






"""simple docstring"""

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__          = logging.get_logger(__name__)
lowercase__          = {"""vocab_file""": """spiece.model"""}

lowercase__          = {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

lowercase__          = {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

lowercase__          = """▁"""


class 		__lowerCamelCase				( snake_case_			):





		'''simple docstring'''






		a_	:				str            =							VOCAB_FILES_NAMES
		a_	:				Union[str, Any]            =							PRETRAINED_VOCAB_FILES_MAP
		a_	:				List[Any]            =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





		def __init__(   self							:	str						,			a_							:	Union[str, Any]						,			a_							:	Any=True						,			a_							:	Any=True						,			a_							:	List[Any]=False						,			a_							:	Dict="[CLS]"						,			a_							:	Optional[Any]="[SEP]"						,			a_							:	List[Any]="<unk>"						,			a_							:	str="[SEP]"						,			a_							:	List[str]="<pad>"						,			a_							:	Optional[int]="[CLS]"						,			a_							:	List[str]="[MASK]"						,			a_							:	List[str] = None						,			**a_							:	List[str]						,			):
				# Mask token behave like a normal word, i.e. include the space before it and
				# is included in the raw text, there should be a match in a non-normalized sentence.
				lowerCAmelCase_   :							Optional[int]									=			(
				    AddedToken(a_						,			lstrip=a_						,			rstrip=a_						,			normalized=a_	)
				    if isinstance(a_						,			a_	)
				    else mask_token
				)

				lowerCAmelCase_   :							Tuple									=			{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=a_						,			remove_space=a_						,			keep_accents=a_						,			bos_token=a_						,			eos_token=a_						,			unk_token=a_						,			sep_token=a_						,			pad_token=a_						,			cls_token=a_						,			mask_token=a_						,			sp_model_kwargs=self.sp_model_kwargs						,			**a_						,			)

				lowerCAmelCase_   :							List[str]									=			do_lower_case
				lowerCAmelCase_   :							Optional[Any]									=			remove_space
				lowerCAmelCase_   :							List[Any]									=			keep_accents
				lowerCAmelCase_   :							Union[str, Any]									=			vocab_file

				lowerCAmelCase_   :							Tuple									=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(a_	)





		@property
		def    lowerCamelCase       (   self							:	List[str]	):
				return len(self.sp_model	)





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Optional[Any]									=			{self.convert_ids_to_tokens(a_	): i for i in range(self.vocab_size	)}
				vocab.update(self.added_tokens_encoder	)
				return vocab





		def __getstate__(   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Tuple									=			self.__dict__.copy()
				lowerCAmelCase_   :							Optional[int]									=			None
				return state





		def __setstate__(   self							:	List[Any]						,			a_							:	Optional[Any]	):
				lowerCAmelCase_   :							int									=			d

				# for backward compatibility
				if not hasattr(self						,			"sp_model_kwargs"	):
						lowerCAmelCase_   :							List[str]									=			{}

				lowerCAmelCase_   :							str									=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(self.vocab_file	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	Union[str, Any]	):
				if self.remove_space:
						lowerCAmelCase_   :							Any									=			" ".join(inputs.strip().split()	)
				else:
						lowerCAmelCase_   :							Any									=			inputs
				lowerCAmelCase_   :							Optional[Any]									=			outputs.replace("``"						,			"\""	).replace("\'\'"						,			"\""	)

				if not self.keep_accents:
						lowerCAmelCase_   :							Dict									=			unicodedata.normalize("NFKD"						,			a_	)
						lowerCAmelCase_   :							Union[str, Any]									=			"".join([c for c in outputs if not unicodedata.combining(a_	)]	)
				if self.do_lower_case:
						lowerCAmelCase_   :							Optional[int]									=			outputs.lower()

				return outputs





		def    lowerCamelCase       (   self							:	str						,			a_							:	Optional[int]	):
				lowerCAmelCase_   :							Tuple									=			self.preprocess_text(a_	)
				lowerCAmelCase_   :							Optional[int]									=			self.sp_model.encode(a_						,			out_type=a_	)
				lowerCAmelCase_   :							Optional[int]									=			[]
				for piece in pieces:
						if len(a_	) > 1 and piece[-1] == str(","	) and piece[-2].isdigit():
								lowerCAmelCase_   :							Any									=			self.sp_model.EncodeAsPieces(piece[:-1].replace(a_						,			""	)	)
								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
										if len(cur_pieces[0]	) == 1:
												lowerCAmelCase_   :							Optional[Any]									=			cur_pieces[1:]
										else:
												lowerCAmelCase_   :							Union[str, Any]									=			cur_pieces[0][1:]
								cur_pieces.append(piece[-1]	)
								new_pieces.extend(a_	)
						else:
								new_pieces.append(a_	)

				return new_pieces





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	List[str]	):
				return self.sp_model.PieceToId(a_	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	List[Any]	):
				return self.sp_model.IdToPiece(a_	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	Optional[Any]	):
				lowerCAmelCase_   :							str									=			[]
				lowerCAmelCase_   :							int									=			""
				lowerCAmelCase_   :							Union[str, Any]									=			False
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								if not prev_is_special:
										out_string += " "
								out_string += self.sp_model.decode(a_	) + token
								lowerCAmelCase_   :							Optional[int]									=			True
								lowerCAmelCase_   :							Tuple									=			[]
						else:
								current_sub_tokens.append(a_	)
								lowerCAmelCase_   :							Tuple									=			False
				out_string += self.sp_model.decode(a_	)
				return out_string.strip()





		def    lowerCamelCase       (   self							:	Any						,			a_							:	int						,			a_							:	Optional[Any] = None	):
				lowerCAmelCase_   :							Tuple									=			[self.sep_token_id]
				lowerCAmelCase_   :							Optional[Any]									=			[self.cls_token_id]
				if token_ids_a is None:
						return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep





		def    lowerCamelCase       (   self							:	str						,			a_							:	Union[str, Any]						,			a_							:	List[str] = None						,			a_							:	str = False	):

