






'''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   (	a						:	Optional[Any] ):
							snake_case__	  =       MobileViTConfig()

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

							if mobilevit_name.startswith("""deeplabv3_""" ):
														snake_case__	  =       512
														snake_case__	  =       16
														snake_case__	  =       21
														snake_case__	  =       """pascal-voc-id2label.json"""
							else:
														snake_case__	  =       1000
														snake_case__	  =       """imagenet-1k-id2label.json"""

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

							return config

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]=False ):







							for i in range(1  ,					6 ):
														if F'''layer_{i}.''' in name:
																					snake_case__	  =       name.replace(F'''layer_{i}.'''  ,					F'''encoder.layer.{i - 1}.''' )

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

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

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

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

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

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

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

							return name

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Any  ,					a						:	Dict=False ):







							if base_model:
														snake_case__	  =       """"""
							else:
														snake_case__	  =       """mobilevit."""

							for key in orig_state_dict.copy().keys():
														snake_case__	  =       orig_state_dict.pop(__UpperCAmelCase )

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

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

							return orig_state_dict

def _UpperCAmelCase   (	):
							snake_case__	  =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case__	  =       Image.open(requests.get(__UpperCAmelCase  ,					stream=__UpperCAmelCase ).raw )
							return im

@torch.no_grad()
def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Optional[int]  ,					a						:	List[str]  ,					a						:	Optional[Any]=False ):
							snake_case__	  =       get_mobilevit_config(__UpperCAmelCase )

							# load original state_dict
							snake_case__	  =       torch.load(__UpperCAmelCase  ,					map_location="""cpu""" )

							# load 🤗 model
							if mobilevit_name.startswith("""deeplabv3_""" ):
														snake_case__	  =       MobileViTForSemanticSegmentation(__UpperCAmelCase ).eval()
							else:
														snake_case__	  =       MobileViTForImageClassification(__UpperCAmelCase ).eval()

							snake_case__	  =       convert_state_dict(__UpperCAmelCase  ,					__UpperCAmelCase )
							model.load_state_dict(__UpperCAmelCase )

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

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					snake_case__	  =       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":
																					snake_case__	  =       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":
																					snake_case__	  =       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, 1000)

														if mobilevit_name == "mobilevit_s":
																					snake_case__	  =       torch.tensor([-0.9866, 0.2392, -1.1241] )
														elif mobilevit_name == "mobilevit_xs":
																					snake_case__	  =       torch.tensor([-2.4761, -0.9399, -1.9587] )
														elif mobilevit_name == "mobilevit_xxs":
																					snake_case__	  =       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:
														snake_case__	  =       {
														    """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...""" )
														snake_case__	  =       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
	)








def _UpperCAmelCase   (	a						:	int = 400_0000 ):
							snake_case__	  =       [0, 1]
							snake_case__	  =       0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1] )
														if fib[i + 2] > n:
																					break
														i += 1
							snake_case__	  =       0
							for j in range(len(a ) - 1 ):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


if __name__ == "__main__":
	print(F'''{solution() = }''')
