



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()
lowercase_	       =			logging.get_logger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any	)  ->						Optional[int]:
					print("""Loading config file..."""	)

					def flatten_yaml_as_dict(__lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    Dict=""				,   __lowerCamelCase	:    Tuple="."	):
										__lowerCAmelCase	   =[]
										for k, v in d.items():
															__lowerCAmelCase	   =parent_key + sep + k if parent_key else k
															if isinstance(lowercase_				,   collections.abc.MutableMapping	):
																				items.extend(flatten_yaml_as_dict(lowercase_				,   lowercase_				,   sep=lowercase_	).items()	)
															else:
																				items.append((new_key, v)	)
										return dict(lowercase_	)

					__lowerCAmelCase	   =argparse.Namespace()
					with open(lowercase_				,   """r"""	) as yaml_file:
										try:
															__lowerCAmelCase	   =yaml.load(lowercase_				,   Loader=yaml.FullLoader	)

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    List[Any]	)  ->						int:
					__lowerCAmelCase	   =MobileViTVaConfig()

					__lowerCAmelCase	   =False

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

					# orig_config
					__lowerCAmelCase	   =load_orig_config_file(lowercase_	)
					assert getattr(lowercase_				,   """model.classification.name"""				,   -1	) == "mobilevit_v2", "Invalid model"
					__lowerCAmelCase	   =getattr(lowercase_				,   """model.classification.mitv2.width_multiplier"""				,   1.0	)
					assert (
					    getattr(lowercase_				,   """model.classification.mitv2.attn_norm_layer"""				,   -1	) == "layer_norm_2d"
					), "Norm layers other than layer_norm_2d is not supported"
					__lowerCAmelCase	   =getattr(lowercase_				,   """model.classification.activation.name"""				,   """swish"""	)
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										__lowerCAmelCase	   =getattr(lowercase_				,   """model.segmentation.output_stride"""				,   16	)
										if "_deeplabv3" in task_name:
															__lowerCAmelCase	   =getattr(lowercase_				,   """model.segmentation.deeplabv3.aspp_rates"""				,   [12, 24, 36]	)
															__lowerCAmelCase	   =getattr(lowercase_				,   """model.segmentation.deeplabv3.aspp_out_channels"""				,   512	)
															__lowerCAmelCase	   =getattr(lowercase_				,   """model.segmentation.deeplabv3.aspp_dropout"""				,   0.1	)

    # id2label
					__lowerCAmelCase	   ="huggingface/label-files"
					__lowerCAmelCase	   =json.load(open(hf_hub_download(lowercase_				,   lowercase_				,   repo_type="""dataset"""	)				,   """r"""	)	)
					__lowerCAmelCase	   ={int(lowercase_	): v for k, v in idalabel.items()}
					__lowerCAmelCase	   =idalabel
					__lowerCAmelCase	   ={v: k for k, v in idalabel.items()}

					return config






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    Optional[Any]				,   __lowerCamelCase	:    List[Any]	)  ->						List[str]:
					__lowerCAmelCase	   =dct.pop(lowercase_	)
					__lowerCAmelCase	   =val






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    Any=False	)  ->						Optional[int]:

					if base_model:
										__lowerCAmelCase	   =""
					else:
										__lowerCAmelCase	   ="mobilevitv2."

					__lowerCAmelCase	   =[]
					for k in state_dict.keys():
										if k[:8] == "encoder.":
															__lowerCAmelCase	   =k[8:]
										else:
															__lowerCAmelCase	   =k

										if ".block." in k:
															__lowerCAmelCase	   =k_new.replace(""".block."""				,   """."""	)
										if ".conv." in k:
															__lowerCAmelCase	   =k_new.replace(""".conv."""				,   """.convolution."""	)
										if ".norm." in k:
															__lowerCAmelCase	   =k_new.replace(""".norm."""				,   """.normalization."""	)

