



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()
_UpperCAmelCase       :	int            =logging.get_logger(__name__)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[str]				=  MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								lowerCAmelCase__  :       str				=  [144, 192, 240]
								lowerCAmelCase__  :       int				=  [16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								lowerCAmelCase__  :       Any				=  [96, 120, 144]
								lowerCAmelCase__  :       Tuple				=  [16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								lowerCAmelCase__  :       List[str]				=  [64, 80, 96]
								lowerCAmelCase__  :       Optional[Any]				=  [16, 16, 24, 48, 64, 80, 320]
								lowerCAmelCase__  :       str				=  0.05
								lowerCAmelCase__  :       Any				=  2.0

				if mobilevit_name.startswith('''deeplabv3_'''):
								lowerCAmelCase__  :       Dict				=  512
								lowerCAmelCase__  :       List[Any]				=  16
								lowerCAmelCase__  :       Optional[int]				=  21
								lowerCAmelCase__  :       Optional[Any]				=  '''pascal-voc-id2label.json'''
				else:
								lowerCAmelCase__  :       Optional[Any]				=  1000
								lowerCAmelCase__  :       Optional[int]				=  '''imagenet-1k-id2label.json'''

				lowerCAmelCase__  :       Any				=  '''huggingface/label-files'''
				lowerCAmelCase__  :       List[str]				=  json.load(open(hf_hub_download(lowerCamelCase_ ,lowerCamelCase_ ,repo_type='''dataset''') ,'''r'''))
				lowerCAmelCase__  :       Optional[int]				=  {int(lowerCamelCase_): v for k, v in idalabel.items()}
				lowerCAmelCase__  :       Dict				=  idalabel
				lowerCAmelCase__  :       Dict				=  {v: k for k, v in idalabel.items()}

				return config






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Optional[int]=False):





				'''simple docstring'''

				for i in range(1 ,6):
								if f"""layer_{i}.""" in name:
												lowerCAmelCase__  :       Any				=  name.replace(f"""layer_{i}.""" ,f"""encoder.layer.{i - 1}.""")

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

				if "exp_1x1" in name:
								lowerCAmelCase__  :       int				=  name.replace('''exp_1x1''' ,'''expand_1x1''')
				if "red_1x1" in name:
								lowerCAmelCase__  :       int				=  name.replace('''red_1x1''' ,'''reduce_1x1''')
				if ".local_rep.conv_3x3." in name:
								lowerCAmelCase__  :       Dict				=  name.replace('''.local_rep.conv_3x3.''' ,'''.conv_kxk.''')
				if ".local_rep.conv_1x1." in name:
								lowerCAmelCase__  :       Dict				=  name.replace('''.local_rep.conv_1x1.''' ,'''.conv_1x1.''')
				if ".norm." in name:
								lowerCAmelCase__  :       Union[str, Any]				=  name.replace('''.norm.''' ,'''.normalization.''')
				if ".conv." in name:
								lowerCAmelCase__  :       List[Any]				=  name.replace('''.conv.''' ,'''.convolution.''')
				if ".conv_proj." in name:
								lowerCAmelCase__  :       Union[str, Any]				=  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__  :       Dict				=  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__  :       Tuple				=  name.replace(f""".{i}.{j}.""" ,f""".{i}.""")
																if "expand_1x1" in name:
																				lowerCAmelCase__  :       Optional[Any]				=  name.replace('''expand_1x1''' ,'''downsampling_layer.expand_1x1''')
																if "conv_3x3" in name:
																				lowerCAmelCase__  :       List[Any]				=  name.replace('''conv_3x3''' ,'''downsampling_layer.conv_3x3''')
																if "reduce_1x1" in name:
																				lowerCAmelCase__  :       Optional[int]				=  name.replace('''reduce_1x1''' ,'''downsampling_layer.reduce_1x1''')

				for i in range(2 ,5):
								if f""".global_rep.{i}.weight""" in name:
												lowerCAmelCase__  :       str				=  name.replace(f""".global_rep.{i}.weight""" ,'''.layernorm.weight''')
								if f""".global_rep.{i}.bias""" in name:
												lowerCAmelCase__  :       Optional[int]				=  name.replace(f""".global_rep.{i}.bias""" ,'''.layernorm.bias''')

				if ".global_rep." in name:
								lowerCAmelCase__  :       Optional[Any]				=  name.replace('''.global_rep.''' ,'''.transformer.''')
				if ".pre_norm_mha.0." in name:
								lowerCAmelCase__  :       List[str]				=  name.replace('''.pre_norm_mha.0.''' ,'''.layernorm_before.''')
				if ".pre_norm_mha.1.out_proj." in name:
								lowerCAmelCase__  :       int				=  name.replace('''.pre_norm_mha.1.out_proj.''' ,'''.attention.output.dense.''')
				if ".pre_norm_ffn.0." in name:
								lowerCAmelCase__  :       List[str]				=  name.replace('''.pre_norm_ffn.0.''' ,'''.layernorm_after.''')
				if ".pre_norm_ffn.1." in name:
								lowerCAmelCase__  :       Union[str, Any]				=  name.replace('''.pre_norm_ffn.1.''' ,'''.intermediate.dense.''')
				if ".pre_norm_ffn.4." in name:
								lowerCAmelCase__  :       Optional[Any]				=  name.replace('''.pre_norm_ffn.4.''' ,'''.output.dense.''')
				if ".transformer." in name:
								lowerCAmelCase__  :       Tuple				=  name.replace('''.transformer.''' ,'''.transformer.layer.''')

