





import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ 							=       logging.get_logger(__name__)





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Optional[Any]:



					'''simple docstring'''




					print('Loading config file...'  )

					def flatten_yaml_as_dict(UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Tuple=""    ,      UpperCamelCase__     :    Optional[int]="."  ):
										_snake_case													=			[]
										for k, v in d.items():
															_snake_case													=			parent_key + sep + k if parent_key else k
															if isinstance(UpperCamelCase__    ,      collections.abc.MutableMapping  ):
																				items.extend(flatten_yaml_as_dict(UpperCamelCase__    ,      UpperCamelCase__    ,      sep=UpperCamelCase__  ).items()  )
															else:
																				items.append((new_key, v)  )
										return dict(UpperCamelCase__  )

					_snake_case													=			argparse.Namespace()
					with open(UpperCamelCase__    ,      'r'  ) as yaml_file:
										try:
															_snake_case													=			yaml.load(UpperCamelCase__    ,      Loader=yaml.FullLoader  )

															_snake_case													=			flatten_yaml_as_dict(UpperCamelCase__  )
															for k, v in flat_cfg.items():
																				setattr(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
										except yaml.YAMLError as exc:
															logger.error('Error while loading config file: {}. Error message: {}'.format(UpperCamelCase__    ,      str(UpperCamelCase__  )  )  )
					return config





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    str  )     ->	Any:



					'''simple docstring'''




					_snake_case													=			MobileViTVaConfig()

					_snake_case													=			False

					# dataset
					if task_name.startswith('imagenet1k_'  ):
										_snake_case													=			1_000
										if int(task_name.strip().split('_'  )[-1]  ) == 384:
															_snake_case													=			384
										else:
															_snake_case													=			256
										_snake_case													=			'imagenet-1k-id2label.json'
					elif task_name.startswith('imagenet21k_to_1k_'  ):
										_snake_case													=			21_000
										if int(task_name.strip().split('_'  )[-1]  ) == 384:
															_snake_case													=			384
										else:
															_snake_case													=			256
										_snake_case													=			'imagenet-22k-id2label.json'
					elif task_name.startswith('ade20k_'  ):
										_snake_case													=			151
										_snake_case													=			512
										_snake_case													=			'ade20k-id2label.json'
										_snake_case													=			True
					elif task_name.startswith('voc_'  ):
										_snake_case													=			21
										_snake_case													=			512
										_snake_case													=			'pascal-voc-id2label.json'
										_snake_case													=			True

					# orig_config
					_snake_case													=			load_orig_config_file(UpperCamelCase__  )
					assert getattr(UpperCamelCase__    ,      'model.classification.name'    ,      -1  ) == "mobilevit_v2", "Invalid model"
					_snake_case													=			getattr(UpperCamelCase__    ,      'model.classification.mitv2.width_multiplier'    ,      1.0  )
					assert (
					    getattr(UpperCamelCase__    ,      'model.classification.mitv2.attn_norm_layer'    ,      -1  ) == "layer_norm_2d"
					), "Norm layers other than layer_norm_2d is not supported"
					_snake_case													=			getattr(UpperCamelCase__    ,      'model.classification.activation.name'    ,      'swish'  )
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										_snake_case													=			getattr(UpperCamelCase__    ,      'model.segmentation.output_stride'    ,      16  )
										if "_deeplabv3" in task_name:
															_snake_case													=			getattr(UpperCamelCase__    ,      'model.segmentation.deeplabv3.aspp_rates'    ,      [12, 24, 36]  )
															_snake_case													=			getattr(UpperCamelCase__    ,      'model.segmentation.deeplabv3.aspp_out_channels'    ,      512  )
															_snake_case													=			getattr(UpperCamelCase__    ,      'model.segmentation.deeplabv3.aspp_dropout'    ,      0.1  )

    # id2label
					_snake_case													=			'huggingface/label-files'
					_snake_case													=			json.load(open(hf_hub_download(UpperCamelCase__    ,      UpperCamelCase__    ,      repo_type='dataset'  )    ,      'r'  )  )
					_snake_case													=			{int(UpperCamelCase__  ): v for k, v in idalabel.items()}
					_snake_case													=			idalabel
					_snake_case													=			{v: k for k, v in idalabel.items()}

					return config





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Optional[Any]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			dct.pop(UpperCamelCase__  )
					_snake_case													=			val





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Union[str, Any]=False  )     ->	Any:



					'''simple docstring'''




					if base_model:
										_snake_case													=			''
					else:
										_snake_case													=			'mobilevitv2.'

