






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__       =     logging.get_logger(__name__)

def _UpperCAmelCase   (	a						:	int  ,					a						:	str=False ):
							snake_case__	  =       OrderedDict()
							for key, value in state_dict.items():
														if encoder_only and not key.startswith("""head""" ):
																					snake_case__	  =       """segformer.encoder.""" + key
														if key.startswith("""backbone""" ):
																					snake_case__	  =       key.replace("""backbone"""  ,					"""segformer.encoder""" )
														if "patch_embed" in key:
																					# replace for example patch_embed1 by patch_embeddings.0
																					snake_case__	  =       key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
																					snake_case__	  =       key.replace(F'''patch_embed{idx}'''  ,					F'''patch_embeddings.{int(a )-1}''' )
														if "norm" in key:
																					snake_case__	  =       key.replace("""norm"""  ,					"""layer_norm""" )
														if "segformer.encoder.layer_norm" in key:
																					# replace for example layer_norm1 by layer_norm.0
																					snake_case__	  =       key[key.find("""segformer.encoder.layer_norm""" ) + len("""segformer.encoder.layer_norm""" )]
																					snake_case__	  =       key.replace(F'''layer_norm{idx}'''  ,					F'''layer_norm.{int(a )-1}''' )
														if "layer_norm1" in key:
																					snake_case__	  =       key.replace("""layer_norm1"""  ,					"""layer_norm_1""" )
														if "layer_norm2" in key:
																					snake_case__	  =       key.replace("""layer_norm2"""  ,					"""layer_norm_2""" )
														if "block" in key:
																					# replace for example block1 by block.0
																					snake_case__	  =       key[key.find("""block""" ) + len("""block""" )]
																					snake_case__	  =       key.replace(F'''block{idx}'''  ,					F'''block.{int(a )-1}''' )
														if "attn.q" in key:
																					snake_case__	  =       key.replace("""attn.q"""  ,					"""attention.self.query""" )
														if "attn.proj" in key:
																					snake_case__	  =       key.replace("""attn.proj"""  ,					"""attention.output.dense""" )
														if "attn" in key:
																					snake_case__	  =       key.replace("""attn"""  ,					"""attention.self""" )
														if "fc1" in key:
																					snake_case__	  =       key.replace("""fc1"""  ,					"""dense1""" )
														if "fc2" in key:
																					snake_case__	  =       key.replace("""fc2"""  ,					"""dense2""" )
														if "linear_pred" in key:
																					snake_case__	  =       key.replace("""linear_pred"""  ,					"""classifier""" )
														if "linear_fuse" in key:
																					snake_case__	  =       key.replace("""linear_fuse.conv"""  ,					"""linear_fuse""" )
																					snake_case__	  =       key.replace("""linear_fuse.bn"""  ,					"""batch_norm""" )
														if "linear_c" in key:
																					# replace for example linear_c4 by linear_c.3
																					snake_case__	  =       key[key.find("""linear_c""" ) + len("""linear_c""" )]
																					snake_case__	  =       key.replace(F'''linear_c{idx}'''  ,					F'''linear_c.{int(a )-1}''' )
														if key.startswith("""head""" ):
																					snake_case__	  =       key.replace("""head"""  ,					"""classifier""" )
														snake_case__	  =       value

							return new_state_dict

def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	Any ):
							# for each of the encoder blocks:
							for i in range(config.num_encoder_blocks ):
														for j in range(config.depths[i] ):
																					# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																					snake_case__	  =       state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight''' )
																					snake_case__	  =       state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias''' )
																					# next, add keys and values (in that order) to the state dict
																					snake_case__	  =       kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					snake_case__	  =       kv_bias[: config.hidden_sizes[i]]
																					snake_case__	  =       kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					snake_case__	  =       kv_bias[
																					    config.hidden_sizes[i] :
																					]

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

							return image

@torch.no_grad()
def _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Optional[Any]  ,					a						:	Any ):
							snake_case__	  =       SegformerConfig()
							snake_case__	  =       False

