



'''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()
__lowerCAmelCase									=   logging.get_logger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		print('Loading config file...'  )

		def flatten_yaml_as_dict(lowerCAmelCase_						,			lowerCAmelCase_=""						,			lowerCAmelCase_="."  ):
				_a     :      Optional[int]        =							[]
				for k, v in d.items():
						_a     :      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  )

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

						_a     :      Dict        =							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     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      List[Any]        =							MobileViTVaConfig()

		_a     :      Optional[int]        =							False

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

		# orig_config
		_a     :      int        =							load_orig_config_file(__UpperCAmelCase  )
		assert getattr(__UpperCAmelCase						,			'model.classification.name'						,			-1  ) == "mobilevit_v2", "Invalid model"
		_a     :      int        =							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     :      Dict        =							getattr(__UpperCAmelCase						,			'model.classification.activation.name'						,			'swish'  )
		# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

		return config



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		_a     :      str        =							dct.pop(__UpperCAmelCase  )
		_a     :      List[str]        =							val



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      List[str]:
		if base_model:
				_a     :      Any        =							''''''
		else:
				_a     :      List[Any]        =							'''mobilevitv2.'''

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

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

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

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

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

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

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

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



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



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      int        =							get_mobilevitva_config(__UpperCAmelCase						,			__UpperCAmelCase  )

		# load original state_dict
		_a     :      Optional[Any]        =							torch.load(__UpperCAmelCase						,			map_location='cpu'  )

		# load huggingface model
		if task_name.startswith('ade20k_'  ) or task_name.startswith('voc_'  ):
				_a     :      Optional[Any]        =							MobileViTVaForSemanticSegmentation(__UpperCAmelCase  ).eval()
				_a     :      Dict        =							False
		else:
				_a     :      Any        =							MobileViTVaForImageClassification(__UpperCAmelCase  ).eval()
				_a     :      Any        =							False

		# remove and rename some keys of load the original model
		_a     :      Optional[int]        =							checkpoint
		remove_unused_keys(__UpperCAmelCase  )
		_a     :      List[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
		_a     :      Tuple        =							MobileViTImageProcessor(crop_size=config.image_size						,			size=config.image_size + 32  )
		_a     :      Optional[int]        =							image_processor(images=prepare_img()						,			return_tensors='pt'  )
		_a     :      Tuple        =							model(**__UpperCAmelCase  )

		# verify classification model
		if task_name.startswith('imagenet'  ):
				_a     :      Union[str, Any]        =							outputs.logits
				_a     :      Tuple        =							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     :      Any        =							torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-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__":
							__lowerCAmelCase									=   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.'''
							)

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











from math import factorial




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	int:

    # If either of the conditions are true, the function is being asked
    # to calculate a factorial of a negative number, which is not possible
    if n < k or k < 0:
        raise ValueError('''Please enter positive integers for n and k where n >= k'''      )
    return factorial(__UpperCAmelCase      ) // (factorial(__UpperCAmelCase      ) * factorial(n - k      ))


if __name__ == "__main__":
  print(
      'The number of five-card hands possible from a standard',
      F'''fifty-two card deck is: {combinations(52, 5)}\n''',
  )

  print(
      'If a class of 40 students must be arranged into groups of',
      F'''4 for group projects, there are {combinations(40, 4)} ways''',
      'to arrange them.\n',
  )

  print(
      'If 10 teams are competing in a Formula One race, there',
      F'''are {combinations(10, 3)} ways that first, second and''',
      'third place can be awarded.',
  )

