

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   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     Dict=False      )     ->	Any:

					"""simple docstring"""

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

					return new_state_dict



def   __UpperCAmelCase					(      __a :     str						,__a :     Union[str, Any]      )     ->	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)
															_a      :       Any   =						state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
															_a      :       List[str]   =						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      :       Tuple   =						kv_weight[
															    : config.hidden_sizes[i], :
															]
															_a      :       Tuple   =						kv_bias[: config.hidden_sizes[i]]
															_a      :       str   =						kv_weight[
															    config.hidden_sizes[i] :, :
															]
															_a      :       Dict   =						kv_bias[
															    config.hidden_sizes[i] :
															]



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

					"""simple docstring"""

					_a      :       int   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a      :       Dict   =						Image.open(requests.get(__a						,stream=__a      ).raw      )

					return image



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Dict						,__a :     str						,__a :     Optional[int]      )     ->	Optional[Any]:

					"""simple docstring"""

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

					# set attributes based on model_name
					_a      :       Union[str, Any]   =						'''huggingface/label-files'''
					if "segformer" in model_name:
										_a      :       List[str]   =						model_name[len('''segformer.'''      ) : len('''segformer.'''      ) + 2]
										if "ade" in model_name:
															_a      :       Optional[Any]   =						150
															_a      :       List[Any]   =						'''ade20k-id2label.json'''
															_a      :       Any   =						(1, 150, 128, 128)
										elif "city" in model_name:
															_a      :       Tuple   =						19
															_a      :       List[str]   =						'''cityscapes-id2label.json'''
															_a      :       int   =						(1, 19, 128, 128)
										else:
															raise ValueError(F"""Model {model_name} not supported"""      )
					elif "mit" in model_name:
										_a      :       List[Any]   =						True
										_a      :       Optional[Any]   =						model_name[4:6]
										_a      :       Dict   =						1_000
										_a      :       Union[str, Any]   =						'''imagenet-1k-id2label.json'''
										_a      :       Union[str, Any]   =						(1, 1_000)
					else:
										raise ValueError(F"""Model {model_name} not supported"""      )

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

					# load image processor (only resize + normalize)
					_a      :       str   =						SegformerImageProcessor(
					    image_scale=(512, 512)						,keep_ratio=__a						,align=__a						,do_random_crop=__a      )

					# prepare image
					_a      :       List[Any]   =						prepare_img()
					_a      :       Union[str, Any]   =						image_processor(images=__a						,return_tensors='''pt'''      ).pixel_values

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

					# load original state dict
					if encoder_only:
										_a      :       List[Any]   =						torch.load(__a						,map_location=torch.device('''cpu'''      )      )
					else:
										_a      :       Any   =						torch.load(__a						,map_location=torch.device('''cpu'''      )      )['''state_dict''']

					# rename keys
					_a      :       int   =						rename_keys(__a						,encoder_only=__a      )
					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(__a						,__a      )

					# create HuggingFace model and load state dict
					if encoder_only:
										_a      :       Dict   =						False
										_a      :       Optional[int]   =						SegformerForImageClassification(__a      )
					else:
										_a      :       List[str]   =						SegformerForSemanticSegmentation(__a      )
					model.load_state_dict(__a      )
					model.eval()

