

'''simple docstring'''
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()
lowerCamelCase			:				Dict 			=					logging.get_logger(__name__)


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]    )				->      Union[str, Any]:
					"""simple docstring"""




					print('Loading config file...'    )

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

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

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					str    )				->      Optional[int]:
					"""simple docstring"""




					_SCREAMING_SNAKE_CASE							   =MobileViTVaConfig()

					_SCREAMING_SNAKE_CASE							   =False

					# dataset
					if task_name.startswith('imagenet1k_'    ):
										_SCREAMING_SNAKE_CASE							   =10_00
										if int(task_name.strip().split('_'    )[-1]    ) == 3_84:
															_SCREAMING_SNAKE_CASE							   =3_84
										else:
															_SCREAMING_SNAKE_CASE							   =2_56
										_SCREAMING_SNAKE_CASE							   ='imagenet-1k-id2label.json'
					elif task_name.startswith('imagenet21k_to_1k_'    ):
										_SCREAMING_SNAKE_CASE							   =2_10_00
										if int(task_name.strip().split('_'    )[-1]    ) == 3_84:
															_SCREAMING_SNAKE_CASE							   =3_84
										else:
															_SCREAMING_SNAKE_CASE							   =2_56
										_SCREAMING_SNAKE_CASE							   ='imagenet-22k-id2label.json'
					elif task_name.startswith('ade20k_'    ):
										_SCREAMING_SNAKE_CASE							   =1_51
										_SCREAMING_SNAKE_CASE							   =5_12
										_SCREAMING_SNAKE_CASE							   ='ade20k-id2label.json'
										_SCREAMING_SNAKE_CASE							   =True
					elif task_name.startswith('voc_'    ):
										_SCREAMING_SNAKE_CASE							   =21
										_SCREAMING_SNAKE_CASE							   =5_12
										_SCREAMING_SNAKE_CASE							   ='pascal-voc-id2label.json'
										_SCREAMING_SNAKE_CASE							   =True

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

					if is_segmentation_model:
										_SCREAMING_SNAKE_CASE							   =getattr(_UpperCamelCase							,			'model.segmentation.output_stride'							,			16    )
										if "_deeplabv3" in task_name:
															_SCREAMING_SNAKE_CASE							   =getattr(_UpperCamelCase							,			'model.segmentation.deeplabv3.aspp_rates'							,			[12, 24, 36]    )
															_SCREAMING_SNAKE_CASE							   =getattr(_UpperCamelCase							,			'model.segmentation.deeplabv3.aspp_out_channels'							,			5_12    )
															_SCREAMING_SNAKE_CASE							   =getattr(_UpperCamelCase							,			'model.segmentation.deeplabv3.aspp_dropout'							,			0.1    )

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

					return config


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					str    )				->      int:
					"""simple docstring"""




					_SCREAMING_SNAKE_CASE							   =dct.pop(_UpperCamelCase    )
					_SCREAMING_SNAKE_CASE							   =val


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					int=False    )				->      Dict:
					"""simple docstring"""




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

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

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

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

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

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

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

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

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      Optional[int]:
					"""simple docstring"""




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


def 	_lowerCAmelCase (						)				->      str:
					"""simple docstring"""




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


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					Any    )				->      Dict:
					"""simple docstring"""




					_SCREAMING_SNAKE_CASE							   =get_mobilevitva_config(_UpperCamelCase							,			_UpperCamelCase    )

					# load original state_dict
					_SCREAMING_SNAKE_CASE							   =torch.load(_UpperCamelCase							,			map_location='cpu'    )

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

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

					# load modified state_dict
					model.load_state_dict(_UpperCamelCase    )

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

					# verify classification model
					if task_name.startswith('imagenet'    ):
										_SCREAMING_SNAKE_CASE							   =outputs.logits
										_SCREAMING_SNAKE_CASE							   =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
															_SCREAMING_SNAKE_CASE							   =torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]    )
															assert torch.allclose(logits[0, :3]							,			_UpperCamelCase							,			atol=1E-4    )

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


if __name__ == "__main__":
					lowerCamelCase			:				Optional[int] 			=					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."
					)

					lowerCamelCase			:				Tuple 			=					parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)



'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching


def 	_lowerCAmelCase (						)				->      int:
     """simple docstring"""




     import os as original_os
     from os import path as original_path
     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join

     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_mock__'
     with patch_submodule(_test_patching							,			'os.path.join'							,			_UpperCamelCase    ):
          # Every way to access os.path.join must be patched, and the rest must stay untouched

          # check os.path.join
          assert isinstance(_test_patching.os							,			_PatchedModuleObj    )
          assert isinstance(_test_patching.os.path							,			_PatchedModuleObj    )
          assert _test_patching.os.path.join is mock

          # check path.join
          assert isinstance(_test_patching.path							,			_PatchedModuleObj    )
          assert _test_patching.path.join is mock

          # check join
          assert _test_patching.join is mock

          # check that the other attributes are untouched
          assert _test_patching.os.rename is original_rename
          assert _test_patching.path.dirname is original_dirname
          assert _test_patching.os.path.dirname is original_dirname

          # Even renamed modules or objects must be patched

          # check renamed_os.path.join
          assert isinstance(_test_patching.renamed_os							,			_PatchedModuleObj    )
          assert isinstance(_test_patching.renamed_os.path							,			_PatchedModuleObj    )
          assert _test_patching.renamed_os.path.join is mock

          # check renamed_path.join
          assert isinstance(_test_patching.renamed_path							,			_PatchedModuleObj    )
          assert _test_patching.renamed_path.join is mock

          # check renamed_join
          assert _test_patching.renamed_join is mock

          # check that the other attributes are untouched
          assert _test_patching.renamed_os.rename is original_rename
          assert _test_patching.renamed_path.dirname is original_dirname
          assert _test_patching.renamed_os.path.dirname is original_dirname

     # check that everthing is back to normal when the patch is over

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join


def 	_lowerCAmelCase (						)				->      Optional[Any]:
     """simple docstring"""




     assert _test_patching.open is open

     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_builtin_mock__'
     # _test_patching has "open" in its globals
     assert _test_patching.open is open
     with patch_submodule(_test_patching							,			'open'							,			_UpperCamelCase    ):
          assert _test_patching.open is mock

     # check that everthing is back to normal when the patch is over

     assert _test_patching.open is open


def 	_lowerCAmelCase (						)				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_missing_mock__'
     with patch_submodule(_test_patching							,			'pandas.read_csv'							,			_UpperCamelCase    ):
          pass


def 	_lowerCAmelCase (						)				->      Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_missing_builtin_mock__'
     # _test_patching doesn't have "len" in its globals
     assert getattr(_test_patching							,			'len'							,			_UpperCamelCase    ) is None
     with patch_submodule(_test_patching							,			'len'							,			_UpperCamelCase    ):
          assert _test_patching.len is mock
     assert _test_patching.len is len


def 	_lowerCAmelCase (						)				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_start_and_stop_mock__'
     _SCREAMING_SNAKE_CASE							   =patch_submodule(_test_patching							,			'open'							,			_UpperCamelCase    )
     assert _test_patching.open is open
     patch.start()
     assert _test_patching.open is mock
     patch.stop()
     assert _test_patching.open is open


def 	_lowerCAmelCase (						)				->      Optional[Any]:
     """simple docstring"""




     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_successive_join__'
     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_successive_dirname__'
     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_successive_rename__'
     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename

     with patch_submodule(_test_patching							,			'os.path.join'							,			_UpperCamelCase    ):
          with patch_submodule(_test_patching							,			'os.rename'							,			_UpperCamelCase    ):
               with patch_submodule(_test_patching							,			'os.path.dirname'							,			_UpperCamelCase    ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

    # try another order
     with patch_submodule(_test_patching							,			'os.rename'							,			_UpperCamelCase    ):
          with patch_submodule(_test_patching							,			'os.path.join'							,			_UpperCamelCase    ):
               with patch_submodule(_test_patching							,			'os.path.dirname'							,			_UpperCamelCase    ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename







def 	_lowerCAmelCase (						)				->      Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='__test_patch_submodule_doesnt_exist_mock__'
     with patch_submodule(_test_patching							,			'__module_that_doesn_exist__.__attribute_that_doesn_exist__'							,			_UpperCamelCase    ):
          pass
     with patch_submodule(_test_patching							,			'os.__attribute_that_doesn_exist__'							,			_UpperCamelCase    ):
          pass
