





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()
snake_case_							=			logging.get_logger(__name__)


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

		return new_state_dict


def 					A__	(       SCREAMING_SNAKE_CASE_     :   Optional[Any]							,       SCREAMING_SNAKE_CASE_     :   Dict		)   ->     Union[str, 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)
						lowerCamelCase   : Tuple								=state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"		)
						lowerCamelCase   : Any								=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
						lowerCamelCase   : Union[str, Any]								=kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase   : str								=kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase   : Dict								=kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase   : Optional[int]								=kv_bias[
						    config.hidden_sizes[i] :
						]


def 					A__	(       )   ->     Optional[Any]:
		lowerCamelCase   : Optional[int]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCamelCase   : List[Any]								=Image.open(requests.get(SCREAMING_SNAKE_CASE_							,       stream=SCREAMING_SNAKE_CASE_		).raw		)

		return image


@torch.no_grad()
def 					A__	(       SCREAMING_SNAKE_CASE_     :   Optional[Any]							,       SCREAMING_SNAKE_CASE_     :   Union[str, Any]							,       SCREAMING_SNAKE_CASE_     :   Dict		)   ->     int:
		lowerCamelCase   : List[str]								=SegformerConfig()
		lowerCamelCase   : Any								=False

		# set attributes based on model_name
		lowerCamelCase   : Union[str, Any]								='''huggingface/label-files'''
		if "segformer" in model_name:
				lowerCamelCase   : Any								=model_name[len('''segformer.'''		) : len('''segformer.'''		) + 2]
				if "ade" in model_name:
						lowerCamelCase   : Dict								=1_5_0
						lowerCamelCase   : int								='''ade20k-id2label.json'''
						lowerCamelCase   : List[Any]								=(1, 1_5_0, 1_2_8, 1_2_8)
				elif "city" in model_name:
						lowerCamelCase   : Tuple								=1_9
						lowerCamelCase   : Tuple								='''cityscapes-id2label.json'''
						lowerCamelCase   : List[Any]								=(1, 1_9, 1_2_8, 1_2_8)
				else:
						raise ValueError(F"Model {model_name} not supported"		)
		elif "mit" in model_name:
				lowerCamelCase   : str								=True
				lowerCamelCase   : Any								=model_name[4:6]
				lowerCamelCase   : Union[str, Any]								=1_0_0_0
				lowerCamelCase   : Dict								='''imagenet-1k-id2label.json'''
				lowerCamelCase   : List[str]								=(1, 1_0_0_0)
		else:
				raise ValueError(F"Model {model_name} not supported"		)

		# set config attributes
		lowerCamelCase   : Optional[Any]								=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       repo_type='''dataset'''		)							,       '''r'''		)		)
		lowerCamelCase   : Any								={int(SCREAMING_SNAKE_CASE_		): v for k, v in idalabel.items()}
		lowerCamelCase   : Optional[int]								=idalabel
		lowerCamelCase   : Optional[Any]								={v: k for k, v in idalabel.items()}
		if size == "b0":
				pass
		elif size == "b1":
				lowerCamelCase   : List[str]								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : List[Any]								=2_5_6
		elif size == "b2":
				lowerCamelCase   : Dict								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : Optional[int]								=7_6_8
				lowerCamelCase   : Optional[Any]								=[3, 4, 6, 3]
		elif size == "b3":
				lowerCamelCase   : Optional[Any]								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : Tuple								=7_6_8
				lowerCamelCase   : Dict								=[3, 4, 1_8, 3]
		elif size == "b4":
				lowerCamelCase   : List[Any]								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : List[str]								=7_6_8
				lowerCamelCase   : List[Any]								=[3, 8, 2_7, 3]
		elif size == "b5":
				lowerCamelCase   : Optional[Any]								=[6_4, 1_2_8, 3_2_0, 5_1_2]
				lowerCamelCase   : List[str]								=7_6_8
				lowerCamelCase   : int								=[3, 6, 4_0, 3]
		else:
				raise ValueError(F"Size {size} not supported"		)

		# load image processor (only resize + normalize)
		lowerCamelCase   : List[Any]								=SegformerImageProcessor(
		    image_scale=(5_1_2, 5_1_2)							,       keep_ratio=SCREAMING_SNAKE_CASE_							,       align=SCREAMING_SNAKE_CASE_							,       do_random_crop=SCREAMING_SNAKE_CASE_		)

		# prepare image
		lowerCamelCase   : Optional[Any]								=prepare_img()
		lowerCamelCase   : Any								=image_processor(images=SCREAMING_SNAKE_CASE_							,       return_tensors='''pt'''		).pixel_values

