




'''simple docstring'''







from math import factorial

_A			:    dict[str, int]					  ={str(digit): factorial(digit) for digit in range(10)}





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Tuple:
							if not isinstance(__lowerCAmelCase       ,    __lowerCAmelCase   ):
														raise TypeError("""Parameter number must be int"""   )

							if number < 0:
														raise ValueError("""Parameter number must be greater than or equal to 0"""   )

							# Converts number in string to iterate on its digits and adds its factorial.
							return sum(DIGIT_FACTORIAL[digit] for digit in str(__lowerCAmelCase   )   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase = 60       ,    UpperCamelCase = 1000000   )   -> Optional[Any]:

							if not isinstance(__lowerCAmelCase       ,    __lowerCAmelCase   ) or not isinstance(__lowerCAmelCase       ,    __lowerCAmelCase   ):
														raise TypeError("""Parameters chain_length and number_limit must be int"""   )

							if chain_length <= 0 or number_limit <= 0:
														raise ValueError(
														    """Parameters chain_length and number_limit must be greater than 0"""   )

							# the counter for the chains with the exact desired length
							lowerCamelCase__      :			int 						= 0
							# the cached sizes of the previous chains
							lowerCamelCase__      :			Union[str, Any] 						= {}

							for start_chain_element in range(1       ,    __lowerCAmelCase   ):
														# The temporary set will contain the elements of the chain
														lowerCamelCase__      :			int 						= set()
														lowerCamelCase__      :			Union[str, Any] 						= 0

														# Stop computing the chain when you find a cached size, a repeating item or the
														# length is greater then the desired one.
														lowerCamelCase__      :			Any 						= start_chain_element
														while (
														    chain_element not in chain_sets_lengths
														    and chain_element not in chain_set
														    and chain_set_length <= chain_length
														):
																					chain_set.add(__lowerCAmelCase   )
																					chain_set_length += 1
																					lowerCamelCase__      :			str 						= digit_factorial_sum(__lowerCAmelCase   )

														if chain_element in chain_sets_lengths:
																					chain_set_length += chain_sets_lengths[chain_element]

														lowerCamelCase__      :			List[str] 						= chain_set_length

														# If chain contains the exact amount of elements increase the counter
														if chain_set_length == chain_length:
																					chains_counter += 1

							return chains_counter


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(F'{solution()}')









'''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			:    int					  =logging.get_logger(__name__)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							print("""Loading config file..."""   )

							def flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase="."   ):
														lowerCamelCase__      :			Optional[int] 						= []
														for k, v in d.items():
																					lowerCamelCase__      :			Optional[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__      :			Any 						= argparse.Namespace()
							with open(UpperCamelCase       ,    """r"""   ) as yaml_file:
														try:
																					lowerCamelCase__      :			int 						= yaml.load(UpperCamelCase       ,    Loader=yaml.FullLoader   )

																					lowerCamelCase__      :			Tuple 						= 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   )   -> Optional[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaConfig()

							lowerCamelCase__      :			str 						= False

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

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

							if is_segmentation_model:
														lowerCamelCase__      :			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__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_out_channels"""       ,    512   )
																					lowerCamelCase__      :			List[Any] 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_dropout"""       ,    0.1   )

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

							return config





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





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

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

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

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

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

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

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

														if "seg_head." in k:
																					lowerCamelCase__      :			Optional[int] 						= k_new.replace("""seg_head."""       ,    """segmentation_head."""   )
														if ".aspp_layer." in k:
																					lowerCamelCase__      :			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   )   -> List[str]:
							lowerCamelCase__      :			Union[str, Any] 						= []
							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_ ()   -> Dict:
							lowerCamelCase__      :			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"
							lowerCamelCase__      :			Tuple 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							return im





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			str 						= get_mobilevitva_config(UpperCamelCase       ,    UpperCamelCase   )

							# load original state_dict
							lowerCamelCase__      :			List[str] 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )

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

							# remove and rename some keys of load the original model
							lowerCamelCase__      :			Tuple 						= checkpoint
							remove_unused_keys(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, 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__      :			int 						= MobileViTImageProcessor(crop_size=config.image_size       ,    size=config.image_size + 32   )
							lowerCamelCase__      :			Dict 						= image_processor(images=prepare_img()       ,    return_tensors="""pt"""   )
							lowerCamelCase__      :			str 						= 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__      :			Optional[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			:    Dict					  =parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)

