


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()
_lowercase       :      Tuple	=	logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   Dict:





	"""simple docstring"""







	print("""Loading config file..."""						)

	def flatten_yaml_as_dict(UpperCamelCase__:     Dict							,					UpperCamelCase__:     List[Any]=""							,					UpperCamelCase__:     int="."						):
		A								=				[]
		for k, v in d.items():
			A								=				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__						)

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

			A								=				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__:     Tuple							,					UpperCamelCase__:     Any						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				MobileViTVaConfig()

	A								=				False

	# dataset
	if task_name.startswith("""imagenet1k_"""						):
		A								=				10_00
		if int(task_name.strip().split("""_"""						)[-1]						) == 3_84:
			A								=				3_84
		else:
			A								=				2_56
		A								=				"""imagenet-1k-id2label.json"""
	elif task_name.startswith("""imagenet21k_to_1k_"""						):
		A								=				2_10_00
		if int(task_name.strip().split("""_"""						)[-1]						) == 3_84:
			A								=				3_84
		else:
			A								=				2_56
		A								=				"""imagenet-22k-id2label.json"""
	elif task_name.startswith("""ade20k_"""						):
		A								=				1_51
		A								=				5_12
		A								=				"""ade20k-id2label.json"""
		A								=				True
	elif task_name.startswith("""voc_"""						):
		A								=				21
		A								=				5_12
		A								=				"""pascal-voc-id2label.json"""
		A								=				True

	# orig_config
	A								=				load_orig_config_file(UpperCamelCase__						)
	assert getattr(UpperCamelCase__							,					"""model.classification.name"""							,					-1						) == "mobilevit_v2", "Invalid model"
	A								=				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"
	A								=				getattr(UpperCamelCase__							,					"""model.classification.activation.name"""							,					"""swish"""						)
	# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

	if is_segmentation_model:
		A								=				getattr(UpperCamelCase__							,					"""model.segmentation.output_stride"""							,					16						)
		if "_deeplabv3" in task_name:
			A								=				getattr(UpperCamelCase__							,					"""model.segmentation.deeplabv3.aspp_rates"""							,					[12, 24, 36]						)
			A								=				getattr(UpperCamelCase__							,					"""model.segmentation.deeplabv3.aspp_out_channels"""							,					5_12						)
			A								=				getattr(UpperCamelCase__							,					"""model.segmentation.deeplabv3.aspp_dropout"""							,					0.1						)

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

	return config





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





	"""simple docstring"""







	A								=				dct.pop(UpperCamelCase__						)
	A								=				val





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Tuple=False						) ->   List[Any]:





	"""simple docstring"""







	if base_model:
		A								=				""""""
	else:
		A								=				"""mobilevitv2."""

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

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

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

		for i in [3, 4, 5]:
			if f'layer_{i}.0.' in k:
				A								=				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:
				A								=				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:
				A								=				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:
				A								=				[0, 1]
			elif i == 4:
				A								=				[0, 1, 2, 3]
			elif i == 5:
				A								=				[0, 1, 2]

			for j in j_in:
				if f'layer_{i}.1.global_rep.{j}.' in k:
					A								=				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:
				A								=				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:
				A								=				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:
			A								=				k_new.replace("""pre_norm_attn.0."""							,					"""layernorm_before."""						)
		if "pre_norm_attn.1." in k:
			A								=				k_new.replace("""pre_norm_attn.1."""							,					"""attention."""						)
		if "pre_norm_ffn.0." in k:
			A								=				k_new.replace("""pre_norm_ffn.0."""							,					"""layernorm_after."""						)
		if "pre_norm_ffn.1." in k:
			A								=				k_new.replace("""pre_norm_ffn.1."""							,					"""ffn.conv1."""						)
		if "pre_norm_ffn.3." in k:
			A								=				k_new.replace("""pre_norm_ffn.3."""							,					"""ffn.conv2."""						)

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

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   str:





	"""simple docstring"""







	A								=				[]
	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"""







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





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





	"""simple docstring"""







	A								=				get_mobilevitva_config(UpperCamelCase__							,					UpperCamelCase__						)

	# load original state_dict
	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)

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

	# remove and rename some keys of load the original model
	A								=				checkpoint
	remove_unused_keys(UpperCamelCase__						)
	A								=				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
	A								=				MobileViTImageProcessor(crop_size=config.image_size							,					size=config.image_size + 32						)
	A								=				image_processor(images=prepare_img()							,					return_tensors="""pt"""						)
	A								=				model(**UpperCamelCase__						)

	# verify classification model
	if task_name.startswith("""imagenet"""						):
		A								=				outputs.logits
		A								=				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
			A								=				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__":
				_lowercase       :      Optional[Any]	=	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 . "
				        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
				    ),
				    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."
				)

				_lowercase       :      Dict	=	parser.parse_args()
				convert_mobilevitva_checkpoint(
				    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
				)







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



