






"""simple docstring"""
import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCAmelCase__							= 1_000


class _lowerCAmelCase	:







						def __init__(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=1.2      ,							lowerCAmelCase_="g"      ,							lowerCAmelCase_=0.5      ,							lowerCAmelCase_="-"      ,							lowerCAmelCase_="test_out.jpg"      ,							lowerCAmelCase_=True      ,							lowerCAmelCase_=False      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=0.7      ,							)		->       List[Any]:
										if isinstance(lowerCAmelCase_      ,							torch.Tensor       ):
														_SCREAMING_SNAKE_CASE      :		int							=						img.numpy().astype('np.uint8'       )
										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						img_tensorize(lowerCAmelCase_       )
										assert isinstance(lowerCAmelCase_      ,							np.ndarray       )

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						img.shape[1], img.shape[0]
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						mplfigure.Figure(frameon=lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						fig.get_dpi()
										_SCREAMING_SNAKE_CASE      :		Any							=						(width * scale + 1e-2) / dpi
										_SCREAMING_SNAKE_CASE      :		Dict							=						(height * scale + 1e-2) / dpi
										fig.set_size_inches(lowerCAmelCase_      ,							lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						fig.add_axes([0.0, 0.0, 1.0, 1.0]       )
										ax.axis('off'       )
										ax.set_xlim(0.0      ,							lowerCAmelCase_       )
										ax.set_ylim(lowerCAmelCase_       )

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						saveas
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						rgb
										_SCREAMING_SNAKE_CASE      :		Dict							=						pynb
										_SCREAMING_SNAKE_CASE      :		Tuple							=						img
										_SCREAMING_SNAKE_CASE      :		str							=						edgecolor
										_SCREAMING_SNAKE_CASE      :		Tuple							=						0.5
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						linestyle
										_SCREAMING_SNAKE_CASE      :		str							=						int(np.sqrt(min(lowerCAmelCase_      ,							lowerCAmelCase_       )       ) * scale // 3       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						width
										_SCREAMING_SNAKE_CASE      :		Tuple							=						height
										_SCREAMING_SNAKE_CASE      :		str							=						scale
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						fig
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ax
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						pad
										_SCREAMING_SNAKE_CASE      :		Dict							=						idaobj
										_SCREAMING_SNAKE_CASE      :		int							=						idaattr
										_SCREAMING_SNAKE_CASE      :		str							=						FigureCanvasAgg(lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=None       )		->       Optional[int]:
										if color is None:
														_SCREAMING_SNAKE_CASE      :		List[str]							=						self.edgecolor
										(_SCREAMING_SNAKE_CASE)      :		Optional[Any]							=						box
										_SCREAMING_SNAKE_CASE      :		Tuple							=						xa - xa
										_SCREAMING_SNAKE_CASE      :		Any							=						ya - ya
										self.ax.add_patch(
										    mpl.patches.Rectangle(
										        (xa, ya)      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							fill=lowerCAmelCase_      ,							edgecolor=lowerCAmelCase_      ,							linewidth=self.font_size // 3      ,							alpha=self.alpha      ,							linestyle=self.linestyle      ,							)       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None       )		->       Optional[Any]:
										if len(boxes.shape       ) > 2:
														_SCREAMING_SNAKE_CASE      :		int							=						boxes[0]
										if len(obj_ids.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		Tuple							=						obj_ids[0]
										if len(obj_scores.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						obj_scores[0]
										if len(attr_ids.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		str							=						attr_ids[0]
										if len(attr_scores.shape       ) > 1:
														_SCREAMING_SNAKE_CASE      :		int							=						attr_scores[0]
										if isinstance(lowerCAmelCase_      ,							torch.Tensor       ):
														_SCREAMING_SNAKE_CASE      :		Any							=						boxes.numpy()
										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		int							=						np.array(lowerCAmelCase_       )
										assert isinstance(lowerCAmelCase_      ,							np.ndarray       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						np.prod(boxes[:, 2:] - boxes[:, :2]      ,							axis=1       )
										_SCREAMING_SNAKE_CASE      :		List[str]							=						np.argsort(-areas       ).tolist()
										_SCREAMING_SNAKE_CASE      :		str							=						boxes[sorted_idxs] if boxes is not None else None
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						obj_ids[sorted_idxs] if obj_ids is not None else None
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						obj_scores[sorted_idxs] if obj_scores is not None else None
										_SCREAMING_SNAKE_CASE      :		int							=						attr_ids[sorted_idxs] if attr_ids is not None else None
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						attr_scores[sorted_idxs] if attr_scores is not None else None

