




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_		:					str          =  logging.get_logger(__name__)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				print("""Loading config file...""" )

				def flatten_yaml_as_dict(lowerCamelCase						,  lowerCamelCase=""						,  lowerCamelCase="." ):
								__magic_name__						:						int										=[]
								for k, v in d.items():
												__magic_name__						:						Dict										=parent_key + sep + k if parent_key else k
												if isinstance(lowerCamelCase						,  collections.abc.MutableMapping ):
																items.extend(flatten_yaml_as_dict(lowerCamelCase						,  lowerCamelCase						,  sep=lowerCamelCase ).items() )
												else:
																items.append((new_key, v) )
								return dict(lowerCamelCase )

				__magic_name__						:						List[Any]										=argparse.Namespace()
				with open(lowerCamelCase						,  """r""" ) as yaml_file:
								try:
												__magic_name__						:						List[Any]										=yaml.load(lowerCamelCase						,  Loader=yaml.FullLoader )

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=MobileViTVaConfig()

				__magic_name__						:						Any										=False

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

				# orig_config
				__magic_name__						:						int										=load_orig_config_file(lowerCamelCase )
				assert getattr(lowerCamelCase						,  """model.classification.name"""						,  -1 ) == "mobilevit_v2", "Invalid model"
				__magic_name__						:						List[str]										=getattr(lowerCamelCase						,  """model.classification.mitv2.width_multiplier"""						,  1.0 )
				assert (
				    getattr(lowerCamelCase						,  """model.classification.mitv2.attn_norm_layer"""						,  -1 ) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				__magic_name__						:						Dict										=getattr(lowerCamelCase						,  """model.classification.activation.name"""						,  """swish""" )
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

				return config




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=dct.pop(lowerCamelCase )
				__magic_name__						:						Optional[int]										=val




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				if base_model:
								__magic_name__						:						Tuple										=""""""
				else:
								__magic_name__						:						int										="""mobilevitv2."""

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

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

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

								for i in [3, 4, 5]:
												if F"layer_{i}.0." in k:
																__magic_name__						:						Optional[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:
																__magic_name__						:						List[str]										=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:
																__magic_name__						:						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:
																__magic_name__						:						int										=[0, 1]
												elif i == 4:
																__magic_name__						:						Tuple										=[0, 1, 2, 3]
												elif i == 5:
																__magic_name__						:						str										=[0, 1, 2]

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

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

								if "seg_head." in k:
												__magic_name__						:						Any										=k_new.replace("""seg_head."""						,  """segmentation_head.""" )
								if ".aspp_layer." in k:
												__magic_name__						:						List[Any]										=k_new.replace(""".aspp_layer."""						,  """.""" )
								if ".aspp_pool." in k:
												__magic_name__						:						str										=k_new.replace(""".aspp_pool."""						,  """.""" )

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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Tuple										=[]
				for k in state_dict.keys():
								if k.startswith("""seg_head.aux_head.""" ):
												keys_to_ignore.append(lowerCamelCase )
				for k in keys_to_ignore:
								state_dict.pop(lowerCamelCase						,  lowerCamelCase )




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




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[int]										=get_mobilevitva_config(lowerCamelCase						,  lowerCamelCase )

				# load original state_dict
				__magic_name__						:						Union[str, Any]										=torch.load(lowerCamelCase						,  map_location="""cpu""" )

				# load huggingface model
				if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
								__magic_name__						:						Union[str, Any]										=MobileViTVaForSemanticSegmentation(lowerCamelCase ).eval()
								__magic_name__						:						Any										=False
				else:
								__magic_name__						:						Tuple										=MobileViTVaForImageClassification(lowerCamelCase ).eval()
								__magic_name__						:						Dict										=False

				# remove and rename some keys of load the original model
				__magic_name__						:						Tuple										=checkpoint
				remove_unused_keys(lowerCamelCase )
				__magic_name__						:						List[str]										=create_rename_keys(lowerCamelCase						,  base_model=lowerCamelCase )
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

				# load modified state_dict
				model.load_state_dict(lowerCamelCase )

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__magic_name__						:						Optional[int]										=MobileViTImageProcessor(crop_size=config.image_size						,  size=config.image_size + 32 )
				__magic_name__						:						Optional[int]										=image_processor(images=prepare_img()						,  return_tensors="""pt""" )
				__magic_name__						:						Tuple										=model(**lowerCamelCase )

				# verify classification model
				if task_name.startswith("""imagenet""" ):
								__magic_name__						:						int										=outputs.logits
								__magic_name__						:						str										=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
												__magic_name__						:						str										=torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
												assert torch.allclose(logits[0, :3]						,  lowerCamelCase						,  atol=1E-4 )

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


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 . "
							        "\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."
							)

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










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