				if ".aspp_layer." in name:
								lowerCAmelCase__  :       str				=  name.replace('''.aspp_layer.''' ,'''.''')
				if ".aspp_pool." in name:
								lowerCAmelCase__  :       Tuple				=  name.replace('''.aspp_pool.''' ,'''.''')
				if "seg_head." in name:
								lowerCAmelCase__  :       List[str]				=  name.replace('''seg_head.''' ,'''segmentation_head.''')
				if "segmentation_head.classifier.classifier." in name:
								lowerCAmelCase__  :       List[str]				=  name.replace('''segmentation_head.classifier.classifier.''' ,'''segmentation_head.classifier.''')

				if "classifier.fc." in name:
								lowerCAmelCase__  :       Union[str, Any]				=  name.replace('''classifier.fc.''' ,'''classifier.''')
				elif (not base_model) and ("segmentation_head." not in name):
								lowerCAmelCase__  :       Union[str, Any]				=  '''mobilevit.''' + name

				return name






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any]=False):





				'''simple docstring'''

				if base_model:
								lowerCAmelCase__  :       Any				=  ''''''
				else:
								lowerCAmelCase__  :       Tuple				=  '''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								lowerCAmelCase__  :       Union[str, Any]				=  orig_state_dict.pop(lowerCamelCase_)

								if key[:8] == "encoder.":
												lowerCAmelCase__  :       Dict				=  key[8:]

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

				return orig_state_dict






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowerCAmelCase__  :       Union[str, Any]				=  Image.open(requests.get(lowerCamelCase_ ,stream=lowerCamelCase_).raw)
				return im






@torch.no_grad()
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	int ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any]=False):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  get_mobilevit_config(lowerCamelCase_)

				# load original state_dict
				lowerCAmelCase__  :       Union[str, Any]				=  torch.load(lowerCamelCase_ ,map_location='''cpu''')

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''):
								lowerCAmelCase__  :       int				=  MobileViTForSemanticSegmentation(lowerCamelCase_).eval()
				else:
								lowerCAmelCase__  :       List[Any]				=  MobileViTForImageClassification(lowerCamelCase_).eval()

				lowerCAmelCase__  :       Union[str, Any]				=  convert_state_dict(lowerCamelCase_ ,lowerCamelCase_)
				model.load_state_dict(lowerCamelCase_)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				lowerCAmelCase__  :       Dict				=  MobileViTImageProcessor(crop_size=config.image_size ,size=config.image_size + 32)
				lowerCAmelCase__  :       List[str]				=  image_processor(images=prepare_img() ,return_tensors='''pt''')
				lowerCAmelCase__  :       Union[str, Any]				=  model(**lowerCamelCase_)
				lowerCAmelCase__  :       List[Any]				=  outputs.logits

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

								if mobilevit_name == "deeplabv3_mobilevit_s":
												lowerCAmelCase__  :       Union[str, Any]				=  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__  :       Optional[int]				=  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__  :       Optional[Any]				=  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] ,lowerCamelCase_ ,atol=1E-4)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												lowerCAmelCase__  :       Tuple				=  torch.tensor([-0.9866, 0.2392, -1.1241])
								elif mobilevit_name == "mobilevit_xs":
												lowerCAmelCase__  :       List[str]				=  torch.tensor([-2.4761, -0.9399, -1.9587])
								elif mobilevit_name == "mobilevit_xxs":
												lowerCAmelCase__  :       Any				=  torch.tensor([-1.9364, -1.2327, -0.4653])
								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:
								lowerCAmelCase__  :       Any				=  {
								    '''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__  :       List[Any]				=  model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowerCamelCase_ ,organization='''apple''')
								model.push_to_hub(lowerCamelCase_ ,organization='''apple''')


if __name__ == "__main__":
							_UpperCAmelCase       :	Dict            =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.'
							)

							_UpperCAmelCase       :	Union[str, Any]            =parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       Dict				=  f"""Input value of [number={number}] must be an integer"""
								raise TypeError(lowerCamelCase_)

				if number < 1:
								lowerCAmelCase__  :       Any				=  f"""Input value of [number={number}] must be > 0"""
								raise ValueError(lowerCamelCase_)

				lowerCAmelCase__  :       List[str]				=  1

				for i in range(1 ,lowerCamelCase_):
								current_number *= 4 * i - 2
								current_number //= i + 1

				return current_number


if __name__ == "__main__":
							import doctest

							doctest.testmod()

