
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()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase=False )		->				Tuple:

		"""simple docstring"""






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

		return new_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		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)
						_UpperCamelCase						    =	state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
						_UpperCamelCase						    =	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
						_UpperCamelCase						    =	kv_weight[
						    : config.hidden_sizes[i], :
						]
						_UpperCamelCase						    =	kv_bias[: config.hidden_sizes[i]]
						_UpperCamelCase						    =	kv_weight[
						    config.hidden_sizes[i] :, :
						]
						_UpperCamelCase						    =	kv_bias[
						    config.hidden_sizes[i] :
						]


def 							__A()		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		_UpperCamelCase						    =	Image.open(requests.get(lowerCAmelCase						,  stream=lowerCAmelCase ).raw )

		return image


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	SegformerConfig()
		_UpperCamelCase						    =	False

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

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

		# load image processor (only resize + normalize)
		_UpperCamelCase						    =	SegformerImageProcessor(
		    image_scale=(5_1_2, 5_1_2)						,  keep_ratio=lowerCAmelCase						,  align=lowerCAmelCase						,  do_random_crop=lowerCAmelCase )

		# prepare image
		_UpperCamelCase						    =	prepare_img()
		_UpperCamelCase						    =	image_processor(images=lowerCAmelCase						,  return_tensors="""pt""" ).pixel_values

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

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

		# rename keys
		_UpperCamelCase						    =	rename_keys(lowerCAmelCase						,  encoder_only=lowerCAmelCase )
		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(lowerCAmelCase						,  lowerCAmelCase )

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

		# forward pass
		_UpperCamelCase						    =	model(lowerCAmelCase )
		_UpperCamelCase						    =	outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [
				            [-1.13_72e01, -1.27_87e01, -1.34_77e01],
				            [-1.25_36e01, -1.41_94e01, -1.44_09e01],
				            [-1.32_17e01, -1.48_88e01, -1.53_27e01],
				        ],
				        [
				            [-1.47_91e01, -1.71_22e01, -1.82_77e01],
				            [-1.71_63e01, -1.91_92e01, -1.95_33e01],
				            [-1.78_97e01, -1.99_91e01, -2.03_15e01],
				        ],
				        [
				            [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
				            [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
				            [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
				        ],
				    ] )
		elif model_name == "segformer.b0.768x768.city.160k":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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":
				_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	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]						,  lowerCAmelCase						,  atol=1e-2 )

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


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





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__	=							{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MCTCTForCTC",
		    "MCTCTModel",
		    "MCTCTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



