




"""simple docstring"""


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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Dict		,   snake_case__							:					str		,   snake_case__							:					Optional[int]		,   snake_case__							:					Optional[Any]		,   snake_case__							:					int = None		,   ):
  """simple docstring"""

  _snake_case							:		Dict								=  {}

  if train_file is not None:
    _snake_case							:		Optional[int]								=  [train_file]
  if eval_file is not None:
    _snake_case							:		List[str]								=  [eval_file]
  if test_file is not None:
    _snake_case							:		List[str]								=  [test_file]

  _snake_case							:		List[Any]								=  datasets.load_dataset("""csv"""		,   data_files=__SCREAMING_SNAKE_CASE    )
  _snake_case							:		Dict								=  list(ds[list(files.keys()    )[0]].features.keys()    )
  _snake_case							:		int								=  features_name.pop(__SCREAMING_SNAKE_CASE    )
  _snake_case							:		Dict								=  list(set(ds[list(files.keys()    )[0]][label_name]    )    )
  _snake_case							:		List[str]								=  {label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE    )}
  _snake_case							:		List[Any]								=  tokenizer.model_input_names
  _snake_case							:		Dict								=  {}

  if len(__SCREAMING_SNAKE_CASE    ) == 1:
    for k in files.keys():
      _snake_case							:		int								=  ds[k].map(
          lambda snake_case__    : tokenizer.batch_encode_plus(
              example[features_name[0]]		,   truncation=__SCREAMING_SNAKE_CASE		,   max_length=__SCREAMING_SNAKE_CASE		,   padding="""max_length"""    )		,   batched=__SCREAMING_SNAKE_CASE		,   )
  elif len(__SCREAMING_SNAKE_CASE    ) == 2:
    for k in files.keys():
      _snake_case							:		int								=  ds[k].map(
          lambda snake_case__    : tokenizer.batch_encode_plus(
              (example[features_name[0]], example[features_name[1]])		,   truncation=__SCREAMING_SNAKE_CASE		,   max_length=__SCREAMING_SNAKE_CASE		,   padding="""max_length"""		,   )		,   batched=__SCREAMING_SNAKE_CASE		,   )

  def gen_train():
    for ex in transformed_ds[datasets.Split.TRAIN]:
      _snake_case							:		Any								=  {k: v for k, v in ex.items() if k in input_names}
      _snake_case							:		int								=  labelaid[ex[label_name]]
      yield (d, label)

  def gen_val():
    for ex in transformed_ds[datasets.Split.VALIDATION]:
      _snake_case							:		Optional[Any]								=  {k: v for k, v in ex.items() if k in input_names}
      _snake_case							:		Tuple								=  labelaid[ex[label_name]]
      yield (d, label)

  def gen_test():
    for ex in transformed_ds[datasets.Split.TEST]:
      _snake_case							:		int								=  {k: v for k, v in ex.items() if k in input_names}
      _snake_case							:		str								=  labelaid[ex[label_name]]
      yield (d, label)

  _snake_case							:		Tuple								=  (
      tf.data.Dataset.from_generator(
          __SCREAMING_SNAKE_CASE		,   ({k: tf.intaa for k in input_names}, tf.intaa)		,   ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))		,   )
      if datasets.Split.TRAIN in transformed_ds
      else None
  )

  if train_ds is not None:
    _snake_case							:		Any								=  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

  _snake_case							:		int								=  (
      tf.data.Dataset.from_generator(
          __SCREAMING_SNAKE_CASE		,   ({k: tf.intaa for k in input_names}, tf.intaa)		,   ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))		,   )
      if datasets.Split.VALIDATION in transformed_ds
      else None
  )

  if val_ds is not None:
    _snake_case							:		Optional[int]								=  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

  _snake_case							:		str								=  (
      tf.data.Dataset.from_generator(
          __SCREAMING_SNAKE_CASE		,   ({k: tf.intaa for k in input_names}, tf.intaa)		,   ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))		,   )
      if datasets.Split.TEST in transformed_ds
      else None
  )

  if test_ds is not None:
    _snake_case							:		Optional[Any]								=  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

  return train_ds, val_ds, test_ds, labelaid


A_						=				logging.getLogger(__name__)






@dataclass
class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					field(metadata={"help": "Which column contains the label"} )
 lowercase__ 					=					field(default=__lowerCamelCase       ,			metadata={"help": "The path of the training file"} )
 lowercase__ 					=					field(default=__lowerCamelCase       ,			metadata={"help": "The path of the development file"} )
 lowercase__ 					=					field(default=__lowerCamelCase       ,			metadata={"help": "The path of the test file"} )
 lowercase__ 					=					field(
     default=1_28       ,			metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }       ,			)
 lowercase__ 					=					field(
     default=__lowerCamelCase       ,			metadata={"help": "Overwrite the cached training and evaluation sets"} )







@dataclass
class 					lowercase:







 '''simple docstring'''





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







def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
  _snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  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		,   )
  logger.info(
      F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, "
      F"16-bits training: {training_args.fpaa}"    )
  logger.info(F"Training/evaluation parameters {training_args}"    )

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

  _snake_case							:		Union[str, 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		,   )

  _snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Dict								=  get_tfds(
      train_file=data_args.train_file		,   eval_file=data_args.dev_file		,   test_file=data_args.test_file		,   tokenizer=__SCREAMING_SNAKE_CASE		,   label_column_id=data_args.label_column_id		,   max_seq_length=data_args.max_seq_length		,   )