					# forward pass
					_a      :       int   =						model(__a      )
					_a      :       List[Any]   =						outputs.logits

					# set expected_slice based on model name
					# ADE20k checkpoints
					if model_name == "segformer.b0.512x512.ade.160k":
										_a      :       Optional[int]   =						torch.tensor(
										    [
										        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
										        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
										        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
										    ]      )
					elif model_name == "segformer.b1.512x512.ade.160k":
										_a      :       Optional[int]   =						torch.tensor(
										    [
										        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
										        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
										        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
										    ]      )
					elif model_name == "segformer.b2.512x512.ade.160k":
										_a      :       Union[str, Any]   =						torch.tensor(
										    [
										        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
										        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
										        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
										    ]      )
					elif model_name == "segformer.b3.512x512.ade.160k":
										_a      :       List[Any]   =						torch.tensor(
										    [
										        [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
										        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
										        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
										    ]      )
					elif model_name == "segformer.b4.512x512.ade.160k":
										_a      :       List[str]   =						torch.tensor(
										    [
										        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
										        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
										        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
										    ]      )
					elif model_name == "segformer.b5.640x640.ade.160k":
										_a      :       List[str]   =						torch.tensor(
										    [
										        [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
										        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
										        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
										    ]      )
					# Cityscapes checkpoints
					elif model_name == "segformer.b0.1024x1024.city.160k":
										_a      :       Tuple   =						torch.tensor(
										    [
										        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
										        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
										        [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
										    ]      )
					elif model_name == "segformer.b0.512x1024.city.160k":
										_a      :       Tuple   =						torch.tensor(
										    [
										        [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
										        [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
										        [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
										    ]      )
					elif model_name == "segformer.b0.640x1280.city.160k":
										_a      :       Optional[int]   =						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":
										_a      :       Optional[Any]   =						torch.tensor(
										    [
										        [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
										        [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
										        [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
										    ]      )
					elif model_name == "segformer.b1.1024x1024.city.160k":
										_a      :       str   =						torch.tensor(
										    [
										        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
										        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
										        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
										    ]      )
					elif model_name == "segformer.b2.1024x1024.city.160k":
										_a      :       int   =						torch.tensor(
										    [
										        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
										        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
										        [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
										    ]      )
					elif model_name == "segformer.b3.1024x1024.city.160k":
										_a      :       Dict   =						torch.tensor(
										    [
										        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
										        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
										        [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
										    ]      )
					elif model_name == "segformer.b4.1024x1024.city.160k":
										_a      :       Tuple   =						torch.tensor(
										    [
										        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
										        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
										        [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
										    ]      )
					elif model_name == "segformer.b5.1024x1024.city.160k":
										_a      :       int   =						torch.tensor(
										    [
										        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
										        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
										        [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
										    ]      )
					else:
										_a      :       List[Any]   =						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]						,__a						,atol=1E-2      )

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


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 re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		str:
			A_									= int(SCREAMING_SNAKE_CASE_     )
			A_		, A_		, A_									= t // 3600, (t // 60) % 60, t % 60
			return F'{h}:{m:02d}:{s:02d}' if h != 0 else F'{m:02d}:{s:02d}'





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=300     )		->		Dict:
			# docstyle-ignore
			return F'\n    <div>\n      {prefix}\n      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n      {label}\n    </div>\n    '





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= '<table border="1" class="dataframe">\n'
			html_code += """  <thead>\n <tr style="text-align: left;">\n"""
			for i in items[0]:
						html_code += F'      <th>{i}</th>\n'
			html_code += "    </tr>\n  </thead>\n  <tbody>\n"
			for line in items[1:]:
						html_code += "    <tr>\n"
						for elt in line:
									A_									= F'{elt:.6f}' if isinstance(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ) else str(SCREAMING_SNAKE_CASE_     )
									html_code += F'      <td>{elt}</td>\n'
						html_code += "    </tr>\n"
			html_code += "  </tbody>\n</table><p>"
			return html_code
class   __UpperCAmelCase				:



					'''simple docstring'''





					_UpperCamelCase							=		5
					_UpperCamelCase							=		0.2



					def __init__(	self     :						Tuple      ,       _lowercase     :						int      ,       _lowercase     :						Optional[str] = None      ,       _lowercase     :						bool = True      ,       _lowercase     :						Optional["NotebookTrainingTracker"] = None      ,       _lowercase     :						int = 300      ,       )    ->							Dict:
								A_									= total
								A_									= '' if prefix is None else prefix
								A_									= leave
								A_									= parent
								A_									= width
								A_									= None
								A_									= None
								A_									= None