				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=a_						,			token_ids_a=a_						,			already_has_special_tokens=a_	)

				if token_ids_a is not None:
						return [1] + ([0] * len(a_	)) + [1] + ([0] * len(a_	)) + [1]
				return [1] + ([0] * len(a_	)) + [1]





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	Optional[Any]						,			a_							:	Any = None	):
				lowerCAmelCase_   :							List[str]									=			[self.sep_token_id]
				lowerCAmelCase_   :							int									=			[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep	) * [0]
				return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





		def    lowerCamelCase       (   self							:	List[str]						,			a_							:	int						,			a_							:	Any = None	):
				if not os.path.isdir(a_	):
						logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
						return
				lowerCAmelCase_   :							int									=			os.path.join(
				    a_						,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

				if os.path.abspath(self.vocab_file	) != os.path.abspath(a_	) and os.path.isfile(self.vocab_file	):
						copyfile(self.vocab_file						,			a_	)
				elif not os.path.isfile(self.vocab_file	):
						with open(a_						,			"wb"	) as fi:
								lowerCAmelCase_   :							Optional[int]									=			self.sp_model.serialized_model_proto()
								fi.write(a_	)

				return (out_vocab_file,)








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()
lowercase  							=					logging.get_logger(__name__)





def 						__UpperCAmelCase			(     a_							,     a_=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_							,     a_):
							# 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_							,     a_							,     a_):
							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_				=							1_50
																					snake_case_				=							'ade20k-id2label.json'
																					snake_case_				=							(1, 1_50, 1_28, 1_28)
														elif "city" in model_name:
																					snake_case_				=							19
																					snake_case_				=							'cityscapes-id2label.json'
																					snake_case_				=							(1, 19, 1_28, 1_28)
														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_				=							10_00
														snake_case_				=							'imagenet-1k-id2label.json'
														snake_case_				=							(1, 10_00)
							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, 1_28, 3_20, 5_12]
														snake_case_				=							2_56
							elif size == "b2":
														snake_case_				=							[64, 1_28, 3_20, 5_12]
														snake_case_				=							7_68
														snake_case_				=							[3, 4, 6, 3]
							elif size == "b3":
														snake_case_				=							[64, 1_28, 3_20, 5_12]
														snake_case_				=							7_68
														snake_case_				=							[3, 4, 18, 3]
							elif size == "b4":
														snake_case_				=							[64, 1_28, 3_20, 5_12]
														snake_case_				=							7_68
														snake_case_				=							[3, 8, 27, 3]
							elif size == "b5":
														snake_case_				=							[64, 1_28, 3_20, 5_12]
														snake_case_				=							7_68
														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=(5_12, 5_12)							,     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(
														    [
														        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
														        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
														        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
														    ])
							elif model_name == "segformer.b1.512x512.ade.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
														        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
														        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
														    ])
							elif model_name == "segformer.b2.512x512.ade.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
														        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
														        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
														    ])
							elif model_name == "segformer.b3.512x512.ade.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
														        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
														        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
														    ])
							elif model_name == "segformer.b4.512x512.ade.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
														        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
														        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
														    ])
							elif model_name == "segformer.b5.640x640.ade.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
														        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
														        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
														    ])
							# Cityscapes checkpoints
							elif model_name == "segformer.b0.1024x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
														        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
														        [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
														    ])
							elif model_name == "segformer.b0.512x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
														        [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
														        [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
														    ])
							elif model_name == "segformer.b0.640x1280.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [
														            [-1.1_372E01, -1.2_787E01, -1.3_477E01],
														            [-1.2_536E01, -1.4_194E01, -1.4_409E01],
														            [-1.3_217E01, -1.4_888E01, -1.5_327E01],
														        ],
														        [
														            [-1.4_791E01, -1.7_122E01, -1.8_277E01],
														            [-1.7_163E01, -1.9_192E01, -1.9_533E01],
														            [-1.7_897E01, -1.9_991E01, -2.0_315E01],
														        ],
														        [
														            [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
														            [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
														            [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
														        ],
														    ])
							elif model_name == "segformer.b0.768x768.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
														        [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
														        [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
														    ])
							elif model_name == "segformer.b1.1024x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
														        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
														        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
														    ])
							elif model_name == "segformer.b2.1024x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
														        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
														        [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
														    ])
							elif model_name == "segformer.b3.1024x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
														        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
														        [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
														    ])
							elif model_name == "segformer.b4.1024x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
														        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
														        [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
														    ])
							elif model_name == "segformer.b5.1024x1024.city.160k":
														snake_case_				=							torch.tensor(
														    [
														        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
														        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
														        [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
														    ])
							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__":
	lowercase  							=					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."
	)
	lowercase  							=					parser.parse_args()
	convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