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

										for i in [3, 4, 5]:
															if f"""layer_{i}.0.""" in k:
																				__lowerCAmelCase	   =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	   =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	   =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	   =[0, 1]
															elif i == 4:
																				__lowerCAmelCase	   =[0, 1, 2, 3]
															elif i == 5:
																				__lowerCAmelCase	   =[0, 1, 2]

															for j in j_in:
																				if f"""layer_{i}.1.global_rep.{j}.""" in k:
																									__lowerCAmelCase	   =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	   =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	   =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	   =k_new.replace("""pre_norm_attn.0."""				,   """layernorm_before."""	)
										if "pre_norm_attn.1." in k:
															__lowerCAmelCase	   =k_new.replace("""pre_norm_attn.1."""				,   """attention."""	)
										if "pre_norm_ffn.0." in k:
															__lowerCAmelCase	   =k_new.replace("""pre_norm_ffn.0."""				,   """layernorm_after."""	)
										if "pre_norm_ffn.1." in k:
															__lowerCAmelCase	   =k_new.replace("""pre_norm_ffn.1."""				,   """ffn.conv1."""	)
										if "pre_norm_ffn.3." in k:
															__lowerCAmelCase	   =k_new.replace("""pre_norm_ffn.3."""				,   """ffn.conv2."""	)

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

										if "seg_head." in k:
															__lowerCAmelCase	   =k_new.replace("""seg_head."""				,   """segmentation_head."""	)
										if ".aspp_layer." in k:
															__lowerCAmelCase	   =k_new.replace(""".aspp_layer."""				,   """."""	)
										if ".aspp_pool." in k:
															__lowerCAmelCase	   =k_new.replace(""".aspp_pool."""				,   """."""	)

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict	)  ->						str:
					__lowerCAmelCase	   =[]
					for k in state_dict.keys():
										if k.startswith("""seg_head.aux_head."""	):
															keys_to_ignore.append(lowercase_	)
					for k in keys_to_ignore:
										state_dict.pop(lowercase_				,   lowercase_	)






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






@torch.no_grad()
def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    List[Any]	)  ->						str:
					__lowerCAmelCase	   =get_mobilevitva_config(lowercase_				,   lowercase_	)

					# load original state_dict
					__lowerCAmelCase	   =torch.load(lowercase_				,   map_location="""cpu"""	)

					# load huggingface model
					if task_name.startswith("""ade20k_"""	) or task_name.startswith("""voc_"""	):
										__lowerCAmelCase	   =MobileViTVaForSemanticSegmentation(lowercase_	).eval()
										__lowerCAmelCase	   =False
					else:
										__lowerCAmelCase	   =MobileViTVaForImageClassification(lowercase_	).eval()
										__lowerCAmelCase	   =False

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

					# load modified state_dict
					model.load_state_dict(lowercase_	)

					# Check outputs on an image, prepared by MobileViTImageProcessor
					__lowerCAmelCase	   =MobileViTImageProcessor(crop_size=config.image_size				,   size=config.image_size + 32	)
					__lowerCAmelCase	   =image_processor(images=prepare_img()				,   return_tensors="""pt"""	)
					__lowerCAmelCase	   =model(**lowercase_	)

					# verify classification model
					if task_name.startswith("""imagenet"""	):
										__lowerCAmelCase	   =outputs.logits
										__lowerCAmelCase	   =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	   =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]				,   lowercase_				,   atol=1E-4	)

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


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

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








def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =int(__lowerCamelCase	)
					if n_element < 1:
										__lowerCAmelCase	   =ValueError("""a should be a positive number"""	)
										raise my_error

					__lowerCAmelCase	   =[1]
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =(0, 0, 0)
					__lowerCAmelCase	   =1
					while index < n_element:
										while hamming_list[i] * 2 <= hamming_list[-1]:
															i += 1
										while hamming_list[j] * 3 <= hamming_list[-1]:
															j += 1
										while hamming_list[k] * 5 <= hamming_list[-1]:
															k += 1
										hamming_list.append(
										    min(hamming_list[i] * 2				,   hamming_list[j] * 3				,   hamming_list[k] * 5	)	)
										index += 1
					return hamming_list


if __name__ == "__main__":
				lowercase_	       =			input('''Enter the last number (nth term) of the Hamming Number Series: ''')
				print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
				lowercase_	       =			hamming(int(n))
				print('''-----------------------------------------------------''')
				print(F"The list with nth numbers is: {hamming_numbers}")
				print('''-----------------------------------------------------''')




