
"""simple docstring"""


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

	return new_state_dict






def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     List[str]     ):







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






def       _snake_case				(		):
	A						   =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	A						   =   Image.open(requests.get(lowercase__	,      stream=lowercase__     ).raw     )

	return image






@torch.no_grad()
def       _snake_case				(		snake_case__   :     List[str]	,      snake_case__   :     int	,      snake_case__   :     Optional[int]     ):
	A						   =   SegformerConfig()
	A						   =   False

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

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

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

	# prepare image
	A						   =   prepare_img()
	A						   =   image_processor(images=lowercase__	,      return_tensors='pt'     ).pixel_values

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

	# load original state dict
	if encoder_only:
		A						   =   torch.load(lowercase__	,      map_location=torch.device('cpu'     )     )
	else:
		A						   =   torch.load(lowercase__	,      map_location=torch.device('cpu'     )     )['state_dict']

	# rename keys
	A						   =   rename_keys(lowercase__	,      encoder_only=lowercase__     )
	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(lowercase__	,      lowercase__     )

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

	# forward pass
	A						   =   model(lowercase__     )
	A						   =   outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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:
		A						   =   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]	,      lowercase__	,      atol=1e-2     )

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


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)




"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







@require_vision
@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )