


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:       List[Any]     			=      logging.get_logger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														a__             =     [1_4_4, 1_9_2, 2_4_0]
														a__             =     [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
							elif "mobilevit_xs" in mobilevit_name:
														a__             =     [9_6, 1_2_0, 1_4_4]
														a__             =     [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
							elif "mobilevit_xxs" in mobilevit_name:
														a__             =     [6_4, 8_0, 9_6]
														a__             =     [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
														a__             =     0.05
														a__             =     2.0

							if mobilevit_name.startswith('deeplabv3_'   ):
														a__             =     5_1_2
														a__             =     1_6
														a__             =     2_1
														a__             =     'pascal-voc-id2label.json'
							else:
														a__             =     1_0_0_0
														a__             =     'imagenet-1k-id2label.json'

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

							return config






def 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				Any=False   ):
							for i in range(1					,    6   ):
														if F'layer_{i}.' in name:
																					a__             =     name.replace(F'layer_{i}.'					,    F'encoder.layer.{i - 1}.'   )

							if "conv_1." in name:
														a__             =     name.replace('conv_1.'					,    'conv_stem.'   )
							if ".block." in name:
														a__             =     name.replace('.block.'					,    '.'   )

							if "exp_1x1" in name:
														a__             =     name.replace('exp_1x1'					,    'expand_1x1'   )
							if "red_1x1" in name:
														a__             =     name.replace('red_1x1'					,    'reduce_1x1'   )
							if ".local_rep.conv_3x3." in name:
														a__             =     name.replace('.local_rep.conv_3x3.'					,    '.conv_kxk.'   )
							if ".local_rep.conv_1x1." in name:
														a__             =     name.replace('.local_rep.conv_1x1.'					,    '.conv_1x1.'   )
							if ".norm." in name:
														a__             =     name.replace('.norm.'					,    '.normalization.'   )
							if ".conv." in name:
														a__             =     name.replace('.conv.'					,    '.convolution.'   )
							if ".conv_proj." in name:
														a__             =     name.replace('.conv_proj.'					,    '.conv_projection.'   )

							for i in range(0					,    2   ):
														for j in range(0					,    4   ):
																					if F'.{i}.{j}.' in name:
																												a__             =     name.replace(F'.{i}.{j}.'					,    F'.{i}.layer.{j}.'   )

							for i in range(2					,    6   ):
														for j in range(0					,    4   ):
																					if F'.{i}.{j}.' in name:
																												a__             =     name.replace(F'.{i}.{j}.'					,    F'.{i}.'   )
																												if "expand_1x1" in name:
																																			a__             =     name.replace('expand_1x1'					,    'downsampling_layer.expand_1x1'   )
																												if "conv_3x3" in name:
																																			a__             =     name.replace('conv_3x3'					,    'downsampling_layer.conv_3x3'   )
																												if "reduce_1x1" in name:
																																			a__             =     name.replace('reduce_1x1'					,    'downsampling_layer.reduce_1x1'   )

							for i in range(2					,    5   ):
														if F'.global_rep.{i}.weight' in name:
																					a__             =     name.replace(F'.global_rep.{i}.weight'					,    '.layernorm.weight'   )
														if F'.global_rep.{i}.bias' in name:
																					a__             =     name.replace(F'.global_rep.{i}.bias'					,    '.layernorm.bias'   )

							if ".global_rep." in name:
														a__             =     name.replace('.global_rep.'					,    '.transformer.'   )
							if ".pre_norm_mha.0." in name:
														a__             =     name.replace('.pre_norm_mha.0.'					,    '.layernorm_before.'   )
							if ".pre_norm_mha.1.out_proj." in name:
														a__             =     name.replace('.pre_norm_mha.1.out_proj.'					,    '.attention.output.dense.'   )
							if ".pre_norm_ffn.0." in name:
														a__             =     name.replace('.pre_norm_ffn.0.'					,    '.layernorm_after.'   )
							if ".pre_norm_ffn.1." in name:
														a__             =     name.replace('.pre_norm_ffn.1.'					,    '.intermediate.dense.'   )
							if ".pre_norm_ffn.4." in name:
														a__             =     name.replace('.pre_norm_ffn.4.'					,    '.output.dense.'   )
							if ".transformer." in name:
														a__             =     name.replace('.transformer.'					,    '.transformer.layer.'   )

							if ".aspp_layer." in name:
														a__             =     name.replace('.aspp_layer.'					,    '.'   )
							if ".aspp_pool." in name:
														a__             =     name.replace('.aspp_pool.'					,    '.'   )
							if "seg_head." in name:
														a__             =     name.replace('seg_head.'					,    'segmentation_head.'   )
							if "segmentation_head.classifier.classifier." in name:
														a__             =     name.replace('segmentation_head.classifier.classifier.'					,    'segmentation_head.classifier.'   )

							if "classifier.fc." in name:
														a__             =     name.replace('classifier.fc.'					,    'classifier.'   )
							elif (not base_model) and ("segmentation_head." not in name):
														a__             =     'mobilevit.' + name

							return name






def 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				List[Any]=False   ):
							if base_model:
														a__             =     ''
							else:
														a__             =     'mobilevit.'