  _snake_case							:		Optional[Any]								=  AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path		,   num_labels=len(__SCREAMING_SNAKE_CASE    )		,   labelaid=__SCREAMING_SNAKE_CASE		,   idalabel={id: label for label, id in labelaid.items()}		,   finetuning_task="""text-classification"""		,   cache_dir=model_args.cache_dir		,   )

  with training_args.strategy.scope():
    _snake_case							:		Optional[Any]								=  TFAutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path		,   from_pt=bool(""".bin""" in model_args.model_name_or_path    )		,   config=__SCREAMING_SNAKE_CASE		,   cache_dir=model_args.cache_dir		,   )

  def compute_metrics(snake_case__							:					Union[str, Any]    ) -> Dict:
    _snake_case							:		Tuple								=  np.argmax(p.predictions		,   axis=1    )

    return {"acc": (preds == p.label_ids).mean()}

  # Initialize our Trainer
  _snake_case							:		Any								=  TFTrainer(
      model=__SCREAMING_SNAKE_CASE		,   args=__SCREAMING_SNAKE_CASE		,   train_dataset=__SCREAMING_SNAKE_CASE		,   eval_dataset=__SCREAMING_SNAKE_CASE		,   compute_metrics=__SCREAMING_SNAKE_CASE		,   )

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir    )

  # Evaluation
  _snake_case							:		Tuple								=  {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""    )
    _snake_case							:		List[str]								=  trainer.evaluate()
    _snake_case							:		Union[str, Any]								=  os.path.join(training_args.output_dir		,   """eval_results.txt"""    )

    with open(__SCREAMING_SNAKE_CASE		,   """w"""    ) as writer:
      logger.info("""***** Eval results *****"""    )

      for key, value in result.items():
        logger.info(F"  {key} = {value}"    )
        writer.write(F"{key} = {value}\n"    )

      results.update(__SCREAMING_SNAKE_CASE    )

  return results


if __name__ == "__main__":
     main()




from manim import *







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Rectangle(height=0.5		,    width=0.5       )
		lowercase       =    Rectangle(height=0.25		,    width=0.25       )
		lowercase       =    Rectangle(height=0.46		,    width=0.46       ).set_stroke(width=0       )

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

		lowercase       =    [mem.copy() for i in range(4       )]
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    Text('GPU'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		gpu.move_to([-1, -1, 0]       )
		self.add(snake_case       )

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

		lowercase       =    Text('Model'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		model.move_to([3, -1.0, 0]       )
		self.add(snake_case       )

		lowercase       =    []
		lowercase       =    []
		lowercase       =    []

		for i, rect in enumerate(snake_case       ):
			rect.set_stroke(snake_case       )

			lowercase       =    Rectangle(height=0.46 / 4		,    width=0.46 / 3       ).set_stroke(width=0.0       ).set_fill(snake_case		,    opacity=0.7       )

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

		self.add(*snake_case		,    *snake_case		,    *snake_case       )

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

		lowercase       =    Text('Loaded Checkpoint'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		checkpoint.move_to([3, 0.5, 0]       )

		self.add(snake_case       )

		lowercase       =    []
		lowercase       =    []

		for i, rect in enumerate(snake_case       ):
			lowercase       =    fill.copy().set_fill(snake_case		,    opacity=0.7       )
			target.move_to(snake_case       )
			ckpt_arr.append(snake_case       )

			lowercase       =    target.copy()
			if i < 5:
				cpu_target.move_to(cpu_left_col_base[i + 1]       )
			else:
				cpu_target.move_to(cpu_right_col_base[i - 5]       )
			ckpt_cpu_arr.append(snake_case       )
		self.add(*snake_case		,    *snake_case       )

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

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

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

		self.add(snake_case		,    snake_case       )

		lowercase       =    MarkupText(
		    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,    font_size=18		,    )

		blue_text.next_to(snake_case		,    DOWN * 2.4		,    aligned_edge=key_text.get_left()       )
		self.add(snake_case       )

		lowercase       =    MarkupText(
		    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'''		,    font_size=24		,    )
		step_a.move_to([2, 2, 0]       )

		lowercase       =    [meta_mem.copy() for i in range(6       )]
		lowercase       =    [meta_mem.copy() for i in range(6       )]
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    Text('Disk'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		disk.move_to([-4.0, -1.25, 0]       )
		self.play(Write(snake_case		,    run_time=3       )		,    Write(snake_case		,    run_time=1       )		,    Create(snake_case		,    run_time=1       )       )

		lowercase       =    []
		for i, rect in enumerate(snake_case       ):
			lowercase       =    rect.copy()
			target.generate_target()
			target.target.move_to(disk_left_col_base[i]       ).scale(0.5       )
			animations.append(MoveToTarget(snake_case		,    run_time=1.5       )       )
		self.play(*snake_case       )

		self.play(FadeOut(snake_case       )       )

		lowercase       =    MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''		,    font_size=24       )
		step_a.move_to([2, 2, 0]       )

		self.play(Write(snake_case		,    run_time=3       )       )

		self.play(
		    FadeOut(snake_case		,    snake_case		,    *snake_case		,    *snake_case       )		,    )

		self.wait()

