

'''simple docstring'''





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()
A_	= logging.get_logger(__name__)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			int:
					lowerCamelCase_  				=      MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										lowerCamelCase_  				=      [1_44, 1_92, 2_40]
										lowerCamelCase_  				=      [16, 32, 64, 96, 1_28, 1_60, 6_40]
					elif "mobilevit_xs" in mobilevit_name:
										lowerCamelCase_  				=      [96, 1_20, 1_44]
										lowerCamelCase_  				=      [16, 32, 48, 64, 80, 96, 3_84]
					elif "mobilevit_xxs" in mobilevit_name:
										lowerCamelCase_  				=      [64, 80, 96]
										lowerCamelCase_  				=      [16, 16, 24, 48, 64, 80, 3_20]
										lowerCamelCase_  				=      0.05
										lowerCamelCase_  				=      2.0

					if mobilevit_name.startswith('deeplabv3_'	):
										lowerCamelCase_  				=      5_12
										lowerCamelCase_  				=      16
										lowerCamelCase_  				=      21
										lowerCamelCase_  				=      'pascal-voc-id2label.json'
					else:
										lowerCamelCase_  				=      10_00
										lowerCamelCase_  				=      'imagenet-1k-id2label.json'

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

					return config







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase=False	)       ->			Optional[Any]:
					for i in range(1    ,6	):
										if f'''layer_{i}.''' in name:
															lowerCamelCase_  				=      name.replace(f'''layer_{i}.'''    ,f'''encoder.layer.{i - 1}.'''	)

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

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

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

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

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

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

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

					return name







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=False	)       ->			List[str]:
					if base_model:
										lowerCamelCase_  				=      ''
					else:
										lowerCamelCase_  				=      'mobilevit.'

					for key in orig_state_dict.copy().keys():
										lowerCamelCase_  				=      orig_state_dict.pop(__UpperCamelCase	)

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

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

					return orig_state_dict







def   _UpperCamelCase	(		)       ->			List[Any]:
					lowerCamelCase_  				=      'http://images.cocodataset.org/val2017/000000039769.jpg'
					lowerCamelCase_  				=      Image.open(requests.get(__UpperCamelCase    ,stream=__UpperCamelCase	).raw	)
					return im







@torch.no_grad()
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=False	)       ->			Any:
					lowerCamelCase_  				=      get_mobilevit_config(__UpperCamelCase	)

					# load original state_dict
					lowerCamelCase_  				=      torch.load(__UpperCamelCase    ,map_location='cpu'	)

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

					lowerCamelCase_  				=      convert_state_dict(__UpperCamelCase    ,__UpperCamelCase	)
					model.load_state_dict(__UpperCamelCase	)

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

					if mobilevit_name.startswith('deeplabv3_'	):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															lowerCamelCase_  				=      torch.tensor(
															    [
															        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
															        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
															        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
															    ]	)
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															lowerCamelCase_  				=      torch.tensor(
															    [
															        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
															        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
															        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
															    ]	)
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															lowerCamelCase_  				=      torch.tensor(
															    [
															        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
															        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
															        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
															    ]	)
										else:
															raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

										assert torch.allclose(logits[0, :3, :3, :3]    ,__UpperCamelCase    ,atol=1e-4	)
					else:
										assert logits.shape == (1, 10_00)

										if mobilevit_name == "mobilevit_s":
															lowerCamelCase_  				=      torch.tensor([-0.9866, 0.2392, -1.1241]	)
										elif mobilevit_name == "mobilevit_xs":
															lowerCamelCase_  				=      torch.tensor([-2.4761, -0.9399, -1.9587]	)
										elif mobilevit_name == "mobilevit_xxs":
															lowerCamelCase_  				=      torch.tensor([-1.9364, -1.2327, -0.4653]	)
										else:
															raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

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

					Path(__UpperCamelCase	).mkdir(exist_ok=__UpperCamelCase	)
					print(f'''Saving model {mobilevit_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 push_to_hub:
										lowerCamelCase_  				=      {
										    '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...'	)
										lowerCamelCase_  				=      model_mapping[mobilevit_name]
										image_processor.push_to_hub(__UpperCamelCase    ,organization='apple'	)
										model.push_to_hub(__UpperCamelCase    ,organization='apple'	)


if __name__ == "__main__":
				A_	= 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."
				)

				A_	= parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)








'''simple docstring'''
import os
from pathlib import Path






def A__       (	):
						from torch.utils.cpp_extension import load

						_UpperCamelCase   :						Optional[Any]												=  Path(UpperCAmelCase_				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
						_UpperCamelCase   :						Tuple												=  [
						    root / filename
						    for filename in [
						        'vision.cpp',
						        os.path.join('cpu'    ,    'ms_deform_attn_cpu.cpp'				),
						        os.path.join('cuda'    ,    'ms_deform_attn_cuda.cu'				),
						    ]
						]

						load(
						    'MultiScaleDeformableAttention'    ,    UpperCAmelCase_    ,    with_cuda=UpperCAmelCase_    ,    extra_include_paths=[str(UpperCAmelCase_				)]    ,    extra_cflags=['-DWITH_CUDA=1']    ,    extra_cuda_cflags=[
						        '-DCUDA_HAS_FP16=1',
						        '-D__CUDA_NO_HALF_OPERATORS__',
						        '-D__CUDA_NO_HALF_CONVERSIONS__',
						        '-D__CUDA_NO_HALF2_OPERATORS__',
						    ]    ,    )

						import MultiScaleDeformableAttention as MSDA

						return MSDA


