






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__      			=						logging.get_logger(__name__)







def     a_   (__A      )				->						List[Any]:



					"""simple docstring"""
					print("Loading config file..."      )

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

															__a					:						List[str]      =					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     a_   (__A				,       __A      )				->						Optional[int]:



					"""simple docstring"""
					__a					:						str      =					MobileViTVaConfig()

					__a					:						Any      =					False

					# dataset
					if task_name.startswith("imagenet1k_"      ):
										__a					:						List[Any]      =					1_000
										if int(task_name.strip().split("_"      )[-1]      ) == 384:
															__a					:						int      =					384
										else:
															__a					:						Optional[Any]      =					256
										__a					:						str      =					"imagenet-1k-id2label.json"
					elif task_name.startswith("imagenet21k_to_1k_"      ):
										__a					:						Any      =					21_000
										if int(task_name.strip().split("_"      )[-1]      ) == 384:
															__a					:						str      =					384
										else:
															__a					:						Optional[Any]      =					256
										__a					:						str      =					"imagenet-22k-id2label.json"
					elif task_name.startswith("ade20k_"      ):
										__a					:						Tuple      =					151
										__a					:						List[Any]      =					512
										__a					:						Optional[Any]      =					"ade20k-id2label.json"
										__a					:						List[str]      =					True
					elif task_name.startswith("voc_"      ):
										__a					:						Optional[Any]      =					21
										__a					:						str      =					512
										__a					:						Dict      =					"pascal-voc-id2label.json"
										__a					:						Optional[Any]      =					True

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

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

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

					return config







def     a_   (__A				,       __A				,       __A      )				->						Optional[Any]:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					dct.pop(__A      )
					__a					:						str      =					val







def     a_   (__A				,       __A=False      )				->						str:



					"""simple docstring"""
					if base_model:
										__a					:						List[str]      =					""
					else:
										__a					:						Any      =					"mobilevitv2."

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

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

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

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

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

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

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

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







def     a_   (__A      )				->						Tuple:



					"""simple docstring"""
					__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     a_   ()				->						str:



					"""simple docstring"""
					__a					:						Dict      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
					# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
					__a					:						str      =					Image.open(requests.get(__A				,       stream=__A      ).raw      )
					return im







@torch.no_grad()
def     a_   (__A				,       __A				,       __A				,       __A      )				->						Optional[int]:



					"""simple docstring"""
					__a					:						Dict      =					get_mobilevitva_config(__A				,       __A      )

					# load original state_dict
					__a					:						Dict      =					torch.load(__A				,       map_location="cpu"      )

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

					# remove and rename some keys of load the original model
					__a					:						Tuple      =					checkpoint
					remove_unused_keys(__A      )
					__a					:						str      =					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					:						str      =					MobileViTImageProcessor(crop_size=config.image_size				,       size=config.image_size + 32      )
					__a					:						str      =					image_processor(images=prepare_img()				,       return_tensors="pt"      )
					__a					:						Tuple      =					model(**__A      )

					# verify classification model
					if task_name.startswith("imagenet"      ):
										__a					:						List[Any]      =					outputs.logits
										__a					:						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
															__a					:						Optional[Any]      =					torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]      )
															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__      			=						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__      			=						parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)













def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if not head:
										return True
					# split the list to two parts
					__a      ,			__a					:						Any      =					head.next, head
					while fast and fast.next:
										__a					:						Optional[int]      =					fast.next.next
										__a					:						Optional[int]      =					slow.next
					__a					:						Optional[int]      =					slow.next
					__a					:						Tuple      =					None  # Don't forget here! But forget still works!
					# reverse the second part
					__a					:						Any      =					None
					while second:
										__a					:						int      =					second.next
										__a					:						int      =					node
										__a					:						Union[str, Any]      =					second
										__a					:						Union[str, Any]      =					nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										__a					:						int      =					node.next
										__a					:						Dict      =					head.next
					return True







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					if not head or not head.next:
										return True
					__a					:						Optional[int]      =					{}
					__a					:						Optional[int]      =					0
					while head:
										if head.val in d:
															d[head.val].append(__A      )
										else:
															__a					:						Dict      =					[pos]
										__a					:						Dict      =					head.next
										pos += 1
					__a					:						Dict      =					pos - 1
					__a					:						Optional[int]      =					0
					for v in d.values():
										if len(__A      ) % 2 != 0:
															middle += 1
										else:
															__a					:						Dict      =					0
															for i in range(0				,       len(__A      )      ):
																				if v[i] + v[len(__A      ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True





