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]		):
       _A   : Optional[int]       					=				MobileNetVaConfig(layer_norm_eps=0.0_01		)

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

       _A   : Any       					=				re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"       ,							_SCREAMING_SNAKE_CASE		)
       if matches:
              _A   : Dict       					=				float(matches[1]		)
              _A   : Union[str, Any]       					=				int(matches[2]		)

       if model_name.startswith("deeplabv3_"		):
              _A   : int       					=				8
              _A   : Union[str, Any]       					=				21
              _A   : Dict       					=				'pascal-voc-id2label.json'
       else:
              # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
              # of the usual 1000. The first class (index 0) is "background".
              _A   : Optional[int]       					=				1001
              _A   : Dict       					=				'imagenet-1k-id2label.json'

       _A   : List[Any]       					=				'huggingface/label-files'
       _A   : str       					=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							repo_type="dataset"		)       ,							"r"		)		)

       if config.num_labels == 1001:
              _A   : List[Any]       					=				{int(_SCREAMING_SNAKE_CASE		) + 1: v for k, v in idalabel.items()}
              _A   : List[Any]       					=				'background'
       else:
              _A   : Tuple       					=				{int(_SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}

       _A   : str       					=				idalabel
       _A   : Tuple       					=				{v: k for k, v in idalabel.items()}

       return config
def 							_UpperCAmelCase       ():
       _A   : Optional[int]       					=				'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A   : Optional[int]       					=				Image.open(requests.get(_SCREAMING_SNAKE_CASE       ,							stream=_SCREAMING_SNAKE_CASE		).raw		)
       return im
@torch.no_grad()
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						List[Any]=False		):
       _A   : List[Any]       					=				get_mobilenet_va_config(_SCREAMING_SNAKE_CASE		)

       # Load 🤗 model
       if model_name.startswith("deeplabv3_"		):
              _A   : Tuple       					=				MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE		).eval()
       else:
              _A   : List[Any]       					=				MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE		).eval()

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

       # Check outputs on an image, prepared by MobileNetV2ImageProcessor
       _A   : Optional[Any]       					=				MobileNetVaImageProcessor(
           crop_size={"width": config.image_size, "height": config.image_size}       ,							size={"shortest_edge": config.image_size + 32}       ,							)
       _A   : int       					=				image_processor(images=prepare_img()       ,							return_tensors="pt"		)
       _A   : Optional[int]       					=				model(**_SCREAMING_SNAKE_CASE		)
       _A   : Optional[Any]       					=				outputs.logits

       if model_name.startswith("deeplabv3_"		):
              assert logits.shape == (1, 21, 65, 65)

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     _A   : List[Any]       					=				torch.tensor(
                         [
                             [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                             [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                             [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                         ]		)

              else:
                     raise ValueError(f"Unknown model name: {model_name}"		)

              assert torch.allclose(logits[0, :3, :3, :3]       ,							_SCREAMING_SNAKE_CASE       ,							atol=1E-4		)
       else:
              assert logits.shape == (1, 1001)

              if model_name == "mobilenet_v2_1.4_224":
                     _A   : Any       					=				torch.tensor([0.01_81, -1.00_15, 0.46_88]		)
              elif model_name == "mobilenet_v2_1.0_224":
                     _A   : Optional[Any]       					=				torch.tensor([0.24_45, -1.19_93, 0.19_05]		)
              elif model_name == "mobilenet_v2_0.75_160":
                     _A   : Optional[int]       					=				torch.tensor([0.24_82, 0.41_36, 0.66_69]		)
              elif model_name == "mobilenet_v2_0.35_96":
                     _A   : Union[str, Any]       					=				torch.tensor([0.14_51, -0.46_24, 0.71_92]		)
              else:
                     _A   : Union[str, Any]       					=				None

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

       Path(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
       print(f"Saving model {model_name} to {pytorch_dump_folder_path}"		)
       model.save_pretrained(_SCREAMING_SNAKE_CASE		)
       print(f"Saving image processor to {pytorch_dump_folder_path}"		)
       image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

       if push_to_hub:
              print("Pushing to the hub..."		)
              _A   : Tuple       					=				'google/' + model_name
              image_processor.push_to_hub(_SCREAMING_SNAKE_CASE		)
              model.push_to_hub(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
   lowerCAmelCase__   	=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='mobilenet_v2_1.0_224',
       type=str,
       help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'
   )

   lowerCAmelCase__   	=				parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
       _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
       _A   : Any       					=				checkpoint["all_modules.0.W"].data
       _A   : Any       					=				checkpoint["all_modules.0.W"].data
       _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
       _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data

       _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
       _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data

       _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
       _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data

       _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
       _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
       _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
       _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data

       _A   : str       					=				4

       def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
              _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
              _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

              _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
              _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

       def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
              _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
              _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
              _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
              _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

              _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
              _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

              if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                     _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                     _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                            module_index += 1

              if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1
                     _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                     _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks		):
              _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                     _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                     _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                     module_index += 1
                     _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                     _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                     module_index += 1

       _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
       _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
       module_index += 1
       _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
       _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
    lowerCAmelCase__   	=				argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_path',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
        type=str,
        required=False,
        help='Path to the checkpoint to convert.',
    )

    parser.add_argument(
        '--config_file',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
        type=str,
        required=False,
        help='The config json file corresponding to the architecture.',
    )

    parser.add_argument(
        '--dump_path',
        default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
        type=str,
        required=False,
        help='Path to the output model.',
    )

    lowerCAmelCase__   	=				parser.parse_args()

    lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')

    with open(args.config_file) as f:
        lowerCAmelCase__   	=				json.loads(f.read())

    lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
        checkpoint,
        config,
    )

    if "sde" in config:
        del config["sde"]

    lowerCAmelCase__   	=				UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

        lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)
