



"""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 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase = None       ,     )       ->  Union[str, Any]:







  '''simple docstring'''


  lowercase_	    =       {}

  if train_file is not None:
    lowercase_	    =       [train_file]
  if eval_file is not None:
    lowercase_	    =       [eval_file]
  if test_file is not None:
    lowercase_	    =       [test_file]

  lowercase_	    =       datasets.load_dataset("""csv"""       ,     data_files=UpperCamelCase__							)
  lowercase_	    =       list(ds[list(files.keys()							)[0]].features.keys()							)
  lowercase_	    =       features_name.pop(UpperCamelCase__							)
  lowercase_	    =       list(set(ds[list(files.keys()							)[0]][label_name]							)							)
  lowercase_	    =       {label: i for i, label in enumerate(UpperCamelCase__							)}
  lowercase_	    =       tokenizer.model_input_names
  lowercase_	    =       {}

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

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

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

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

  lowercase_	    =       (
      tf.data.Dataset.from_generator(
          UpperCamelCase__       ,     ({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:
    lowercase_	    =       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

  lowercase_	    =       (
      tf.data.Dataset.from_generator(
          UpperCamelCase__       ,     ({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:
    lowercase_	    =       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

  lowercase_	    =       (
      tf.data.Dataset.from_generator(
          UpperCamelCase__       ,     ({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:
    lowercase_	    =       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

  return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase  :						List[Any]	=       logging.getLogger(__name__)


@dataclass
class       SCREAMING_SNAKE_CASE__       :
    lowercase__          =      field(metadata={"help": "Which column contains the label"}      )
    lowercase__          =      field(default=__UpperCAmelCase					,     metadata={"help": "The path of the training file"}      )
    lowercase__          =      field(default=__UpperCAmelCase					,     metadata={"help": "The path of the development file"}      )
    lowercase__          =      field(default=__UpperCAmelCase					,     metadata={"help": "The path of the test file"}      )
    lowercase__          =      field(
        default=128					,     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=__UpperCAmelCase					,     metadata={"help": "Overwrite the cached training and evaluation sets"}      )







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







def 	_SCREAMING_SNAKE_CASE  ()       ->  int:







  '''simple docstring'''


  lowercase_	    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
  lowercase_    ,			lowercase_    ,			lowercase_	    =       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.

  lowercase_	    =       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       ,     )

  lowercase_    ,			lowercase_    ,			lowercase_    ,			lowercase_	    =       get_tfds(
      train_file=data_args.train_file       ,     eval_file=data_args.dev_file       ,     test_file=data_args.test_file       ,     tokenizer=UpperCamelCase__       ,     label_column_id=data_args.label_column_id       ,     max_seq_length=data_args.max_seq_length       ,     )

  lowercase_	    =       AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,     num_labels=len(UpperCamelCase__							)       ,     labelaid=UpperCamelCase__       ,     idalabel={id: label for label, id in labelaid.items()}       ,     finetuning_task="""text-classification"""       ,     cache_dir=model_args.cache_dir       ,     )

  with training_args.strategy.scope():
    lowercase_	    =       TFAutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path       ,     from_pt=bool(""".bin""" in model_args.model_name_or_path							)       ,     config=UpperCamelCase__       ,     cache_dir=model_args.cache_dir       ,     )

  def compute_metrics(__lowerCAmelCase							) -> Dict:
    lowercase_	    =       np.argmax(p.predictions       ,     axis=1							)

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

  # Initialize our Trainer
  lowercase_	    =       TFTrainer(
      model=UpperCamelCase__       ,     args=UpperCamelCase__       ,     train_dataset=UpperCamelCase__       ,     eval_dataset=UpperCamelCase__       ,     compute_metrics=UpperCamelCase__       ,     )

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

  # Evaluation
  lowercase_	    =       {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""							)
    lowercase_	    =       trainer.evaluate()
    lowercase_	    =       os.path.join(training_args.output_dir       ,     """eval_results.txt"""							)

    with open(UpperCamelCase__       ,     """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(UpperCamelCase__							)

  return results


if __name__ == "__main__":
       main()







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42


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



							'''simple docstring'''
							assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
							assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
							assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.width, self.height]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.arange(self.height * self.width							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='trunc'							),
							    ]			,						axis=1			,						)
							return coords


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

							__lowerCamelCase		       =    self.get_image_coords()
							__lowerCamelCase		       =    torch.broadcast_to(coords.unsqueeze(0							)			,						[batch_size * inner_batch_size, *coords.shape]							)
							__lowerCamelCase		       =    self.get_camera_rays(lowerCamelCase__							)

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase       ,						__lowerCamelCase		       =    coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

							__lowerCamelCase		       =    (flat.float() / (res - 1)) * 2 - 1
							__lowerCamelCase		       =    fracs * torch.tan(fov / 2							)

							__lowerCamelCase		       =    fracs.view(lowerCamelCase__			,						-1			,						2							)
							__lowerCamelCase		       =    (
							    self.z.view(lowerCamelCase__			,						1			,						3							)
							    + self.x.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, 1:]
							)
							__lowerCamelCase		       =    directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3							)			,						[batch_size, directions.shape[1], 3]							),
							        directions,
							    ]			,						dim=2			,						)
							return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



							'''simple docstring'''
							assert width * self.height == height * self.width, "The aspect ratio should not change."
							return DifferentiableProjectiveCamera(
							    origin=self.origin			,						x=self.x			,						y=self.y			,						z=self.z			,						width=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				for theta in np.linspace(0 ,     2 * np.pi ,     num=20      ):
								__lowerCamelCase		       =    np.array([np.sin(UpperCamelCase__      ), np.cos(UpperCamelCase__      ), -0.5]      )
								z /= np.sqrt(np.sum(z**2      )      )
								__lowerCamelCase		       =    -z * 4
								__lowerCamelCase		       =    np.array([np.cos(UpperCamelCase__      ), -np.sin(UpperCamelCase__      ), 0.0]      )
								__lowerCamelCase		       =    np.cross(UpperCamelCase__ ,     UpperCamelCase__      )
								origins.append(UpperCamelCase__      )
								xs.append(UpperCamelCase__      )
								ys.append(UpperCamelCase__      )
								zs.append(UpperCamelCase__      )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     x=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     y=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     z=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     width=UpperCamelCase__ ,     height=UpperCamelCase__ ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(UpperCamelCase__      )) ,     )