							for key in orig_state_dict.copy().keys():
														a__             =     orig_state_dict.pop(__lowerCAmelCase   )

														if key[:8] == "encoder.":
																					a__             =     key[8:]

														if "qkv" in key:
																					a__             =     key.split('.'   )
																					a__             =     int(key_split[0][6:]   ) - 1
																					a__             =     int(key_split[3]   )
																					a__             =     model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'   )
																					a__             =     layer.transformer.layer[transformer_num].attention.attention.all_head_size
																					a__             =     (
																					    F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
																					)
																					if "weight" in key:
																												a__             =     val[:dim, :]
																												a__             =     val[dim : dim * 2, :]
																												a__             =     val[-dim:, :]
																					else:
																												a__             =     val[:dim]
																												a__             =     val[dim : dim * 2]
																												a__             =     val[-dim:]
														else:
																					a__             =     val

							return orig_state_dict






def 							__lowercase   ( ):
							a__             =     'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__             =     Image.open(requests.get(__lowerCAmelCase					,    stream=__lowerCAmelCase   ).raw   )
							return im






@torch.no_grad()
def 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				int=False   ):
							a__             =     get_mobilevit_config(__lowerCAmelCase   )

							# load original state_dict
							a__             =     torch.load(__lowerCAmelCase					,    map_location='cpu'   )

							# load 🤗 model
							if mobilevit_name.startswith('deeplabv3_'   ):
														a__             =     MobileViTForSemanticSegmentation(__lowerCAmelCase   ).eval()
							else:
														a__             =     MobileViTForImageClassification(__lowerCAmelCase   ).eval()

							a__             =     convert_state_dict(__lowerCAmelCase					,    __lowerCAmelCase   )
							model.load_state_dict(__lowerCAmelCase   )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							a__             =     MobileViTImageProcessor(crop_size=config.image_size					,    size=config.image_size + 3_2   )
							a__             =     image_processor(images=prepare_img()					,    return_tensors='pt'   )
							a__             =     model(**__lowerCAmelCase   )
							a__             =     outputs.logits

							if mobilevit_name.startswith('deeplabv3_'   ):
														assert logits.shape == (1, 2_1, 3_2, 3_2)

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					a__             =     torch.tensor(
																					    [
																					        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																					        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																					        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																					    ]   )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					a__             =     torch.tensor(
																					    [
																					        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																					        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																					        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																					    ]   )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					a__             =     torch.tensor(
																					    [
																					        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																					        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																					        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																					    ]   )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'   )

														assert torch.allclose(logits[0, :3, :3, :3]					,    __lowerCAmelCase					,    atol=1E-4   )
							else:
														assert logits.shape == (1, 1_0_0_0)

														if mobilevit_name == "mobilevit_s":
																					a__             =     torch.tensor([-0.9_866, 0.2_392, -1.1_241]   )
														elif mobilevit_name == "mobilevit_xs":
																					a__             =     torch.tensor([-2.4_761, -0.9_399, -1.9_587]   )
														elif mobilevit_name == "mobilevit_xxs":
																					a__             =     torch.tensor([-1.9_364, -1.2_327, -0.4_653]   )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'   )

														assert torch.allclose(logits[0, :3]					,    __lowerCAmelCase					,    atol=1E-4   )

							Path(__lowerCAmelCase   ).mkdir(exist_ok=__lowerCAmelCase   )
							print(F'Saving model {mobilevit_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 push_to_hub:
														a__             =     {
														    'mobilevit_s': 'mobilevit-small',
														    'mobilevit_xs': 'mobilevit-x-small',
														    'mobilevit_xxs': 'mobilevit-xx-small',
														    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
														    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
														    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
														}

														print('Pushing to the hub...'   )
														a__             =     model_mapping[mobilevit_name]
														image_processor.push_to_hub(__lowerCAmelCase					,    organization='apple'   )
														model.push_to_hub(__lowerCAmelCase					,    organization='apple'   )


if __name__ == "__main__":
						snake_case							:       Tuple     			=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--mobilevit_name''',
						    default='''mobilevit_s''',
						    type=str,
						    help=(
						        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
						        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						snake_case							:       Tuple     			=      parser.parse_args()
						convert_movilevit_checkpoint(
						    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												if "height" not in size or "width" not in size:
																			raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'      )
												return center_crop(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												a__             =     image_mean if image_mean is not None else self.image_mean
												a__             =     image_std if image_std is not None else self.image_std

												a__             =     size if size is not None else self.size
												a__             =     get_size_dict(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			raise ValueError(
																			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																			    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

												if do_resize and size is None:
																			raise ValueError('Size must be specified if do_resize is True.'      )

												if do_center_crop and crop_size is None:
																			raise ValueError('Crop size must be specified if do_center_crop is True.'      )

												if do_rescale and rescale_factor is None:
																			raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

												# All transformations expect numpy arrays.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

												if do_resize:
																			a__             =     [self.resize(image=__snake_case			,size=__snake_case			,resample=__snake_case      ) for image in images]

												if do_center_crop:
																			a__             =     [self.center_crop(image=__snake_case			,size=__snake_case      ) for image in images]

												if do_rescale:
																			a__             =     [self.rescale(image=__snake_case			,scale=__snake_case      ) for image in images]

												if do_normalize:
																			a__             =     [self.normalize(image=__snake_case			,mean=__snake_case			,std=__snake_case      ) for image in images]

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




