


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):
					__SCREAMING_SNAKE_CASE		     =       MobileViTConfig()

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

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

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

					return config




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=False     ):


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

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

					if "exp_1x1" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("exp_1x1"						,		"expand_1x1"     )
					if "red_1x1" in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace("red_1x1"						,		"reduce_1x1"     )
					if ".local_rep.conv_3x3." in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace(".local_rep.conv_3x3."						,		".conv_kxk."     )
					if ".local_rep.conv_1x1." in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace(".local_rep.conv_1x1."						,		".conv_1x1."     )
					if ".norm." in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace(".norm."						,		".normalization."     )
					if ".conv." in name:
										__SCREAMING_SNAKE_CASE		     =       name.replace(".conv."						,		".convolution."     )
					if ".conv_proj." in name:
										__SCREAMING_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:
																				__SCREAMING_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:
																				__SCREAMING_SNAKE_CASE		     =       name.replace(f""".{i}.{j}."""						,		f""".{i}."""     )
																				if "expand_1x1" in name:
																									__SCREAMING_SNAKE_CASE		     =       name.replace("expand_1x1"						,		"downsampling_layer.expand_1x1"     )
																				if "conv_3x3" in name:
																									__SCREAMING_SNAKE_CASE		     =       name.replace("conv_3x3"						,		"downsampling_layer.conv_3x3"     )
																				if "reduce_1x1" in name:
																									__SCREAMING_SNAKE_CASE		     =       name.replace("reduce_1x1"						,		"downsampling_layer.reduce_1x1"     )

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

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

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

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

					return name




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=False     ):


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

					for key in orig_state_dict.copy().keys():
										__SCREAMING_SNAKE_CASE		     =       orig_state_dict.pop(lowerCAmelCase_     )

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

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

					return orig_state_dict




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




@torch.no_grad()
def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=False     ):
					__SCREAMING_SNAKE_CASE		     =       get_mobilevit_config(lowerCAmelCase_     )

					# load original state_dict
					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_						,		map_location="cpu"     )

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

					__SCREAMING_SNAKE_CASE		     =       convert_state_dict(lowerCAmelCase_						,		lowerCAmelCase_     )
					model.load_state_dict(lowerCAmelCase_     )

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

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

										if mobilevit_name == "deeplabv3_mobilevit_s":
															__SCREAMING_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":
															__SCREAMING_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":
															__SCREAMING_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]						,		lowerCAmelCase_						,		atol=1E-4     )
					else:
										assert logits.shape == (1, 1000)

										if mobilevit_name == "mobilevit_s":
															__SCREAMING_SNAKE_CASE		     =       torch.tensor([-0.9866, 0.2392, -1.1241]     )
										elif mobilevit_name == "mobilevit_xs":
															__SCREAMING_SNAKE_CASE		     =       torch.tensor([-2.4761, -0.9399, -1.9587]     )
										elif mobilevit_name == "mobilevit_xxs":
															__SCREAMING_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]						,		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:
										__SCREAMING_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..."     )
										__SCREAMING_SNAKE_CASE		     =       model_mapping[mobilevit_name]
										image_processor.push_to_hub(lowerCAmelCase_						,		organization="apple"     )
										model.push_to_hub(lowerCAmelCase_						,		organization="apple"     )


if __name__ == "__main__":
		a__       :							int        =  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__       :							Union[str, Any]        =  parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




"""simple docstring"""







import functools




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for day in days     ):
										raise ValueError("The parameter days should be a list of integers"     )

					if len(lowerCAmelCase_     ) != 3 or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for cost in costs     ):
										raise ValueError("The parameter costs should be a list of three integers"     )

					if len(lowerCAmelCase_     ) == 0:
										return 0

					if min(lowerCAmelCase_     ) <= 0:
										raise ValueError("All days elements should be greater than 0"     )

					if max(lowerCAmelCase_     ) >= 366:
										raise ValueError("All days elements should be less than 366"     )

					__SCREAMING_SNAKE_CASE		     =       set(lowerCAmelCase_     )

					@functools.cache
					def dynamic_programming(lowerCAmelCase_     ) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1     )

										return min(
										    costs[0] + dynamic_programming(index + 1     )						,		costs[1] + dynamic_programming(index + 7     )						,		costs[2] + dynamic_programming(index + 30     )						,		)

					return dynamic_programming(1     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
