






'''simple docstring'''







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     :      str   					=logging.get_logger(__name__)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[int]:
							print("""Loading config file..."""				)

							def flatten_yaml_as_dict(UpperCamelCase    ,      UpperCamelCase=""    ,      UpperCamelCase="."				):
														lowerCamelCase__ :     Dict	  =	[]
														for k, v in d.items():
																					lowerCamelCase__ :     int	  =	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				)

							lowerCamelCase__ :     List[Any]	  =	argparse.Namespace()
							with open(UpperCamelCase    ,      """r"""				) as yaml_file:
														try:
																					lowerCamelCase__ :     str	  =	yaml.load(UpperCamelCase    ,      Loader=yaml.FullLoader				)

																					lowerCamelCase__ :     int	  =	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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Dict:
							lowerCamelCase__ :     List[Any]	  =	MobileViTVaConfig()

							lowerCamelCase__ :     str	  =	False

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

							# orig_config
							lowerCamelCase__ :     List[Any]	  =	load_orig_config_file(UpperCamelCase				)
							assert getattr(UpperCamelCase    ,      """model.classification.name"""    ,      -1				) == "mobilevit_v2", "Invalid model"
							lowerCamelCase__ :     Any	  =	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"
							lowerCamelCase__ :     Union[str, Any]	  =	getattr(UpperCamelCase    ,      """model.classification.activation.name"""    ,      """swish"""				)
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														lowerCamelCase__ :     List[Any]	  =	getattr(UpperCamelCase    ,      """model.segmentation.output_stride"""    ,      16				)
														if "_deeplabv3" in task_name:
																					lowerCamelCase__ :     str	  =	getattr(UpperCamelCase    ,      """model.segmentation.deeplabv3.aspp_rates"""    ,      [12, 24, 36]				)
																					lowerCamelCase__ :     Dict	  =	getattr(UpperCamelCase    ,      """model.segmentation.deeplabv3.aspp_out_channels"""    ,      512				)
																					lowerCamelCase__ :     List[Any]	  =	getattr(UpperCamelCase    ,      """model.segmentation.deeplabv3.aspp_dropout"""    ,      0.1				)

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

							return config





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     Optional[int]	  =	dct.pop(UpperCamelCase				)
							lowerCamelCase__ :     Dict	  =	val





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							if base_model:
														lowerCamelCase__ :     Tuple	  =	""""""
							else:
														lowerCamelCase__ :     Dict	  =	"""mobilevitv2."""

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

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

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

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

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

														if "classifier.1." in k:
																					lowerCamelCase__ :     List[str]	  =	k_new.replace("""classifier.1."""    ,      """classifier."""				)

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

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





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





@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Optional[Any]:
							lowerCamelCase__ :     Optional[int]	  =	get_mobilevitva_config(UpperCamelCase    ,      UpperCamelCase				)

							# load original state_dict
							lowerCamelCase__ :     int	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)

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

							# remove and rename some keys of load the original model
							lowerCamelCase__ :     str	  =	checkpoint
							remove_unused_keys(UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	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
							lowerCamelCase__ :     List[str]	  =	MobileViTImageProcessor(crop_size=config.image_size    ,      size=config.image_size + 32				)
							lowerCamelCase__ :     List[str]	  =	image_processor(images=prepare_img()    ,      return_tensors="""pt"""				)
							lowerCamelCase__ :     Union[str, Any]	  =	model(**UpperCamelCase				)

							# verify classification model
							if task_name.startswith("""imagenet"""				):
														lowerCamelCase__ :     Dict	  =	outputs.logits
														lowerCamelCase__ :     Optional[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
																					lowerCamelCase__ :     List[Any]	  =	torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-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__":
			_A     :      Optional[int]   					=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.'''
			)

			_A     :      Optional[Any]   					=parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)









'''simple docstring'''







from math import asin, atan, cos, radians, sin, sqrt, tan

_A     :      List[str]   					=637_8137.0
_A     :      Dict   					=635_6752.31_4245
_A     :      int   					=6_378_137





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	float:
							lowerCamelCase__ :     str	  =	(AXIS_A - AXIS_B) / AXIS_A
							lowerCamelCase__ :     Dict	  =	atan((1 - flattening) * tan(radians(UpperCamelCase				)				)				)
							lowerCamelCase__ :     Dict	  =	atan((1 - flattening) * tan(radians(UpperCamelCase				)				)				)
							lowerCamelCase__ :     Optional[Any]	  =	radians(UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	radians(UpperCamelCase				)
							# Equation
							lowerCamelCase__ :     Tuple	  =	sin((phi_a - phi_a) / 2				)
							lowerCamelCase__ :     List[Any]	  =	sin((lambda_a - lambda_a) / 2				)
							# Square both values
							sin_sq_phi *= sin_sq_phi
							sin_sq_lambda *= sin_sq_lambda
							lowerCamelCase__ :     Tuple	  =	sqrt(sin_sq_phi + (cos(UpperCamelCase				) * cos(UpperCamelCase				) * sin_sq_lambda)				)
							return 2 * RADIUS * asin(UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

