


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()
a       	=  logging.get_logger(__name__)


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					print("""Loading config file..."""	)

					def flatten_yaml_as_dict(UpperCAmelCase__    ,				UpperCAmelCase__=""    ,				UpperCAmelCase__="."	):
										lowercase_					=      []
										for k, v in d.items():
															lowercase_					=      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	)

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

															lowercase_					=      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       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__	):
					lowercase_					=      MobileViTVaConfig()

					lowercase_					=      False

					# dataset
					if task_name.startswith("""imagenet1k_"""	):
										lowercase_					=      1_0_0_0
										if int(task_name.strip().split("""_"""	)[-1]	) == 3_8_4:
															lowercase_					=      3_8_4
										else:
															lowercase_					=      2_5_6
										lowercase_					=      "imagenet-1k-id2label.json"
					elif task_name.startswith("""imagenet21k_to_1k_"""	):
										lowercase_					=      2_1_0_0_0
										if int(task_name.strip().split("""_"""	)[-1]	) == 3_8_4:
															lowercase_					=      3_8_4
										else:
															lowercase_					=      2_5_6
										lowercase_					=      "imagenet-22k-id2label.json"
					elif task_name.startswith("""ade20k_"""	):
										lowercase_					=      1_5_1
										lowercase_					=      5_1_2
										lowercase_					=      "ade20k-id2label.json"
										lowercase_					=      True
					elif task_name.startswith("""voc_"""	):
										lowercase_					=      2_1
										lowercase_					=      5_1_2
										lowercase_					=      "pascal-voc-id2label.json"
										lowercase_					=      True

					# orig_config
					lowercase_					=      load_orig_config_file(__A	)
					assert getattr(__A    ,				"""model.classification.name"""    ,				-1	) == "mobilevit_v2", "Invalid model"
					lowercase_					=      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"
					lowercase_					=      getattr(__A    ,				"""model.classification.activation.name"""    ,				"""swish"""	)
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										lowercase_					=      getattr(__A    ,				"""model.segmentation.output_stride"""    ,				1_6	)
										if "_deeplabv3" in task_name:
															lowercase_					=      getattr(__A    ,				"""model.segmentation.deeplabv3.aspp_rates"""    ,				[1_2, 2_4, 3_6]	)
															lowercase_					=      getattr(__A    ,				"""model.segmentation.deeplabv3.aspp_out_channels"""    ,				5_1_2	)
															lowercase_					=      getattr(__A    ,				"""model.segmentation.deeplabv3.aspp_dropout"""    ,				0.1	)

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

					return config


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__	):
					lowercase_					=      dct.pop(__A	)
					lowercase_					=      val


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__=False	):
					if base_model:
										lowercase_					=      ""
					else:
										lowercase_					=      "mobilevitv2."

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

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

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

										for i in [3, 4, 5]:
															if F'''layer_{i}.0.''' in k:
																				lowercase_					=      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:
																				lowercase_					=      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:
																				lowercase_					=      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:
																				lowercase_					=      [0, 1]
															elif i == 4:
																				lowercase_					=      [0, 1, 2, 3]
															elif i == 5:
																				lowercase_					=      [0, 1, 2]

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

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

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

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


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


@torch.no_grad()
def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__	):
					lowercase_					=      get_mobilevitva_config(__A    ,				__A	)

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

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

					# remove and rename some keys of load the original model
					lowercase_					=      checkpoint
					remove_unused_keys(__A	)
					lowercase_					=      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
					lowercase_					=      MobileViTImageProcessor(crop_size=config.image_size    ,				size=config.image_size + 3_2	)
					lowercase_					=      image_processor(images=prepare_img()    ,				return_tensors="""pt"""	)
					lowercase_					=      model(**__A	)

					# verify classification model
					if task_name.startswith("""imagenet"""	):
										lowercase_					=      outputs.logits
										lowercase_					=      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
															lowercase_					=      torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-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__":
							a       	=  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.'
							)

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










UpperCAmelCase__      			=						'''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase__      			=						'''TRWAGMYFPDXBNJZSQVHLCKE'''







def     a_   (__A      )				->						bool:



					"""simple docstring"""

					if not isinstance(__A				,       __A      ):
										__a					:						Any      =					f'Expected string as input, found {type(__A      ).__name__}'
										raise TypeError(__A      )

					__a					:						int      =					spanish_id.replace("-"				,       ""      ).upper()
					if len(__A      ) != 9:
										raise ValueError(__A      )

					try:
										__a					:						Tuple      =					int(spanish_id_clean[0:8]      )
										__a					:						Optional[int]      =					spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__A      ) from ex

					if letter.isdigit():
										raise ValueError(__A      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





