


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__			:      Tuple								=logging.getLogger(__name__)






def 						a__			(				A__,      A__				):
			return (preds == labels).mean()







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
							_UpperCAmelCase      =						field(
							    default=_SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
							_UpperCAmelCase      =						field(
							    default=_SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
							_UpperCAmelCase      =						field(
							    default=_SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,       )







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()  )}  )
							_UpperCAmelCase      =						field(metadata={"""help""": """Should contain the data files for the task."""}  )
							_UpperCAmelCase      =						field(
							    default=1_2_8	,       metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }	,       )
							_UpperCAmelCase      =						field(
							    default=_SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )







def 						a__			(				):
			# 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_		:     int					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     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				)

			# Set seed
			set_seed(training_args.seed				)

			try:
						SCREAMING_SNAKE_CASE_		:     int					=					processors[data_args.task_name]()
						SCREAMING_SNAKE_CASE_		:     Tuple					=					processor.get_labels()
						SCREAMING_SNAKE_CASE_		:     Tuple					=					len(__lowerCamelCase				)
			except KeyError:
						raise ValueError('Task not found: %s' % (data_args.task_name)				)

			# 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_		:     List[str]					=					AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path,      num_labels=__lowerCamelCase,      finetuning_task=data_args.task_name,      cache_dir=model_args.cache_dir,      )
			SCREAMING_SNAKE_CASE_		:     List[str]					=					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,      )
			SCREAMING_SNAKE_CASE_		:     Dict					=					AutoModelForMultipleChoice.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_		:     Union[str, Any]					=					(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir,      tokenizer=__lowerCamelCase,      task=data_args.task_name,      max_seq_length=data_args.max_seq_length,      overwrite_cache=data_args.overwrite_cache,      mode=Split.train,      )
			    if training_args.do_train
			    else None
			)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir,      tokenizer=__lowerCamelCase,      task=data_args.task_name,      max_seq_length=data_args.max_seq_length,      overwrite_cache=data_args.overwrite_cache,      mode=Split.dev,      )
			    if training_args.do_eval
			    else None
			)

			def compute_metrics(A__				) -> Dict:
						SCREAMING_SNAKE_CASE_		:     Dict					=					np.argmax(p.predictions,      axis=1				)
						return {"acc": simple_accuracy(__lowerCamelCase,      p.label_ids				)}

			# 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_		:     Dict					=					Trainer(
			    model=__lowerCamelCase,      args=__lowerCamelCase,      train_dataset=__lowerCamelCase,      eval_dataset=__lowerCamelCase,      compute_metrics=__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				)

    # Evaluation
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'				)

						SCREAMING_SNAKE_CASE_		:     str					=					trainer.evaluate()

						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					os.path.join(training_args.output_dir,      'eval_results.txt'				)
						if trainer.is_world_master():
									with open(__lowerCamelCase,      'w'				) as writer:
												logger.info('***** Eval results *****'				)
												for key, value in result.items():
															logger.info('  %s = %s',      __lowerCamelCase,      __lowerCamelCase				)
															writer.write('%s = %s\n' % (key, value)				)

												results.update(__lowerCamelCase				)

			return results






def 						a__			(				A__				):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()







'''simple docstring'''



from manim import *




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					Rectangle(height=0.5       ,  width=0.5 )
												_lowerCAmelCase    		=					Rectangle(height=0.46       ,  width=0.46 ).set_stroke(width=0 )

												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					VGroup(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					Text("""CPU"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												cpu.move_to([-2.5, -0.5, 0] )
												self.add(_lowercase )

												_lowerCAmelCase    		=					[mem.copy() for i in range(1 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					Text("""GPU"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												gpu.align_to(_lowercase       ,  _lowercase )
												gpu.set_x(gpu.get_x() - 1 )

												self.add(_lowercase )

												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )

												_lowerCAmelCase    		=					Text("""Model"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												model.move_to([3, -1.0, 0] )

												self.play(
												    Create(_lowercase       ,  run_time=1 )       ,  Create(_lowercase       ,  run_time=1 )       ,  Create(_lowercase       ,  run_time=1 )       ,  )

												_lowerCAmelCase    		=					MarkupText(
												    F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'       ,  font_size=24       ,  )

												_lowerCAmelCase    		=					Square(side_length=2.2 )
												key.move_to([-5, 2, 0] )

												_lowerCAmelCase    		=					MarkupText(
												    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'       ,  font_size=18       ,  )

												key_text.move_to([-5, 2.4, 0] )

												step_a.move_to([2, 2, 0] )
												self.play(Write(_lowercase       ,  run_time=2.5 )       ,  Write(_lowercase )       ,  Write(_lowercase ) )

												self.add(_lowercase )

												_lowerCAmelCase    		=					[]
												_lowerCAmelCase    		=					[]
												_lowerCAmelCase    		=					[]
												for i, rect in enumerate(_lowercase ):
																		_lowerCAmelCase    		=					Rectangle(height=0.46       ,  width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase       ,  opacity=0.7 )
																		cpu_target.move_to(_lowercase )
																		cpu_target.generate_target()
																		_lowerCAmelCase    		=					0.46 / 4
																		_lowerCAmelCase    		=					0.46 / 3

																		if i == 0:
																								cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT )       ,  buff=0.02       ,  direction=_lowercase )
																								cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
																		elif i == 3:
																								cpu_target.target.next_to(cpu_targs[0].target       ,  direction=_lowercase       ,  buff=0.0 )
																		else:
																								cpu_target.target.next_to(cpu_targs[i - 1].target       ,  direction=_lowercase       ,  buff=0.0 )
																		cpu_targs.append(_lowercase )

																		first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
																		second_animations.append(MoveToTarget(_lowercase       ,  run_time=1.5 ) )

												self.play(*_lowercase )
												self.play(*_lowercase )

												self.wait()
