

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()
a_      =     logging.get_logger(__name__)

def 			lowerCamelCase__			(       _a):
	print("Loading config file...")

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

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							argparse.Namespace()
	with open(_a				,      "r") as yaml_file:
		try:
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							yaml.load(_a				,      Loader=yaml.FullLoader)

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

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							MobileViTVaConfig()

	SCREAMING_SNAKE_CASE :   int					  =							False

	# dataset
	if task_name.startswith("imagenet1k_"):
		SCREAMING_SNAKE_CASE :   str					  =							1000
		if int(task_name.strip().split("_")[-1]) == 384:
			SCREAMING_SNAKE_CASE :   Dict					  =							384
		else:
			SCREAMING_SNAKE_CASE :   Tuple					  =							256
		SCREAMING_SNAKE_CASE :   str					  =							"imagenet-1k-id2label.json"
	elif task_name.startswith("imagenet21k_to_1k_"):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							21000
		if int(task_name.strip().split("_")[-1]) == 384:
			SCREAMING_SNAKE_CASE :   List[Any]					  =							384
		else:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							256
		SCREAMING_SNAKE_CASE :   List[Any]					  =							"imagenet-22k-id2label.json"
	elif task_name.startswith("ade20k_"):
		SCREAMING_SNAKE_CASE :   str					  =							151
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							512
		SCREAMING_SNAKE_CASE :   str					  =							"ade20k-id2label.json"
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
	elif task_name.startswith("voc_"):
		SCREAMING_SNAKE_CASE :   Dict					  =							21
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							512
		SCREAMING_SNAKE_CASE :   Dict					  =							"pascal-voc-id2label.json"
		SCREAMING_SNAKE_CASE :   Tuple					  =							True

	# orig_config
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_orig_config_file(_a)
	assert getattr(_a				,      "model.classification.name"				,      -1) == "mobilevit_v2", "Invalid model"
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							getattr(_a				,      "model.classification.mitv2.width_multiplier"				,      1.0)
	assert (
	    getattr(_a				,      "model.classification.mitv2.attn_norm_layer"				,      -1) == "layer_norm_2d"
	), "Norm layers other than layer_norm_2d is not supported"
	SCREAMING_SNAKE_CASE :   List[Any]					  =							getattr(_a				,      "model.classification.activation.name"				,      "swish")
	# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

	if is_segmentation_model:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							getattr(_a				,      "model.segmentation.output_stride"				,      16)
		if "_deeplabv3" in task_name:
			SCREAMING_SNAKE_CASE :   int					  =							getattr(_a				,      "model.segmentation.deeplabv3.aspp_rates"				,      [12, 24, 36])
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							getattr(_a				,      "model.segmentation.deeplabv3.aspp_out_channels"				,      512)
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							getattr(_a				,      "model.segmentation.deeplabv3.aspp_dropout"				,      0.1)

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

	return config

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Tuple					  =							dct.pop(_a)
	SCREAMING_SNAKE_CASE :   int					  =							val

def 			lowerCamelCase__			(       _a				,      _a=False):
	if base_model:
		SCREAMING_SNAKE_CASE :   int					  =							""
	else:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							"mobilevitv2."

	SCREAMING_SNAKE_CASE :   int					  =							[]
	for k in state_dict.keys():
		if k[:8] == "encoder.":
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							k[8:]
		else:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							k

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

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

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

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

		if "classifier.1." in k:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							k_new.replace("classifier.1."				,      "classifier.")

		if "seg_head." in k:
			SCREAMING_SNAKE_CASE :   List[Any]					  =							k_new.replace("seg_head."				,      "segmentation_head.")
		if ".aspp_layer." in k:
			SCREAMING_SNAKE_CASE :   int					  =							k_new.replace(".aspp_layer."				,      ".")
		if ".aspp_pool." in k:
			SCREAMING_SNAKE_CASE :   Tuple					  =							k_new.replace(".aspp_pool."				,      ".")