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						int      ,       _lowercase     :						bool = False      ,       _lowercase     :						str = None)    ->							Dict:
								A_									= value
								if comment is not None:
											A_									= comment
								if self.last_value is None:
											A_									= A_									= time.time()
											A_									= A_									= value
											A_									= A_									= None
											A_									= self.warmup
											A_									= 1
											self.update_bar(_lowercase)
								elif value <= self.last_value and not force_update:
											return
								elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for      ,       self.total):
											if self.first_calls > 0:
														self.first_calls -= 1
											A_									= time.time()
											A_									= current_time - self.start_time
											# We could have value = self.start_value if the update is called twixe with the same start value.
											if value > self.start_value:
														A_									= self.elapsed_time / (value - self.start_value)
											else:
														A_									= None
											if value >= self.total:
														A_									= self.total
														A_									= None
														if not self.leave:
																	self.close()
											elif self.average_time_per_item is not None:
														A_									= self.average_time_per_item * (self.total - value)
											self.update_bar(_lowercase)
											A_									= value
											A_									= current_time
											if self.average_time_per_item is None:
														A_									= 1
											else:
														A_									= max(int(self.update_every / self.average_time_per_item)      ,       1)



					def     __snake_case						(	self     :						str      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Optional[int]=None)    ->							str:
								A_									= ' ' * (len(str(self.total)) - len(str(_lowercase))) + str(_lowercase)
								if self.elapsed_time is None:
											A_									= F'[{spaced_value}/{self.total} : < :'
								elif self.predicted_remaining is None:
											A_									= F'[{spaced_value}/{self.total} {format_time(self.elapsed_time)}'
								else:
											A_									= (
											    F'[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <'
											    F' {format_time(self.predicted_remaining)}'
											)
											self.label += F', {1/self.average_time_per_item:.2f} it/s'
								self.label += "]" if self.comment is None or len(self.comment) == 0 else F', {self.comment}]'
								self.display()



					def     __snake_case						(	self     :						Optional[int])    ->							Dict:
								A_									= html_progress_bar(self.value      ,       self.total      ,       self.prefix      ,       self.label      ,       self.width)
								if self.parent is not None:
											# If this is a child bar, the parent will take care of the display.
											self.parent.display()
											return
								if self.output is None:
											A_									= disp.display(disp.HTML(self.html_code)      ,       display_id=_lowercase)
								else:
											self.output.update(disp.HTML(self.html_code))



					def     __snake_case						(	self     :						List[Any])    ->							Optional[Any]:
								if self.parent is None and self.output is not None:
											self.output.update(disp.HTML(''))





class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str=None)    ->							str:
								super().__init__(_lowercase)
								A_									= None if column_names is None else [column_names]
								A_									= None



					def     __snake_case						(	self     :						Dict)    ->							List[Any]:
								A_									= html_progress_bar(self.value      ,       self.total      ,       self.prefix      ,       self.label      ,       self.width)
								if self.inner_table is not None:
											self.html_code += text_to_html_table(self.inner_table)
								if self.child_bar is not None:
											self.html_code += self.child_bar.html_code
								if self.output is None:
											A_									= disp.display(disp.HTML(self.html_code)      ,       display_id=_lowercase)
								else:
											self.output.update(disp.HTML(self.html_code))



					def     __snake_case						(	self     :						str      ,       _lowercase     :						int)    ->							Optional[int]:
								if self.inner_table is None:
											A_									= [list(values.keys()), list(values.values())]
								else:
											A_									= self.inner_table[0]
											if len(self.inner_table) == 1:
														# We give a chance to update the column names at the first iteration
														for key in values.keys():
																	if key not in columns:
																				columns.append(_lowercase)
														A_									= columns
											self.inner_table.append([values[c] for c in columns])



