





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()
UpperCAmelCase						:   Dict			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					print('Loading config file...'  )

					def flatten_yaml_as_dict(a					,						a=""					,						a="."  ):
										__A	:						Tuple       =				[]
										for k, v in d.items():
															__A	:						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  )

					__A	:						Optional[int]       =				argparse.Namespace()
					with open(a					,						'r'  ) as yaml_file:
										try:
															__A	:						Dict       =				yaml.load(a					,						Loader=yaml.FullLoader  )

															__A	:						Union[str, Any]       =				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  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      List[str]:
					__A	:						Optional[int]       =				MobileViTVaConfig()

					__A	:						Dict       =				False

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

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

					if is_segmentation_model:
										__A	:						Union[str, Any]       =				getattr(a					,						'model.segmentation.output_stride'					,						16  )
										if "_deeplabv3" in task_name:
															__A	:						Optional[Any]       =				getattr(a					,						'model.segmentation.deeplabv3.aspp_rates'					,						[12, 24, 36]  )
															__A	:						str       =				getattr(a					,						'model.segmentation.deeplabv3.aspp_out_channels'					,						5_12  )
															__A	:						Any       =				getattr(a					,						'model.segmentation.deeplabv3.aspp_dropout'					,						0.1  )

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

					return config







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Any:
					__A	:						List[str]       =				dct.pop(a  )
					__A	:						Optional[int]       =				val







def  _SCREAMING_SNAKE_CASE				(   a					,						a=False  )      ->      Optional[int]:
					if base_model:
										__A	:						str       =				''
					else:
										__A	:						Any       =				'mobilevitv2.'

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

										if ".block." in k:
															__A	:						Any       =				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	:						List[str]       =				k_new.replace('conv_1.'					,						F"""{model_prefix}conv_stem."""  )
										for i in [1, 2]:
															if F"""layer_{i}.""" in k:
																				__A	:						Any       =				k_new.replace(F"""layer_{i}."""					,						F"""{model_prefix}encoder.layer.{i-1}.layer."""  )
										if ".exp_1x1." in k:
															__A	:						Tuple       =				k_new.replace('.exp_1x1.'					,						'.expand_1x1.'  )
										if ".red_1x1." in k:
															__A	:						Tuple       =				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	:						int       =				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	:						Optional[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	:						List[str]       =				[0, 1]
															elif i == 4:
																				__A	:						Dict       =				[0, 1, 2, 3]
															elif i == 5:
																				__A	:						str       =				[0, 1, 2]

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

										if "classifier.1." in k:
															__A	:						str       =				k_new.replace('classifier.1.'					,						'classifier.'  )

										if "seg_head." in k:
															__A	:						str       =				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	:						Optional[int]       =				k_new.replace('.aspp_pool.'					,						'.'  )

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







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Optional[Any]:
					__A	:						Optional[Any]       =				[]
					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  _SCREAMING_SNAKE_CASE				(   )      ->      List[str]:
					__A	:						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"
					__A	:						List[Any]       =				Image.open(requests.get(a					,						stream=a  ).raw  )
					return im







@torch.no_grad()
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Union[str, Any]:
					__A	:						int       =				get_mobilevitva_config(a					,						a  )

					# load original state_dict
					__A	:						Tuple       =				torch.load(a					,						map_location='cpu'  )

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

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

					# verify classification model
					if task_name.startswith('imagenet'  ):
										__A	:						Optional[int]       =				outputs.logits
										__A	:						int       =				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	:						List[Any]       =				torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]  )
															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__":
				UpperCAmelCase						:   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 . '''
				        '''
                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.'''
				)

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











import math







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      list[int]:
					__A	:						List[str]       =				[]
					__A	:						Any       =				2
					__A	:						Union[str, Any]       =				int(math.sqrt(a  )  )  # Size of every segment
					__A	:						Any       =				[True] * (end + 1)
					__A	:						List[Any]       =				[]

					while start <= end:
										if temp[start] is True:
															in_prime.append(a  )
															for i in range(start * start					,						end + 1					,						a  ):
																				__A	:						Optional[int]       =				False
										start += 1
					prime += in_prime

					__A	:						Any       =				end + 1
					__A	:						Any       =				min(2 * end					,						a  )

					while low <= n:
										__A	:						List[Any]       =				[True] * (high - low + 1)
										for each in in_prime:
															__A	:						List[str]       =				math.floor(low / each  ) * each
															if t < low:
																				t += each

															for j in range(a					,						high + 1					,						a  ):
																				__A	:						Optional[int]       =				False

										for j in range(len(a  )  ):
															if temp[j] is True:
																				prime.append(j + low  )

										__A	:						Optional[int]       =				high + 1
										__A	:						Tuple       =				min(high + end					,						a  )

					return prime




print(sieve(10**6))