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

		# load original state dict
		if encoder_only:
				lowerCamelCase   : int								=torch.load(SCREAMING_SNAKE_CASE_							,       map_location=torch.device('''cpu'''		)		)
		else:
				lowerCamelCase   : List[str]								=torch.load(SCREAMING_SNAKE_CASE_							,       map_location=torch.device('''cpu'''		)		)['''state_dict''']

		# rename keys
		lowerCamelCase   : str								=rename_keys(SCREAMING_SNAKE_CASE_							,       encoder_only=SCREAMING_SNAKE_CASE_		)
		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(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

		# create HuggingFace model and load state dict
		if encoder_only:
				lowerCamelCase   : Tuple								=False
				lowerCamelCase   : int								=SegformerForImageClassification(SCREAMING_SNAKE_CASE_		)
		else:
				lowerCamelCase   : str								=SegformerForSemanticSegmentation(SCREAMING_SNAKE_CASE_		)
		model.load_state_dict(SCREAMING_SNAKE_CASE_		)
		model.eval()

		# forward pass
		lowerCamelCase   : List[str]								=model(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : int								=outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				lowerCamelCase   : Union[str, Any]								=torch.tensor(
				    [
				        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
				        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
				        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
				    ]		)
		elif model_name == "segformer.b1.512x512.ade.160k":
				lowerCamelCase   : Optional[int]								=torch.tensor(
				    [
				        [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
				        [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
				        [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
				    ]		)
		elif model_name == "segformer.b2.512x512.ade.160k":
				lowerCamelCase   : str								=torch.tensor(
				    [
				        [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
				        [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
				        [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
				    ]		)
		elif model_name == "segformer.b3.512x512.ade.160k":
				lowerCamelCase   : int								=torch.tensor(
				    [
				        [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
				        [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
				        [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
				    ]		)
		elif model_name == "segformer.b4.512x512.ade.160k":
				lowerCamelCase   : str								=torch.tensor(
				    [
				        [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
				        [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
				        [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
				    ]		)
		elif model_name == "segformer.b5.640x640.ade.160k":
				lowerCamelCase   : Tuple								=torch.tensor(
				    [
				        [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
				        [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
				        [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
				    ]		)
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				lowerCamelCase   : str								=torch.tensor(
				    [
				        [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
				        [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
				        [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
				    ]		)
		elif model_name == "segformer.b0.512x1024.city.160k":
				lowerCamelCase   : int								=torch.tensor(
				    [
				        [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
				        [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
				        [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
				    ]		)
		elif model_name == "segformer.b0.640x1280.city.160k":
				lowerCamelCase   : str								=torch.tensor(
				    [
				        [
				            [-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
				            [-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
				            [-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
				        ],
				        [
				            [-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
				            [-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
				            [-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
				        ],
				        [
				            [7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
				            [4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
				            [3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
				        ],
				    ]		)
		elif model_name == "segformer.b0.768x768.city.160k":
				lowerCamelCase   : Optional[Any]								=torch.tensor(
				    [
				        [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
				        [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
				        [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
				    ]		)
		elif model_name == "segformer.b1.1024x1024.city.160k":
				lowerCamelCase   : Optional[int]								=torch.tensor(
				    [
				        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
				        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
				        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
				    ]		)
		elif model_name == "segformer.b2.1024x1024.city.160k":
				lowerCamelCase   : str								=torch.tensor(
				    [
				        [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
				        [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
				        [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
				    ]		)
		elif model_name == "segformer.b3.1024x1024.city.160k":
				lowerCamelCase   : List[Any]								=torch.tensor(
				    [
				        [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
				        [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
				        [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
				    ]		)
		elif model_name == "segformer.b4.1024x1024.city.160k":
				lowerCamelCase   : Optional[int]								=torch.tensor(
				    [
				        [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
				        [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
				        [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
				    ]		)
		elif model_name == "segformer.b5.1024x1024.city.160k":
				lowerCamelCase   : Tuple								=torch.tensor(
				    [
				        [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
				        [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
				        [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
				    ]		)
		else:
				lowerCamelCase   : str								=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]							,       SCREAMING_SNAKE_CASE_							,       atol=1E-2		)