										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[self._random_color(maximum=1       ) for _ in range(len(lowerCAmelCase_       )       )]
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
														_SCREAMING_SNAKE_CASE      :		str							=						self._create_text_labels_attr(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )
														for i in range(len(lowerCAmelCase_       )       ):
																		_SCREAMING_SNAKE_CASE      :		Dict							=						assigned_colors[i]
																		self.add_box(boxes[i]      ,							lowerCAmelCase_       )
																		self.draw_labels(labels[i]      ,							boxes[i]      ,							lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       int:
										_SCREAMING_SNAKE_CASE      :		Dict							=						box
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						(xa, ya)
										_SCREAMING_SNAKE_CASE      :		Dict							=						(ya - ya) * (xa - xa)
										_SCREAMING_SNAKE_CASE      :		List[str]							=						_SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 4_0 * self.scale:
														if ya >= self.height - 5:
																		_SCREAMING_SNAKE_CASE      :		List[str]							=						(xa, ya)
														else:
																		_SCREAMING_SNAKE_CASE      :		str							=						(xa, ya)

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						(ya - ya) / np.sqrt(self.height * self.width       )
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						self._change_color_brightness(lowerCAmelCase_      ,							brightness_factor=0.7       )
										_SCREAMING_SNAKE_CASE      :		Any							=						np.clip((height_ratio - 0.02) / 0.08 + 1      ,							1.2      ,							2       )
										font_size *= 0.75 * self.font_size

										self.draw_text(
										    text=lowerCAmelCase_      ,							position=lowerCAmelCase_      ,							color=lowerCAmelCase_      ,							)







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_="g"      ,							lowerCAmelCase_="left"      ,							)		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						0
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						self.font_size
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						np.maximum(list(mplc.to_rgb(lowerCAmelCase_       )       )      ,							0.2       )
										_SCREAMING_SNAKE_CASE      :		Dict							=						max(0.8      ,							np.max(lowerCAmelCase_       )       )
										_SCREAMING_SNAKE_CASE      :		int							=						{
										    'facecolor': 'black',
										    'alpha': self.alpha,
										    'pad': self.pad,
										    'edgecolor': 'none',
										}
										_SCREAMING_SNAKE_CASE      :		str							=						position
										self.ax.text(
										    lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							size=font_size * self.scale      ,							family='sans-serif'      ,							bbox=lowerCAmelCase_      ,							verticalalignment='top'      ,							horizontalalignment=lowerCAmelCase_      ,							color=lowerCAmelCase_      ,							zorder=1_0      ,							rotation=lowerCAmelCase_      ,							)







						def  A			(						self      ,							lowerCAmelCase_=None       )		->       List[Any]:
										if saveas is None:
														_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						self.saveas
										if saveas.lower().endswith('.jpg'       ) or saveas.lower().endswith('.png'       ):
														cva.imwrite(
														    lowerCAmelCase_      ,							self._get_buffer()[:, :, ::-1]      ,							)
										else:
														self.fig.savefig(lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       List[str]:
										_SCREAMING_SNAKE_CASE      :		Tuple							=						[self.idaobj[i] for i in classes]
										_SCREAMING_SNAKE_CASE      :		Tuple							=						[self.idaattr[i] for i in attr_classes]
										_SCREAMING_SNAKE_CASE      :		str							=						[
										    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
										    for label, score, attr, attr_score in zip(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )
										]
										return labels







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						[self.idaobj[i] for i in classes]
										if scores is not None:
														if labels is None:
																		_SCREAMING_SNAKE_CASE      :		List[Any]							=						['{:.0f}%'.format(s * 1_0_0       ) for s in scores]
														else:
																		_SCREAMING_SNAKE_CASE      :		Any							=						['{} {:.0f}%'.format(lowerCAmelCase_      ,							s * 1_0_0       ) for li, s in zip(lowerCAmelCase_      ,							lowerCAmelCase_       )]
										return labels







						def  A			(						self      ,							lowerCAmelCase_=2_5_5       )		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		int							=						np.random.randint(0      ,							len(_COLORS       )       )
										_SCREAMING_SNAKE_CASE      :		Any							=						_COLORS[idx] * maximum
										if not self.rgb:
														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						ret[::-1]
										return ret