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str=None      ,       _lowercase     :						Optional[int]=300)    ->							Dict:
								A_									= NotebookProgressBar(_lowercase      ,       prefix=_lowercase      ,       parent=self      ,       width=_lowercase)
								return self.child_bar



					def     __snake_case						(	self     :						Any)    ->							int:
								A_									= None
								self.display()





class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						Dict)    ->							Tuple:
								A_									= None
								A_									= None
								A_									= False



					def     __snake_case						(	self     :						int      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       **_lowercase     :						List[Any])    ->							List[Any]:
								A_									= 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
								A_									= 0
								A_									= 0
								A_									= [self.first_column] + ['Training Loss']
								if args.evaluation_strategy != IntervalStrategy.NO:
											column_names.append('Validation Loss')
								A_									= NotebookTrainingTracker(state.max_steps      ,       _lowercase)



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[str]      ,       **_lowercase     :						Union[str, Any])    ->							str:
								A_									= int(state.epoch) if int(state.epoch) == state.epoch else F'{state.epoch:.2f}'
								self.training_tracker.update(
								    state.global_step + 1      ,       comment=F'Epoch {epoch}/{state.num_train_epochs}'      ,       force_update=self._force_next_update      ,       )
								A_									= False



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						Any      ,       _lowercase     :						int      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						List[Any]=None      ,       **_lowercase     :						Any)    ->							Union[str, Any]:
								if not has_length(_lowercase):
											return
								if self.prediction_bar is None:
											if self.training_tracker is not None:
														A_									= self.training_tracker.add_child(len(_lowercase))
											else:
														A_									= NotebookProgressBar(len(_lowercase))
											self.prediction_bar.update(1)
								else:
											self.prediction_bar.update(self.prediction_bar.value + 1)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						List[str]      ,       _lowercase     :						Any      ,       _lowercase     :						Union[str, Any]      ,       **_lowercase     :						Optional[int])    ->							Optional[Any]:
								if self.prediction_bar is not None:
											self.prediction_bar.close()
								A_									= None



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						Dict      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       _lowercase     :						Optional[Any]=None      ,       **_lowercase     :						Dict)    ->							Optional[int]:
								# Only for when there is no evaluation
								if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
											A_									= {'Training Loss': logs['loss']}
											# First column is necessarily Step sine we're not in epoch eval strategy
											A_									= state.global_step
											self.training_tracker.write_line(_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Any      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						str      ,       _lowercase     :						Optional[Any]=None      ,       **_lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								if self.training_tracker is not None:
											A_									= {'Training Loss': 'No log', 'Validation Loss': 'No log'}
											for log in reversed(state.log_history):
														if "loss" in log:
																	A_									= log['loss']
																	break

											if self.first_column == "Epoch":
														A_									= int(state.epoch)
											else:
														A_									= state.global_step
											A_									= 'eval'
											for k in metrics:
														if k.endswith('_loss'):
																	A_									= re.sub(r'\_loss$'      ,       ''      ,       _lowercase)
											A_									= metrics.pop('total_flos'      ,       _lowercase)
											A_									= metrics.pop('epoch'      ,       _lowercase)
											A_									= metrics.pop(F'{metric_key_prefix}_runtime'      ,       _lowercase)
											A_									= metrics.pop(F'{metric_key_prefix}_samples_per_second'      ,       _lowercase)
											A_									= metrics.pop(F'{metric_key_prefix}_steps_per_second'      ,       _lowercase)
											A_									= metrics.pop(F'{metric_key_prefix}_jit_compilation_time'      ,       _lowercase)
											for k, v in metrics.items():
														if k == F'{metric_key_prefix}_loss':
																	A_									= v
														else:
																	A_									= k.split('_')
																	A_									= ' '.join([part.capitalize() for part in splits[1:]])
																	A_									= v
											self.training_tracker.write_line(_lowercase)
											self.training_tracker.remove_child()
											A_									= None
											# Evaluation takes a long time so we should force the next update.
											A_									= True



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Dict      ,       _lowercase     :						str      ,       _lowercase     :						List[str]      ,       **_lowercase     :						str)    ->							Optional[int]:
								self.training_tracker.update(
								    state.global_step      ,       comment=F'Epoch {int(state.epoch)}/{state.num_train_epochs}'      ,       force_update=_lowercase)
								A_									= None