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


if __name__ == "__main__":
				snake_case_							=			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.'''
				)
				snake_case_							=			parser.parse_args()
				convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			'''▁'''

snake_case_							=			{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

snake_case_							=			{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

snake_case_							=			{
    '''facebook/m2m100_418M''': 1_0_2_4,
}

# fmt: off
snake_case_							=			{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class        snake_case_		(			_A):
		lowerCamelCase    :Union[str, Any]						=     VOCAB_FILES_NAMES
		lowerCamelCase    :List[Any]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase    :str						=     PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase    :Union[str, Any]						=     ["input_ids", "attention_mask"]

		lowerCamelCase    :List[int]						=     []
		lowerCamelCase    :List[int]						=     []







		def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase=None			,     __lowercase=None			,     __lowercase="<s>"			,     __lowercase="</s>"			,     __lowercase="</s>"			,     __lowercase="<pad>"			,     __lowercase="<unk>"			,     __lowercase="m2m100"			,     __lowercase = None			,     __lowercase=8			,     **__lowercase			,     )				->   None:
				lowerCamelCase   : Union[str, Any]								={} if sp_model_kwargs is None else sp_model_kwargs

				lowerCamelCase   : List[str]								=language_codes
				lowerCamelCase   : int								=FAIRSEQ_LANGUAGE_CODES[language_codes]
				lowerCamelCase   : str								={lang_code: F"__{lang_code}__" for lang_code in fairseq_language_code}

				lowerCamelCase   : List[Any]								=kwargs.get('''additional_special_tokens'''			,     []   )
				kwargs["additional_special_tokens"] += [
				    self.get_lang_token(__lowercase   )
				    for lang_code in fairseq_language_code
				    if self.get_lang_token(__lowercase   ) not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=__lowercase			,     tgt_lang=__lowercase			,     bos_token=__lowercase			,     eos_token=__lowercase			,     sep_token=__lowercase			,     unk_token=__lowercase			,     pad_token=__lowercase			,     language_codes=__lowercase			,     sp_model_kwargs=self.sp_model_kwargs			,     num_madeup_words=__lowercase			,     **__lowercase			,     )

				lowerCamelCase   : Dict								=vocab_file
				lowerCamelCase   : List[Any]								=load_json(__lowercase   )
				lowerCamelCase   : Optional[int]								={v: k for k, v in self.encoder.items()}
				lowerCamelCase   : List[Any]								=spm_file
				lowerCamelCase   : str								=load_spm(__lowercase			,     self.sp_model_kwargs   )

				lowerCamelCase   : Tuple								=len(self.encoder   )

				lowerCamelCase   : Optional[int]								={
				    self.get_lang_token(__lowercase   ): self.encoder_size + i for i, lang_code in enumerate(__lowercase   )
				}
				lowerCamelCase   : Tuple								={lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowercase   )}
				lowerCamelCase   : Tuple								={v: k for k, v in self.lang_token_to_id.items()}

				lowerCamelCase   : Optional[Any]								=src_lang if src_lang is not None else '''en'''
				lowerCamelCase   : Any								=tgt_lang
				lowerCamelCase   : List[Any]								=self.get_lang_id(self._src_lang   )
				self.set_src_lang_special_tokens(self._src_lang   )

				lowerCamelCase   : Optional[Any]								=num_madeup_words







		@property
		def 			__lowercase   (		self   )				->   int:
				return len(self.encoder   ) + len(self.lang_token_to_id   )







		@property
		def 			__lowercase   (		self   )				->   str:
				return self._src_lang







		@src_lang.setter
		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				lowerCamelCase   : Any								=new_src_lang
				self.set_src_lang_special_tokens(self._src_lang   )







		def 			__lowercase   (		self			,     __lowercase   )				->   List[str]:
				return self.sp_model.encode(__lowercase			,     out_type=__lowercase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				if token in self.lang_token_to_id:
						return self.lang_token_to_id[token]
				return self.encoder.get(__lowercase			,     self.encoder[self.unk_token]   )







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				if index in self.id_to_lang_token:
						return self.id_to_lang_token[index]
				return self.decoder.get(__lowercase			,     self.unk_token   )







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				lowerCamelCase   : Dict								=[]
				lowerCamelCase   : Dict								=''''''
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								out_string += self.sp_model.decode(__lowercase   ) + token
								lowerCamelCase   : str								=[]
						else:
								current_sub_tokens.append(__lowercase   )
				out_string += self.sp_model.decode(__lowercase   )
				return out_string.strip()







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None			,     __lowercase = False   )				->   List[int]:

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

				lowerCamelCase   : int								=[1] * len(self.prefix_tokens   )
				lowerCamelCase   : List[str]								=[1] * len(self.suffix_tokens   )
				if token_ids_a is None:
						return prefix_ones + ([0] * len(__lowercase   )) + suffix_ones
				return prefix_ones + ([0] * len(__lowercase   )) + ([0] * len(__lowercase   )) + suffix_ones







