






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()
UpperCamelCase			    =     logging.get_logger(__name__)

def 	__lowerCamelCase      (		snake_case__	,snake_case__=False		)							->  Any:






						"""simple docstring"""





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

						return new_state_dict

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Dict:






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

def 	__lowerCamelCase      (		)							->  Union[str, Any]:






						"""simple docstring"""





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

						return image

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  SegformerConfig()
						_SCREAMING_SNAKE_CASE     				=  False

						# set attributes based on model_name
						_SCREAMING_SNAKE_CASE     				=  """huggingface/label-files"""
						if "segformer" in model_name:
												_SCREAMING_SNAKE_CASE     				=  model_name[len("""segformer."""		) : len("""segformer."""		) + 2]
												if "ade" in model_name:
																		_SCREAMING_SNAKE_CASE     				=  1_50
																		_SCREAMING_SNAKE_CASE     				=  """ade20k-id2label.json"""
																		_SCREAMING_SNAKE_CASE     				=  (1, 1_50, 1_28, 1_28)
												elif "city" in model_name:
																		_SCREAMING_SNAKE_CASE     				=  19
																		_SCREAMING_SNAKE_CASE     				=  """cityscapes-id2label.json"""
																		_SCREAMING_SNAKE_CASE     				=  (1, 19, 1_28, 1_28)
												else:
																		raise ValueError(F'Model {model_name} not supported'		)
						elif "mit" in model_name:
												_SCREAMING_SNAKE_CASE     				=  True
												_SCREAMING_SNAKE_CASE     				=  model_name[4:6]
												_SCREAMING_SNAKE_CASE     				=  10_00
												_SCREAMING_SNAKE_CASE     				=  """imagenet-1k-id2label.json"""
												_SCREAMING_SNAKE_CASE     				=  (1, 10_00)
						else:
												raise ValueError(F'Model {model_name} not supported'		)

						# set config attributes
						_SCREAMING_SNAKE_CASE     				=  json.load(open(hf_hub_download(snake_case__	,snake_case__	,repo_type="""dataset"""		)	,"""r"""		)		)
						_SCREAMING_SNAKE_CASE     				=  {int(snake_case__		): v for k, v in idalabel.items()}
						_SCREAMING_SNAKE_CASE     				=  idalabel
						_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in idalabel.items()}
						if size == "b0":
												pass
						elif size == "b1":
												_SCREAMING_SNAKE_CASE     				=  [64, 1_28, 3_20, 5_12]
												_SCREAMING_SNAKE_CASE     				=  2_56
						elif size == "b2":
												_SCREAMING_SNAKE_CASE     				=  [64, 1_28, 3_20, 5_12]
												_SCREAMING_SNAKE_CASE     				=  7_68
												_SCREAMING_SNAKE_CASE     				=  [3, 4, 6, 3]
						elif size == "b3":
												_SCREAMING_SNAKE_CASE     				=  [64, 1_28, 3_20, 5_12]
												_SCREAMING_SNAKE_CASE     				=  7_68
												_SCREAMING_SNAKE_CASE     				=  [3, 4, 18, 3]
						elif size == "b4":
												_SCREAMING_SNAKE_CASE     				=  [64, 1_28, 3_20, 5_12]
												_SCREAMING_SNAKE_CASE     				=  7_68
												_SCREAMING_SNAKE_CASE     				=  [3, 8, 27, 3]
						elif size == "b5":
												_SCREAMING_SNAKE_CASE     				=  [64, 1_28, 3_20, 5_12]
												_SCREAMING_SNAKE_CASE     				=  7_68
												_SCREAMING_SNAKE_CASE     				=  [3, 6, 40, 3]
						else:
												raise ValueError(F'Size {size} not supported'		)

						# load image processor (only resize + normalize)
						_SCREAMING_SNAKE_CASE     				=  SegformerImageProcessor(
						    image_scale=(5_12, 5_12)	,keep_ratio=snake_case__	,align=snake_case__	,do_random_crop=snake_case__		)

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

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

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

						# rename keys
						_SCREAMING_SNAKE_CASE     				=  rename_keys(snake_case__	,encoder_only=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(snake_case__	,snake_case__		)

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

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

						# set expected_slice based on model name
						# ADE20k checkpoints
						if model_name == "segformer.b0.512x512.ade.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
												        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
												        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
												    ]		)
						elif model_name == "segformer.b1.512x512.ade.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
												        [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
												        [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
												    ]		)
						elif model_name == "segformer.b2.512x512.ade.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
												        [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
												        [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
												    ]		)
						elif model_name == "segformer.b3.512x512.ade.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
												        [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
												        [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
												    ]		)
						elif model_name == "segformer.b4.512x512.ade.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
												        [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
												        [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
												    ]		)
						elif model_name == "segformer.b5.640x640.ade.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
												        [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
												        [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
												    ]		)
						# Cityscapes checkpoints
						elif model_name == "segformer.b0.1024x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
												        [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
												        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
												    ]		)
						elif model_name == "segformer.b0.512x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
												        [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
												        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
												    ]		)
						elif model_name == "segformer.b0.640x1280.city.160k":
												_SCREAMING_SNAKE_CASE     				=  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":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
												        [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
												        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
												    ]		)
						elif model_name == "segformer.b1.1024x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
												        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
												        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
												    ]		)
						elif model_name == "segformer.b2.1024x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
												        [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
												        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
												    ]		)
						elif model_name == "segformer.b3.1024x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
												        [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
												        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
												    ]		)
						elif model_name == "segformer.b4.1024x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
												        [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
												        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
												    ]		)
						elif model_name == "segformer.b5.1024x1024.city.160k":
												_SCREAMING_SNAKE_CASE     				=  torch.tensor(
												    [
												        [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
												        [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
												        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
												    ]		)
						else:
												_SCREAMING_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]	,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(snake_case__		).mkdir(exist_ok=snake_case__		)
						model.save_pretrained(snake_case__		)
						image_processor.save_pretrained(snake_case__		)


if __name__ == "__main__":
		UpperCamelCase			    =     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.'''
		)
		UpperCamelCase			    =     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_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase			    =     {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3FeatureExtractor''']
		UpperCamelCase			    =     ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		UpperCamelCase			    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

