





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:	Tuple = logging.get_logger(__name__)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]			)   ->							Optional[Any]:




		'''simple docstring'''
		print('Loading config file...'			)

		def flatten_yaml_as_dict(_lowercase     : List[Any]   ,    _lowercase     : str=""   ,    _lowercase     : Union[str, Any]="."			):
				lowercase__					:      Union[str, Any]      =		[]
				for k, v in d.items():
						lowercase__					:      Optional[Any]      =		parent_key + sep + k if parent_key else k
						if isinstance(_lowercase   ,    collections.abc.MutableMapping			):
								items.extend(flatten_yaml_as_dict(_lowercase   ,    _lowercase   ,    sep=_lowercase			).items()			)
						else:
								items.append((new_key, v)			)
				return dict(_lowercase			)

		lowercase__					:      Union[str, Any]      =		argparse.Namespace()
		with open(_lowercase   ,    'r'			) as yaml_file:
				try:
						lowercase__					:      Union[str, Any]      =		yaml.load(_lowercase   ,    Loader=yaml.FullLoader			)

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Tuple			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		MobileViTVaConfig()

		lowercase__					:      List[Any]      =		False

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

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

		if is_segmentation_model:
				lowercase__					:      Tuple      =		getattr(_lowercase   ,    'model.segmentation.output_stride'   ,    16			)
				if "_deeplabv3" in task_name:
						lowercase__					:      Any      =		getattr(_lowercase   ,    'model.segmentation.deeplabv3.aspp_rates'   ,    [12, 24, 36]			)
						lowercase__					:      Optional[Any]      =		getattr(_lowercase   ,    'model.segmentation.deeplabv3.aspp_out_channels'   ,    512			)
						lowercase__					:      Optional[int]      =		getattr(_lowercase   ,    'model.segmentation.deeplabv3.aspp_dropout'   ,    0.1			)

    # id2label
		lowercase__					:      List[Any]      =		'huggingface/label-files'
		lowercase__					:      Optional[int]      =		json.load(open(hf_hub_download(_lowercase   ,    _lowercase   ,    repo_type='dataset'			)   ,    'r'			)			)
		lowercase__					:      Any      =		{int(_lowercase			): v for k, v in idalabel.items()}
		lowercase__					:      int      =		idalabel
		lowercase__					:      Tuple      =		{v: k for k, v in idalabel.items()}

		return config


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str   ,    _lowercase     : Any   ,    _lowercase     : List[Any]			)   ->							Any:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		dct.pop(_lowercase			)
		lowercase__					:      int      =		val


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Dict=False			)   ->							List[Any]:




		'''simple docstring'''
		if base_model:
				lowercase__					:      Optional[int]      =		''
		else:
				lowercase__					:      str      =		'mobilevitv2.'

		lowercase__					:      str      =		[]
		for k in state_dict.keys():
				if k[:8] == "encoder.":
						lowercase__					:      List[str]      =		k[8:]
				else:
						lowercase__					:      Any      =		k

				if ".block." in k:
						lowercase__					:      Tuple      =		k_new.replace('.block.'   ,    '.'			)
				if ".conv." in k:
						lowercase__					:      List[Any]      =		k_new.replace('.conv.'   ,    '.convolution.'			)
				if ".norm." in k:
						lowercase__					:      Optional[Any]      =		k_new.replace('.norm.'   ,    '.normalization.'			)

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

				for i in [3, 4, 5]:
						if f"""layer_{i}.0.""" in k:
								lowercase__					:      Union[str, Any]      =		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:
								lowercase__					:      int      =		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:
								lowercase__					:      Optional[Any]      =		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:
								lowercase__					:      int      =		[0, 1]
						elif i == 4:
								lowercase__					:      List[Any]      =		[0, 1, 2, 3]
						elif i == 5:
								lowercase__					:      Dict      =		[0, 1, 2]

						for j in j_in:
								if f"""layer_{i}.1.global_rep.{j}.""" in k:
										lowercase__					:      Union[str, Any]      =		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:
								lowercase__					:      Optional[int]      =		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:
								lowercase__					:      Optional[Any]      =		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:
						lowercase__					:      Optional[Any]      =		k_new.replace('pre_norm_attn.0.'   ,    'layernorm_before.'			)
				if "pre_norm_attn.1." in k:
						lowercase__					:      str      =		k_new.replace('pre_norm_attn.1.'   ,    'attention.'			)
				if "pre_norm_ffn.0." in k:
						lowercase__					:      str      =		k_new.replace('pre_norm_ffn.0.'   ,    'layernorm_after.'			)
				if "pre_norm_ffn.1." in k:
						lowercase__					:      str      =		k_new.replace('pre_norm_ffn.1.'   ,    'ffn.conv1.'			)
				if "pre_norm_ffn.3." in k:
						lowercase__					:      List[Any]      =		k_new.replace('pre_norm_ffn.3.'   ,    'ffn.conv2.'			)

				if "classifier.1." in k:
						lowercase__					:      Union[str, Any]      =		k_new.replace('classifier.1.'   ,    'classifier.'			)

				if "seg_head." in k:
						lowercase__					:      List[Any]      =		k_new.replace('seg_head.'   ,    'segmentation_head.'			)
				if ".aspp_layer." in k:
						lowercase__					:      List[str]      =		k_new.replace('.aspp_layer.'   ,    '.'			)
				if ".aspp_pool." in k:
						lowercase__					:      Optional[int]      =		k_new.replace('.aspp_pool.'   ,    '.'			)

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      Any      =		[]
		for k in state_dict.keys():
				if k.startswith('seg_head.aux_head.'			):
						keys_to_ignore.append(_lowercase			)
		for k in keys_to_ignore:
				state_dict.pop(_lowercase   ,    _lowercase			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      List[str]      =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
		lowercase__					:      Any      =		Image.open(requests.get(_lowercase   ,    stream=_lowercase			).raw			)
		return im


@torch.no_grad()
def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str   ,    _lowercase     : Any   ,    _lowercase     : List[Any]   ,    _lowercase     : Tuple			)   ->							Any:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		get_mobilevitva_config(_lowercase   ,    _lowercase			)

		# load original state_dict
		lowercase__					:      Optional[int]      =		torch.load(_lowercase   ,    map_location='cpu'			)

		# load huggingface model
		if task_name.startswith('ade20k_'			) or task_name.startswith('voc_'			):
				lowercase__					:      int      =		MobileViTVaForSemanticSegmentation(_lowercase			).eval()
				lowercase__					:      Any      =		False
		else:
				lowercase__					:      Any      =		MobileViTVaForImageClassification(_lowercase			).eval()
				lowercase__					:      Dict      =		False

		# remove and rename some keys of load the original model
		lowercase__					:      Dict      =		checkpoint
		remove_unused_keys(_lowercase			)
		lowercase__					:      List[str]      =		create_rename_keys(_lowercase   ,    base_model=_lowercase			)
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(_lowercase   ,    _lowercase   ,    _lowercase			)

		# load modified state_dict
		model.load_state_dict(_lowercase			)

		# Check outputs on an image, prepared by MobileViTImageProcessor
		lowercase__					:      List[Any]      =		MobileViTImageProcessor(crop_size=config.image_size   ,    size=config.image_size + 32			)
		lowercase__					:      str      =		image_processor(images=prepare_img()   ,    return_tensors='pt'			)
		lowercase__					:      int      =		model(**_lowercase			)

		# verify classification model
		if task_name.startswith('imagenet'			):
				lowercase__					:      str      =		outputs.logits
				lowercase__					:      Any      =		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
						lowercase__					:      int      =		torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]			)
						assert torch.allclose(logits[0, :3]   ,    _lowercase   ,    atol=1e-4			)

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


if __name__ == "__main__":
			__UpperCamelCase:	List[str] = 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:	Optional[int] = parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)









import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		argparse.ArgumentParser()
		parser.add_argument(
		    '-m'   ,    '--pretrained_model_name_or_path'   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    )
		parser.add_argument(
		    '-c'   ,    '--caption'   ,    type=_lowercase   ,    default='robotic cat with wings'   ,    help='Text used to generate images.'   ,    )
		parser.add_argument(
		    '-n'   ,    '--images_num'   ,    type=_lowercase   ,    default=4   ,    help='How much images to generate.'   ,    )
		parser.add_argument(
		    '-s'   ,    '--seed'   ,    type=_lowercase   ,    default=42   ,    help='Seed for random process.'   ,    )
		parser.add_argument(
		    '-ci'   ,    '--cuda_id'   ,    type=_lowercase   ,    default=0   ,    help='cuda_id.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if not len(_lowercase			) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'			)

		lowercase__   ,				lowercase__					:      Any      =		imgs[0].size
		lowercase__					:      int      =		Image.new('RGB'   ,    size=(cols * w, rows * h)			)
		lowercase__   ,				lowercase__					:      Any      =		grid.size

		for i, img in enumerate(_lowercase			):
				grid.paste(_lowercase   ,    box=(i % cols * w, i // cols * h)			)
		return grid


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




		'''simple docstring'''
		lowercase__					:      int      =		torch.Generator(pipeline.device			).manual_seed(_lowercase			)
		lowercase__					:      Any      =		pipeline(
		    _lowercase   ,    guidance_scale=_lowercase   ,    num_inference_steps=_lowercase   ,    generator=_lowercase   ,    num_images_per_prompt=_lowercase   ,    ).images
		lowercase__					:      Dict      =		int(math.sqrt(_lowercase			)			)
		lowercase__					:      int      =		image_grid(_lowercase   ,    rows=_rows   ,    cols=num_images_per_prompt // _rows			)
		return grid, images


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__UpperCamelCase:	List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__UpperCamelCase:	int = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__UpperCamelCase:	int = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__UpperCamelCase:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
__UpperCamelCase:	List[str] = os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
			image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))