					_snake_case													=			[]
					for k in state_dict.keys():
										if k[:8] == "encoder.":
															_snake_case													=			k[8:]
										else:
															_snake_case													=			k

										if ".block." in k:
															_snake_case													=			k_new.replace('.block.'    ,      '.'  )
										if ".conv." in k:
															_snake_case													=			k_new.replace('.conv.'    ,      '.convolution.'  )
										if ".norm." in k:
															_snake_case													=			k_new.replace('.norm.'    ,      '.normalization.'  )

										if "conv_1." in k:
															_snake_case													=			k_new.replace('conv_1.'    ,      F'''{model_prefix}conv_stem.'''  )
										for i in [1, 2]:
															if F'''layer_{i}.''' in k:
																				_snake_case													=			k_new.replace(F'''layer_{i}.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.layer.'''  )
										if ".exp_1x1." in k:
															_snake_case													=			k_new.replace('.exp_1x1.'    ,      '.expand_1x1.'  )
										if ".red_1x1." in k:
															_snake_case													=			k_new.replace('.red_1x1.'    ,      '.reduce_1x1.'  )

										for i in [3, 4, 5]:
															if F'''layer_{i}.0.''' in k:
																				_snake_case													=			k_new.replace(F'''layer_{i}.0.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''  )
															if F'''layer_{i}.1.local_rep.0.''' in k:
																				_snake_case													=			k_new.replace(F'''layer_{i}.1.local_rep.0.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''  )
															if F'''layer_{i}.1.local_rep.1.''' in k:
																				_snake_case													=			k_new.replace(F'''layer_{i}.1.local_rep.1.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''  )

										for i in [3, 4, 5]:
															if i == 3:
																				_snake_case													=			[0, 1]
															elif i == 4:
																				_snake_case													=			[0, 1, 2, 3]
															elif i == 5:
																				_snake_case													=			[0, 1, 2]

															for j in j_in:
																				if F'''layer_{i}.1.global_rep.{j}.''' in k:
																									_snake_case													=			k_new.replace(
																									    F'''layer_{i}.1.global_rep.{j}.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''  )
															if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
																				_snake_case													=			k_new.replace(
																				    F'''layer_{i}.1.global_rep.{j+1}.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''  )

															if F'''layer_{i}.1.conv_proj.''' in k:
																				_snake_case													=			k_new.replace(F'''layer_{i}.1.conv_proj.'''    ,      F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''  )

										if "pre_norm_attn.0." in k:
															_snake_case													=			k_new.replace('pre_norm_attn.0.'    ,      'layernorm_before.'  )
										if "pre_norm_attn.1." in k:
															_snake_case													=			k_new.replace('pre_norm_attn.1.'    ,      'attention.'  )
										if "pre_norm_ffn.0." in k:
															_snake_case													=			k_new.replace('pre_norm_ffn.0.'    ,      'layernorm_after.'  )
										if "pre_norm_ffn.1." in k:
															_snake_case													=			k_new.replace('pre_norm_ffn.1.'    ,      'ffn.conv1.'  )
										if "pre_norm_ffn.3." in k:
															_snake_case													=			k_new.replace('pre_norm_ffn.3.'    ,      'ffn.conv2.'  )

										if "classifier.1." in k:
															_snake_case													=			k_new.replace('classifier.1.'    ,      'classifier.'  )

										if "seg_head." in k:
															_snake_case													=			k_new.replace('seg_head.'    ,      'segmentation_head.'  )
										if ".aspp_layer." in k:
															_snake_case													=			k_new.replace('.aspp_layer.'    ,      '.'  )
										if ".aspp_pool." in k:
															_snake_case													=			k_new.replace('.aspp_pool.'    ,      '.'  )

										rename_keys.append((k, k_new)  )
					return rename_keys





def   lowerCamelCase__       (       UpperCamelCase__     :    str  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			[]
					for k in state_dict.keys():
										if k.startswith('seg_head.aux_head.'  ):
															keys_to_ignore.append(UpperCamelCase__  )
					for k in keys_to_ignore:
										state_dict.pop(UpperCamelCase__    ,      UpperCamelCase__  )





def   lowerCamelCase__       (       )     ->	Optional[int]:



					'''simple docstring'''




					_snake_case													=			'http://images.cocodataset.org/val2017/000000039769.jpg'
					# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
					_snake_case													=			Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__  ).raw  )
					return im





@torch.no_grad()
def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    Optional[int]  )     ->	Any:



					'''simple docstring'''




					_snake_case													=			get_mobilevitva_config(UpperCamelCase__    ,      UpperCamelCase__  )

					# load original state_dict
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )

					# load huggingface model
					if task_name.startswith('ade20k_'  ) or task_name.startswith('voc_'  ):
										_snake_case													=			MobileViTVaForSemanticSegmentation(UpperCamelCase__  ).eval()
										_snake_case													=			False
					else:
										_snake_case													=			MobileViTVaForImageClassification(UpperCamelCase__  ).eval()
										_snake_case													=			False

					# remove and rename some keys of load the original model
					_snake_case													=			checkpoint
					remove_unused_keys(UpperCamelCase__  )
					_snake_case													=			create_rename_keys(UpperCamelCase__    ,      base_model=UpperCamelCase__  )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )

					# load modified state_dict
					model.load_state_dict(UpperCamelCase__  )

					# Check outputs on an image, prepared by MobileViTImageProcessor
					_snake_case													=			MobileViTImageProcessor(crop_size=config.image_size    ,      size=config.image_size + 32  )
					_snake_case													=			image_processor(images=prepare_img()    ,      return_tensors='pt'  )
					_snake_case													=			model(**UpperCamelCase__  )

					# verify classification model
					if task_name.startswith('imagenet'  ):
										_snake_case													=			outputs.logits
										_snake_case													=			logits.argmax(-1  ).item()
										print('Predicted class:'    ,      model.config.idalabel[predicted_class_idx]  )
										if task_name.startswith('imagenet1k_256'  ) and config.width_multiplier == 1.0:
															# expected_logits for base variant
															_snake_case													=			torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]  )
															assert torch.allclose(logits[0, :3]    ,      UpperCamelCase__    ,      atol=1e-4  )

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


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--task""",
					    default="""imagenet1k_256""",
					    type=str,
					    help=(
					        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
					        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
					    ),
					    choices=[
					        """imagenet1k_256""",
					        """imagenet1k_384""",
					        """imagenet21k_to_1k_256""",
					        """imagenet21k_to_1k_384""",
					        """ade20k_deeplabv3""",
					        """voc_deeplabv3""",
					    ],
					)

					parser.add_argument(
					    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
					)
					parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)

					UpperCAmelCase_ 							=       parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

					# extract state_dict for VQVAE
					_snake_case													=			{}
					_snake_case													=			'first_stage_model.'
					for key in keys:
										if key.startswith(UpperCamelCase__  ):
															_snake_case													=			state_dict[key]

    # extract state_dict for UNetLDM
					_snake_case													=			{}
					_snake_case													=			'model.diffusion_model.'
					for key in keys:
										if key.startswith(UpperCamelCase__  ):
															_snake_case													=			state_dict[key]

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_snake_case													=			DDIMScheduler(
					    timesteps=config.model.params.timesteps    ,      beta_schedule='scaled_linear'    ,      beta_start=config.model.params.linear_start    ,      beta_end=config.model.params.linear_end    ,      clip_sample=UpperCamelCase__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					parser.add_argument("""--checkpoint_path""", type=str, required=True)
					parser.add_argument("""--config_path""", type=str, required=True)
					parser.add_argument("""--output_path""", type=str, required=True)
					UpperCAmelCase_ 							=       parser.parse_args()

					convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)