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   List[int]:
				if token_ids_a is None:
						return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : Dict								={self.convert_ids_to_tokens(__lowercase   ): i for i in range(self.vocab_size   )}
				vocab.update(self.added_tokens_encoder   )
				return vocab







		def __getstate__(		self   )				->   Dict:
				lowerCamelCase   : Optional[Any]								=self.__dict__.copy()
				lowerCamelCase   : Union[str, Any]								=None
				return state







		def __setstate__(		self			,     __lowercase   )				->   None:
				lowerCamelCase   : int								=d

				# for backward compatibility
				if not hasattr(self			,     '''sp_model_kwargs'''   ):
						lowerCamelCase   : Optional[int]								={}

				lowerCamelCase   : Optional[Any]								=load_spm(self.spm_file			,     self.sp_model_kwargs   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   Tuple[str]:
				lowerCamelCase   : Optional[Any]								=Path(__lowercase   )
				if not save_dir.is_dir():
						raise OSError(F"{save_directory} should be a directory"   )
				lowerCamelCase   : List[str]								=save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
				)
				lowerCamelCase   : Tuple								=save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
				)

				save_json(self.encoder			,     __lowercase   )

				if os.path.abspath(self.spm_file   ) != os.path.abspath(__lowercase   ) and os.path.isfile(self.spm_file   ):
						copyfile(self.spm_file			,     __lowercase   )
				elif not os.path.isfile(self.spm_file   ):
						with open(__lowercase			,     '''wb'''   ) as fi:
								lowerCamelCase   : List[Any]								=self.sp_model.serialized_model_proto()
								fi.write(__lowercase   )

				return (str(__lowercase   ), str(__lowercase   ))







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = "en"			,     __lowercase = None			,     __lowercase = "ro"			,     **__lowercase			,     )				->   BatchEncoding:
				lowerCamelCase   : Union[str, Any]								=src_lang
				lowerCamelCase   : Optional[int]								=tgt_lang
				self.set_src_lang_special_tokens(self.src_lang   )
				return super().prepare_seqaseq_batch(__lowercase			,     __lowercase			,     **__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     **__lowercase   )				->   Optional[int]:
				if src_lang is None or tgt_lang is None:
						raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
				lowerCamelCase   : List[Any]								=src_lang
				lowerCamelCase   : str								=self(__lowercase			,     add_special_tokens=__lowercase			,     **__lowercase   )
				lowerCamelCase   : Tuple								=self.get_lang_id(__lowercase   )
				lowerCamelCase   : Optional[int]								=tgt_lang_id
				return inputs







		def 			__lowercase   (		self   )				->   List[str]:
				self.set_src_lang_special_tokens(self.src_lang   )







		def 			__lowercase   (		self   )				->   List[str]:
				self.set_tgt_lang_special_tokens(self.tgt_lang   )







		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				lowerCamelCase   : Union[str, Any]								=self.get_lang_token(__lowercase   )
				lowerCamelCase   : List[Any]								=self.lang_token_to_id[lang_token]
				lowerCamelCase   : Optional[Any]								=[self.cur_lang_id]
				lowerCamelCase   : Union[str, Any]								=[self.eos_token_id]







		def 			__lowercase   (		self			,     __lowercase   )				->   None:
				lowerCamelCase   : Tuple								=self.get_lang_token(__lowercase   )
				lowerCamelCase   : Tuple								=self.lang_token_to_id[lang_token]
				lowerCamelCase   : List[Any]								=[self.cur_lang_id]
				lowerCamelCase   : Tuple								=[self.eos_token_id]







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				return self.lang_code_to_token[lang]







		def 			__lowercase   (		self			,     __lowercase   )				->   int:
				lowerCamelCase   : List[str]								=self.get_lang_token(__lowercase   )
				return self.lang_token_to_id[lang_token]


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     sentencepiece.SentencePieceProcessor:
		lowerCamelCase   : List[Any]								=sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE_		)
		spm.Load(str(SCREAMING_SNAKE_CASE_		)		)
		return spm


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Union[Dict, List]:
		with open(SCREAMING_SNAKE_CASE_							,       '''r'''		) as f:
				return json.load(SCREAMING_SNAKE_CASE_		)



def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     None:
		with open(SCREAMING_SNAKE_CASE_							,       '''w'''		) as f:
				json.dump(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       indent=2		)