							# set attributes based on model_name
							snake_case__	  =       """huggingface/label-files"""
							if "segformer" in model_name:
														snake_case__	  =       model_name[len("""segformer.""" ) : len("""segformer.""" ) + 2]
														if "ade" in model_name:
																					snake_case__	  =       150
																					snake_case__	  =       """ade20k-id2label.json"""
																					snake_case__	  =       (1, 150, 128, 128)
														elif "city" in model_name:
																					snake_case__	  =       19
																					snake_case__	  =       """cityscapes-id2label.json"""
																					snake_case__	  =       (1, 19, 128, 128)
														else:
																					raise ValueError(F'''Model {model_name} not supported''' )
							elif "mit" in model_name:
														snake_case__	  =       True
														snake_case__	  =       model_name[4:6]
														snake_case__	  =       1000
														snake_case__	  =       """imagenet-1k-id2label.json"""
														snake_case__	  =       (1, 1000)
							else:
														raise ValueError(F'''Model {model_name} not supported''' )

							# set config attributes
							snake_case__	  =       json.load(open(hf_hub_download(a  ,					a  ,					repo_type="""dataset""" )  ,					"""r""" ) )
							snake_case__	  =       {int(a ): v for k, v in idalabel.items()}
							snake_case__	  =       idalabel
							snake_case__	  =       {v: k for k, v in idalabel.items()}
							if size == "b0":
														pass
							elif size == "b1":
														snake_case__	  =       [64, 128, 320, 512]
														snake_case__	  =       256
							elif size == "b2":
														snake_case__	  =       [64, 128, 320, 512]
														snake_case__	  =       768
														snake_case__	  =       [3, 4, 6, 3]
							elif size == "b3":
														snake_case__	  =       [64, 128, 320, 512]
														snake_case__	  =       768
														snake_case__	  =       [3, 4, 18, 3]
							elif size == "b4":
														snake_case__	  =       [64, 128, 320, 512]
														snake_case__	  =       768
														snake_case__	  =       [3, 8, 27, 3]
							elif size == "b5":
														snake_case__	  =       [64, 128, 320, 512]
														snake_case__	  =       768
														snake_case__	  =       [3, 6, 40, 3]
							else:
														raise ValueError(F'''Size {size} not supported''' )

							# load image processor (only resize + normalize)
							snake_case__	  =       SegformerImageProcessor(
							    image_scale=(512, 512)  ,					keep_ratio=a  ,					align=a  ,					do_random_crop=a )

							# prepare image
							snake_case__	  =       prepare_img()
							snake_case__	  =       image_processor(images=a  ,					return_tensors="""pt""" ).pixel_values

							logger.info(F'''Converting model {model_name}...''' )

							# load original state dict
							if encoder_only:
														snake_case__	  =       torch.load(a  ,					map_location=torch.device("""cpu""" ) )
							else:
														snake_case__	  =       torch.load(a  ,					map_location=torch.device("""cpu""" ) )["""state_dict"""]

							# rename keys
							snake_case__	  =       rename_keys(a  ,					encoder_only=a )
							if not encoder_only:
														del state_dict["decode_head.conv_seg.weight"]
														del state_dict["decode_head.conv_seg.bias"]

							# key and value matrices need special treatment
							read_in_k_v(a  ,					a )

							# create HuggingFace model and load state dict
							if encoder_only:
														snake_case__	  =       False
														snake_case__	  =       SegformerForImageClassification(a )
							else:
														snake_case__	  =       SegformerForSemanticSegmentation(a )
							model.load_state_dict(a )
							model.eval()

							# forward pass
							snake_case__	  =       model(a )
							snake_case__	  =       outputs.logits

							# set expected_slice based on model name
							# ADE20k checkpoints
							if model_name == "segformer.b0.512x512.ade.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
														        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
														        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
														    ] )
							elif model_name == "segformer.b1.512x512.ade.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
														        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
														        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
														    ] )
							elif model_name == "segformer.b2.512x512.ade.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
														        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
														        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
														    ] )
							elif model_name == "segformer.b3.512x512.ade.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
														        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
														        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
														    ] )
							elif model_name == "segformer.b4.512x512.ade.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
														        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
														        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
														    ] )
							elif model_name == "segformer.b5.640x640.ade.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
														        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
														        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
														    ] )
							# Cityscapes checkpoints
							elif model_name == "segformer.b0.1024x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
														        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
														        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
														    ] )
							elif model_name == "segformer.b0.512x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
														        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
														        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
														    ] )
							elif model_name == "segformer.b0.640x1280.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [
														            [-1.1372e01, -1.2787e01, -1.3477e01],
														            [-1.2536e01, -1.4194e01, -1.4409e01],
														            [-1.3217e01, -1.4888e01, -1.5327e01],
														        ],
														        [
														            [-1.4791e01, -1.7122e01, -1.8277e01],
														            [-1.7163e01, -1.9192e01, -1.9533e01],
														            [-1.7897e01, -1.9991e01, -2.0315e01],
														        ],
														        [
														            [7.6723e-01, 4.1921e-01, -7.7878e-02],
														            [4.7772e-01, 9.5557e-03, -2.8082e-01],
														            [3.6032e-01, -2.4826e-01, -5.1168e-01],
														        ],
														    ] )
							elif model_name == "segformer.b0.768x768.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
														        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
														        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
														    ] )
							elif model_name == "segformer.b1.1024x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
														        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
														        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
														    ] )
							elif model_name == "segformer.b2.1024x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
														        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
														        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
														    ] )
							elif model_name == "segformer.b3.1024x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
														        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
														        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
														    ] )
							elif model_name == "segformer.b4.1024x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
														        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
														        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
														    ] )
							elif model_name == "segformer.b5.1024x1024.city.160k":
														snake_case__	  =       torch.tensor(
														    [
														        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
														        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
														        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
														    ] )
							else:
														snake_case__	  =       logits.argmax(-1 ).item()
														print("""Predicted class:"""  ,					model.config.idalabel[predicted_class_idx] )

							# verify logits
							if not encoder_only:
														assert logits.shape == expected_shape
														assert torch.allclose(logits[0, :3, :3, :3]  ,					a  ,					atol=1e-2 )

							# finally, save model and image processor
							logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
							Path(a ).mkdir(exist_ok=a )
							model.save_pretrained(a )
							image_processor.save_pretrained(a )


if __name__ == "__main__":
	a__       =     argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name""",
	    default="""segformer.b0.512x512.ade.160k""",
	    type=str,
	    help="""Name of the model you'd like to convert.""",
	)
	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	a__       =     parser.parse_args()
	convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








from math import factorial

a__       =     {str(digit): factorial(digit) for digit in range(1_0)}

def _UpperCAmelCase   (	a						:	int ):
							if not isinstance(a  ,					a ):
														raise TypeError("""Parameter number must be int""" )

							if number < 0:
														raise ValueError("""Parameter number must be greater than or equal to 0""" )

							# Converts number in string to iterate on its digits and adds its factorial.
							return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )

def _UpperCAmelCase   (	a						:	int = 60  ,					a						:	int = 100_0000 ):

							if not isinstance(a  ,					a ) or not isinstance(a  ,					a ):
														raise TypeError("""Parameters chain_length and number_limit must be int""" )

							if chain_length <= 0 or number_limit <= 0:
														raise ValueError(
														    """Parameters chain_length and number_limit must be greater than 0""" )

							# the counter for the chains with the exact desired length
							snake_case__	  =       0
							# the cached sizes of the previous chains
							snake_case__	  =       {}

							for start_chain_element in range(1  ,					a ):
														# The temporary set will contain the elements of the chain
														snake_case__	  =       set()
														snake_case__	  =       0

														# Stop computing the chain when you find a cached size, a repeating item or the
														# length is greater then the desired one.
														snake_case__	  =       start_chain_element
														while (
														    chain_element not in chain_sets_lengths
														    and chain_element not in chain_set
														    and chain_set_length <= chain_length
														):
																					chain_set.add(a )
																					chain_set_length += 1
																					snake_case__	  =       digit_factorial_sum(a )

														if chain_element in chain_sets_lengths:
																					chain_set_length += chain_sets_lengths[chain_element]

														snake_case__	  =       chain_set_length

														# If chain contains the exact amount of elements increase the counter
														if chain_set_length == chain_length:
																					chains_counter += 1

							return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