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

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   List[str]					  =							[]
	for k in state_dict.keys():
		if k.startswith("seg_head.aux_head."):
			keys_to_ignore.append(_a)
	for k in keys_to_ignore:
		state_dict.pop(_a				,      _a)

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							"http://images.cocodataset.org/val2017/000000039769.jpg"
	# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
	SCREAMING_SNAKE_CASE :   Tuple					  =							Image.open(requests.get(_a				,      stream=_a).raw)
	return im

@torch.no_grad()
def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							get_mobilevitva_config(_a				,      _a)

	# load original state_dict
	SCREAMING_SNAKE_CASE :   Any					  =							torch.load(_a				,      map_location="cpu")

	# load huggingface model
	if task_name.startswith("ade20k_") or task_name.startswith("voc_"):
		SCREAMING_SNAKE_CASE :   List[str]					  =							MobileViTVaForSemanticSegmentation(_a).eval()
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							False
	else:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							MobileViTVaForImageClassification(_a).eval()
		SCREAMING_SNAKE_CASE :   str					  =							False

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

	# load modified state_dict
	model.load_state_dict(_a)

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

	# verify classification model
	if task_name.startswith("imagenet"):
		SCREAMING_SNAKE_CASE :   Any					  =							outputs.logits
		SCREAMING_SNAKE_CASE :   Dict					  =							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
			SCREAMING_SNAKE_CASE :   Any					  =							torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01])
			assert torch.allclose(logits[0, :3]				,      _a				,      atol=1E-4)

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


if __name__ == "__main__":
			a_      =     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.'
			)

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


from __future__ import annotations

import math






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			int						)				->							None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							size
				# approximate the overall size of segment tree with given value
				SCREAMING_SNAKE_CASE :   Any					  =							[0 for i in range(0   ,   4 * size						)]
				# create array to store lazy update
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[0 for i in range(0   ,   4 * size						)]
				SCREAMING_SNAKE_CASE :   Any					  =							[0 for i in range(0   ,   4 * size						)]  # flag for lazy update
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return idx * 2
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			int						)				->							int:

				"""simple docstring"""

				return idx * 2 + 1
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			list[int]						)				->							None:

				"""simple docstring"""

				if left_element == right_element:
					SCREAMING_SNAKE_CASE :   int					  =							a[left_element - 1]
				else:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							(left_element + right_element) // 2
					self.build(self.left(a						)   ,   a   ,   a   ,   a						)
					self.build(self.right(a						)   ,   mid + 1   ,   a   ,   a						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							max(
					    self.segment_tree[self.left(a						)]   ,   self.segment_tree[self.right(a						)]						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int						)				->							bool:

				"""simple docstring"""

				if self.flag[idx] is True:
					SCREAMING_SNAKE_CASE :   Any					  =							self.lazy[idx]
					SCREAMING_SNAKE_CASE :   List[str]					  =							False
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   int					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Any					  =							True
						SCREAMING_SNAKE_CASE :   List[Any]					  =							True

				if right_element < a or left_element > b:
					return True
				if left_element >= a and right_element <= b:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							val
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   str					  =							val
						SCREAMING_SNAKE_CASE :   str					  =							val
						SCREAMING_SNAKE_CASE :   Tuple					  =							True
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
					return True
				SCREAMING_SNAKE_CASE :   int					  =							(left_element + right_element) // 2
				self.update(self.left(a						)   ,   a   ,   a   ,   a   ,   a   ,   a						)
				self.update(self.right(a						)   ,   mid + 1   ,   a   ,   a   ,   a   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max(
				    self.segment_tree[self.left(a						)]   ,   self.segment_tree[self.right(a						)]						)
				return True
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int   ,   a				:			int						)				->							int | float:

				"""simple docstring"""

				if self.flag[idx] is True:
					SCREAMING_SNAKE_CASE :   int					  =							self.lazy[idx]
					SCREAMING_SNAKE_CASE :   List[Any]					  =							False
					if left_element != right_element:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.lazy[idx]
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
				if right_element < a or left_element > b:
					return -math.inf
				if left_element >= a and right_element <= b:
					return self.segment_tree[idx]
				SCREAMING_SNAKE_CASE :   Dict					  =							(left_element + right_element) // 2
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.query(self.left(a						)   ,   a   ,   a   ,   a   ,   a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.query(self.right(a						)   ,   mid + 1   ,   a   ,   a   ,   a						)
				return max(a   ,   a						)






			def __str__(      self				:			str						)				->							str:

				"""simple docstring"""

				return str([self.query(1   ,   1   ,   self.size   ,   a   ,   a						) for i in range(1   ,   self.size + 1						)]						)


if __name__ == "__main__":
			a_      =     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			a_      =     15
			a_      =     SegmentTree(size)
			segt.build(1, 1, size, A)
			print(segt.query(1, 1, size, 4, 6))
			print(segt.query(1, 1, size, 7, 11))
			print(segt.query(1, 1, size, 7, 12))
			segt.update(1, 1, size, 1, 3, 111)
			print(segt.query(1, 1, size, 1, 15))
			segt.update(1, 1, size, 7, 8, 235)
			print(segt)