
from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
lowercase__							:							Optional[int]									=			Lock()




def 	A_ (							snake_case							:   Tuple ,      snake_case							:   Optional[Any] ,      snake_case							:   Any ,      snake_case							:   Optional[int] ,      snake_case							:   str ,      snake_case							:   Any ,      snake_case							:   Any )      ->    List[Any]:



						'''simple docstring'''

						global process_lock

						# we perform n swaps since after n swaps we know we are sorted
						# we *could* stop early if we are sorted already, but it takes as long to
						# find out we are sorted as it does to sort the list with this algorithm
						for i in range(0 ,      10 ):
												if (i + position) % 2 == 0 and r_send is not None:
																		# send your value to your right neighbor
																		process_lock.acquire()
																		r_send[1].send(snake_case )
																		process_lock.release()

																		# receive your right neighbor's value
																		process_lock.acquire()
																		__UpperCamelCase       =			rr_cv[0].recv()
																		process_lock.release()

																		# take the lower value since you are on the left
																		__UpperCamelCase       =			min(snake_case ,      snake_case )
												elif (i + position) % 2 != 0 and l_send is not None:
																		# send your value to your left neighbor
																		process_lock.acquire()
																		l_send[1].send(snake_case )
																		process_lock.release()

																		# receive your left neighbor's value
																		process_lock.acquire()
																		__UpperCamelCase       =			lr_cv[0].recv()
																		process_lock.release()

																		# take the higher value since you are on the right
																		__UpperCamelCase       =			max(snake_case ,      snake_case )
    # after all swaps are performed, send the values back to main
						result_pipe[1].send(snake_case )




def 	A_ (							snake_case							:   List[Any] )      ->    Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						__UpperCamelCase       =			[]
						# initialize the list of pipes where the values will be retrieved
						for _ in arr:
												result_pipe.append(Pipe() )
						# creates the processes
						# the first and last process only have one neighbor so they are made outside
						# of the loop
						__UpperCamelCase       =			Pipe()
						__UpperCamelCase       =			Pipe()
						process_array_.append(
						    Process(
						        target=snake_case ,      args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,      ) )
						__UpperCamelCase       =			temp_rs
						__UpperCamelCase       =			temp_rr

						for i in range(1 ,      len(snake_case ) - 1 ):
												__UpperCamelCase       =			Pipe()
												__UpperCamelCase       =			Pipe()
												process_array_.append(
												    Process(
												        target=snake_case ,      args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,      ) )
												__UpperCamelCase       =			temp_rs
												__UpperCamelCase       =			temp_rr

						process_array_.append(
						    Process(
						        target=snake_case ,      args=(
						            len(snake_case ) - 1,
						            arr[len(snake_case ) - 1],
						            temp_ls,
						            None,
						            temp_lr,
						            None,
						            result_pipe[len(snake_case ) - 1],
						        ) ,      ) )

						# start the processes
						for p in process_array_:
												p.start()

						# wait for the processes to end and write their values to the list
						for p in range(0 ,      len(snake_case ) ):
												__UpperCamelCase       =			result_pipe[p][0].recv()
												process_array_[p].join()
						return arr




def 	A_ (							)      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			list(range(10 ,      0 ,      -1 ) )
						print('''Initial List''' )
						print(*snake_case )
						__UpperCamelCase       =			odd_even_transposition(snake_case )
						print('''Sorted List\n''' )
						print(*snake_case )


if __name__ == "__main__":
				main()






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__							:							Union[str, Any]									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   List[str] )      ->    List[str]:



						'''simple docstring'''

						print('''Loading config file...''' )

						def flatten_yaml_as_dict(snake_case							:   Optional[int] ,      snake_case							:   List[Any]="" ,      snake_case							:   str="." ):
												__UpperCamelCase       =			[]
												for k, v in d.items():
																		__UpperCamelCase       =			parent_key + sep + k if parent_key else k
																		if isinstance(snake_case ,      collections.abc.MutableMapping ):
																								items.extend(flatten_yaml_as_dict(snake_case ,      snake_case ,      sep=snake_case ).items() )
																		else:
																								items.append((new_key, v) )
												return dict(snake_case )

						__UpperCamelCase       =			argparse.Namespace()
						with open(snake_case ,      '''r''' ) as yaml_file:
												try:
																		__UpperCamelCase       =			yaml.load(snake_case ,      Loader=yaml.FullLoader )

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




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   Optional[Any] )      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			MobileViTVaConfig()

						__UpperCamelCase       =			False

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

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

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

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

						return config




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   int ,      snake_case							:   Any )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			dct.pop(snake_case )
						__UpperCamelCase       =			val




def 	A_ (							snake_case							:   int ,      snake_case							:   List[Any]=False )      ->    Optional[Any]:



						'''simple docstring'''

						if base_model:
												__UpperCamelCase       =			''''''
						else:
												__UpperCamelCase       =			'''mobilevitv2.'''

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

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

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

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

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

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

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

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




def 	A_ (							snake_case							:   List[str] )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						for k in state_dict.keys():
												if k.startswith('''seg_head.aux_head.''' ):
																		keys_to_ignore.append(snake_case )
						for k in keys_to_ignore:
												state_dict.pop(snake_case ,      snake_case )




def 	A_ (							)      ->    str:



						'''simple docstring'''

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




@torch.no_grad()
def 	A_ (							snake_case							:   Dict ,      snake_case							:   List[str] ,      snake_case							:   Optional[Any] ,      snake_case							:   Optional[int] )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			get_mobilevitva_config(snake_case ,      snake_case )

						# load original state_dict
						__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )

						# load huggingface model
						if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
												__UpperCamelCase       =			MobileViTVaForSemanticSegmentation(snake_case ).eval()
												__UpperCamelCase       =			False
						else:
												__UpperCamelCase       =			MobileViTVaForImageClassification(snake_case ).eval()
												__UpperCamelCase       =			False

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

						# load modified state_dict
						model.load_state_dict(snake_case )

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

						# verify classification model
						if task_name.startswith('''imagenet''' ):
												__UpperCamelCase       =			outputs.logits
												__UpperCamelCase       =			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
																		__UpperCamelCase       =			torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01] )
																		assert torch.allclose(logits[0, :3] ,      snake_case ,      atol=1e-4 )

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


if __name__ == "__main__":
				lowercase__							:							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 . "
				        "\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__							:							Tuple									=			parser.parse_args()
				convert_mobilevitva_checkpoint(
				    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
				)




