
"""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()
a_			   =					logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]=False			):



					"""simple docstring"""





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

					return new_state_dict







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Tuple			):



					"""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)
															snake_case_					:		Tuple 					=					state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'			)
															snake_case_					:		Union[str, 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
															snake_case_					:		Tuple 					=					kv_weight[
															    : config.hidden_sizes[i], :
															]
															snake_case_					:		List[str] 					=					kv_bias[: config.hidden_sizes[i]]
															snake_case_					:		Optional[Any] 					=					kv_weight[
															    config.hidden_sizes[i] :, :
															]
															snake_case_					:		Tuple 					=					kv_bias[
															    config.hidden_sizes[i] :
															]







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
					snake_case_					:		Tuple 					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,    stream=SCREAMING_SNAKE_CASE__			).raw			)

					return image







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Tuple			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					SegformerConfig()
					snake_case_					:		Optional[int] 					=					False

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

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

					# load image processor (only resize + normalize)
					snake_case_					:		Optional[int] 					=					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
					snake_case_					:		str 					=					prepare_img()
					snake_case_					:		Optional[int] 					=					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:
										snake_case_					:		List[Any] 					=					torch.load(SCREAMING_SNAKE_CASE__				,    map_location=torch.device("""cpu"""			)			)
					else:
										snake_case_					:		int 					=					torch.load(SCREAMING_SNAKE_CASE__				,    map_location=torch.device("""cpu"""			)			)['state_dict']

					# rename keys
					snake_case_					:		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:
										snake_case_					:		List[Any] 					=					False
										snake_case_					:		Optional[Any] 					=					SegformerForImageClassification(SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		List[str] 					=					SegformerForSemanticSegmentation(SCREAMING_SNAKE_CASE__			)
					model.load_state_dict(SCREAMING_SNAKE_CASE__			)
					model.eval()

					# forward pass
					snake_case_					:		str 					=					model(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Optional[Any] 					=					outputs.logits

					# set expected_slice based on model name
					# ADE20k checkpoints
					if model_name == "segformer.b0.512x512.ade.160k":
										snake_case_					:		int 					=					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":
										snake_case_					:		Tuple 					=					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":
										snake_case_					:		Optional[Any] 					=					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":
										snake_case_					:		int 					=					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":
										snake_case_					:		Union[str, Any] 					=					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":
										snake_case_					:		int 					=					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":
										snake_case_					:		List[str] 					=					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":
										snake_case_					:		Optional[Any] 					=					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":
										snake_case_					:		Dict 					=					torch.tensor(
										    [
										        [
										            [-1.1_372E01, -1.2_787E01, -1.3_477E01],
										            [-1.2_536E01, -1.4_194E01, -1.4_409E01],
										            [-1.3_217E01, -1.4_888E01, -1.5_327E01],
										        ],
										        [
										            [-1.4_791E01, -1.7_122E01, -1.8_277E01],
										            [-1.7_163E01, -1.9_192E01, -1.9_533E01],
										            [-1.7_897E01, -1.9_991E01, -2.0_315E01],
										        ],
										        [
										            [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
										            [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
										            [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
										        ],
										    ]			)
					elif model_name == "segformer.b0.768x768.city.160k":
										snake_case_					:		Optional[Any] 					=					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":
										snake_case_					:		List[Any] 					=					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":
										snake_case_					:		List[str] 					=					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":
										snake_case_					:		Optional[int] 					=					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":
										snake_case_					:		Tuple 					=					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":
										snake_case_					:		Tuple 					=					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:
										snake_case_					:		Dict 					=					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__":
			a_			   =					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.'''
			)
			a_			   =					parser.parse_args()
			convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_			   =					logging.getLogger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=-1       ):
											# in NER datasets, the last column is usually reserved for NER label
											snake_case_					:		Union[str, Any] 					=					label_idx







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					mode.value
											snake_case_					:		List[Any] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		Any 					=					[]
											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																snake_case_					:		str 					=					[]
																snake_case_					:		List[Any] 					=					[]
																for line in f:
																					if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																										if words:
																															examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																															guid_index += 1
																															snake_case_					:		Optional[Any] 					=					[]
																															snake_case_					:		int 					=					[]
																					else:
																										snake_case_					:		Optional[Any] 					=					line.split(""" """       )
																										words.append(splits[0]       )
																										if len(lowercase__       ) > 1:
																															labels.append(splits[self.label_idx].replace("""\n"""   ,		""""""       )       )
																										else:
																															# Examples could have no label for mode = "test"
																															labels.append("""O"""       )
																if words:
																					examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					0
											for line in test_input_reader:
																if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																					writer.write(lowercase__       )
																					if not preds_list[example_id]:
																										example_id += 1
																elif preds_list[example_id]:
																					snake_case_					:		Optional[int] 					=					line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
																					writer.write(lowercase__       )
																else:
																					logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Dict 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		List[Any] 					=					["""O"""] + labels
																return labels
											else:
																return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self       ):
											# in CONLL2003 dataset chunk column is second-to-last
											super().__init__(label_idx=-2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Any 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		Tuple 					=					["""O"""] + labels
																return labels
											else:
																return [
																    "O",
																    "B-ADVP",
																    "B-INTJ",
																    "B-LST",
																    "B-PRT",
																    "B-NP",
																    "B-SBAR",
																    "B-VP",
																    "B-ADJP",
																    "B-CONJP",
																    "B-PP",
																    "I-ADVP",
																    "I-INTJ",
																    "I-LST",
																    "I-PRT",
																    "I-NP",
																    "I-SBAR",
																    "I-VP",
																    "I-ADJP",
																    "I-CONJP",
																    "I-PP",
																]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[Any] 					=					mode.value
											snake_case_					:		Optional[int] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		str 					=					[]

											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																for sentence in parse_incr(lowercase__       ):
																					snake_case_					:		Tuple 					=					[]
																					snake_case_					:		Any 					=					[]
																					for token in sentence:
																										words.append(token["""form"""]       )
																										labels.append(token["""upos"""]       )
																					assert len(lowercase__       ) == len(lowercase__       )
																					if words:
																										examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																										guid_index += 1
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					0
											for sentence in parse_incr(lowercase__       ):
																snake_case_					:		int 					=					preds_list[example_id]
																snake_case_					:		Dict 					=					""""""
																for token in sentence:
																					out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
																out += "\n"
																writer.write(lowercase__       )
																example_id += 1







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					return f.read().splitlines()
											else:
																return [
																    "ADJ",
																    "ADP",
																    "ADV",
																    "AUX",
																    "CCONJ",
																    "DET",
																    "INTJ",
																    "NOUN",
																    "NUM",
																    "PART",
																    "PRON",
																    "PROPN",
																    "PUNCT",
																    "SCONJ",
																    "SYM",
																    "VERB",
																    "X",
																]




