
"""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()
__A   :							List[Any]										=   logging.get_logger(__name__)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



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

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

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

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''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					=    2_1000
								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(_SCREAMING_SNAKE_CASE				)
				assert getattr(_SCREAMING_SNAKE_CASE					,     '''model.classification.name'''					,     -1				) == "mobilevit_v2", "Invalid model"
				_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     '''model.classification.mitv2.width_multiplier'''					,     1.0				)
				assert (
				    getattr(_SCREAMING_SNAKE_CASE					,     '''model.classification.mitv2.attn_norm_layer'''					,     -1				) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				_UpperCAmelCase					=    getattr(_SCREAMING_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(_SCREAMING_SNAKE_CASE					,     '''model.segmentation.output_stride'''					,     16				)
								if "_deeplabv3" in task_name:
												_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     '''model.segmentation.deeplabv3.aspp_rates'''					,     [12, 24, 36]				)
												_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     '''model.segmentation.deeplabv3.aspp_out_channels'''					,     512				)
												_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     '''model.segmentation.deeplabv3.aspp_dropout'''					,     0.1				)

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

				return config







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    dct.pop(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    val







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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







def  lowercase	(      ):






				'''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(_SCREAMING_SNAKE_CASE					,     stream=_SCREAMING_SNAKE_CASE				).raw				)
				return im







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_mobilevitva_config(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

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

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

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

				# load modified state_dict
				model.load_state_dict(_SCREAMING_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(**_SCREAMING_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]					,     _SCREAMING_SNAKE_CASE					,     atol=1E-4				)

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


if __name__ == "__main__":
					__A   :							List[str]										=   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   :							Any										=   parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)








"""simple docstring"""


class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    list[int]   )->None:
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    [0] * len_array

							if len_array > 0:
											_UpperCAmelCase					=    array[0]

							for i in range(1				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    self.prefix_sum[i - 1] + array[i]



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int   )->int:
							if start == 0:
											return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    int   )->bool:
							_UpperCAmelCase					=    {0}
							for sum_item in self.prefix_sum:
											if sum_item - target_sum in sums:
															return True

											sums.add(__UpperCamelCase   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()