						def  A			(						self       )		->       Any:
										if not self.pynb:
														_SCREAMING_SNAKE_CASE      :		Dict							=						self.canvas.print_to_buffer()
														if (width, height) != (self.width, self.height):
																		_SCREAMING_SNAKE_CASE      :		Dict							=						cva.resize(self.img      ,							(width, height)       )
														else:
																		_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.img
										else:
														_SCREAMING_SNAKE_CASE      :		Tuple							=						io.BytesIO()  # works for cairo backend
														self.canvas.print_rgba(lowerCAmelCase_       )
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.width, self.height
														_SCREAMING_SNAKE_CASE      :		Any							=						buf.getvalue()
														_SCREAMING_SNAKE_CASE      :		Dict							=						self.img

										_SCREAMING_SNAKE_CASE      :		Any							=						np.frombuffer(lowerCAmelCase_      ,							dtype='uint8'       )
										_SCREAMING_SNAKE_CASE      :		Any							=						buffer.reshape(lowerCAmelCase_      ,							lowerCAmelCase_      ,							4       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						np.split(lowerCAmelCase_      ,							[3]      ,							axis=2       )

										try:
														import numexpr as ne  # fuse them with numexpr

														_SCREAMING_SNAKE_CASE      :		List[str]							=						ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'       )
										except ImportError:
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						alpha.astype('float32'       ) / 255.0
														_SCREAMING_SNAKE_CASE      :		int							=						img * (1 - alpha) + rgb * alpha

										return visualized_image.astype('uint8'       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Optional[int]:
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										_SCREAMING_SNAKE_CASE      :		Any							=						mplc.to_rgb(lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		Dict							=						colorsys.rgb_to_hls(*mplc.to_rgb(lowerCAmelCase_       )       )
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						polygon_color[1] + (brightness_factor * polygon_color[1])
										_SCREAMING_SNAKE_CASE      :		str							=						0.0 if modified_lightness < 0.0 else modified_lightness
										_SCREAMING_SNAKE_CASE      :		Dict							=						1.0 if modified_lightness > 1.0 else modified_lightness
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						colorsys.hls_to_rgb(polygon_color[0]      ,							lowerCAmelCase_      ,							polygon_color[2]       )
										return modified_color







lowerCAmelCase__							= (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)









"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCAmelCase__							= logging.getLogger(__name__)


@dataclass
class _lowerCAmelCase	:
						SCREAMING_SNAKE_CASE_:				str   				=							field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
						SCREAMING_SNAKE_CASE_:				bool   				=							field(default=__UpperCAmelCase      ,     metadata={'help': 'Set this flag to use fast tokenization.'}					)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )







def 							lowercase__		(				):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

				if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir					)
				    and os.listdir(training_args.output_dir					)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ' --overwrite_output_dir to overcome.'					)

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',	datefmt='%m/%d/%Y %H:%M:%S',	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,	)
				logger.warning(
				    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s',	training_args.local_rank,	training_args.device,	training_args.n_gpu,	bool(training_args.local_rank != -1					),	training_args.fpaa,	)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank					):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('Training/evaluation parameters %s',	lowerCamelCase					)

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	use_fast=lowerCamelCase,	)
				_SCREAMING_SNAKE_CASE      :		Dict							=						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path,	from_tf=bool('.ckpt' in model_args.model_name_or_path					),	config=lowerCamelCase,	cache_dir=model_args.cache_dir,	)

				# Get datasets
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						hasattr(model.config,	'lang2id'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						(
				    SquadDataset(
				        lowerCamelCase,	tokenizer=lowerCamelCase,	is_language_sensitive=lowerCamelCase,	cache_dir=model_args.cache_dir					)
				    if training_args.do_train
				    else None
				)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						(
				    SquadDataset(
				        lowerCamelCase,	tokenizer=lowerCamelCase,	mode='dev',	is_language_sensitive=lowerCamelCase,	cache_dir=model_args.cache_dir,	)
				    if training_args.do_eval
				    else None
				)

				# Data collator
				_SCREAMING_SNAKE_CASE      :		int							=						DataCollatorWithPadding(lowerCamelCase,	pad_to_multiple_of=8					) if training_args.fpaa else None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						Trainer(
				    model=lowerCamelCase,	args=lowerCamelCase,	train_dataset=lowerCamelCase,	eval_dataset=lowerCamelCase,	data_collator=lowerCamelCase,	)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir					)





def 							lowercase__		(				lowerCamelCase					):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
			main()

