
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : List[Any]				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : List[Any]=1024    ):
       snake_case				,					snake_case       :   Tuple							=      [], []

       snake_case       :   str							=      list(zip(__lowerCamelCase				,					__lowerCamelCase    )    )
       snake_case				,					snake_case       :   List[str]							=      sorted_examples[0]

       def is_too_big(__lowerCamelCase     : int    ):
              return tok(__lowerCamelCase				,					return_tensors="pt"    ).input_ids.shape[1] > max_tokens

       for src, tgt in tqdm(sorted_examples[1:]    ):
              snake_case       :   Optional[Any]							=      new_src + " " + src
              snake_case       :   Dict							=      new_tgt + " " + tgt
              if is_too_big(__lowerCamelCase    ) or is_too_big(__lowerCamelCase    ):  # cant fit, finalize example
                     finished_src.append(__lowerCamelCase    )
                     finished_tgt.append(__lowerCamelCase    )
                     snake_case				,					snake_case       :   Optional[Any]							=      src, tgt
              else:  # can fit, keep adding
                     snake_case				,					snake_case       :   Optional[Any]							=      cand_src, cand_tgt

    # cleanup
       if new_src:
              assert new_tgt
              finished_src.append(__lowerCamelCase    )
              finished_tgt.append(__lowerCamelCase    )
       return finished_src, finished_tgt


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Path				,					__lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Union[str, Any]    ):
       snake_case       :   Union[str, Any]							=      Path(__lowerCamelCase    )
       save_path.mkdir(exist_ok=__lowerCamelCase    )
       for split in ["train"]:
              snake_case				,					snake_case       :   str							=      data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
              snake_case       :   List[str]							=      [x.rstrip() for x in Path(__lowerCamelCase    ).open().readlines()]
              snake_case       :   Tuple							=      [x.rstrip() for x in Path(__lowerCamelCase    ).open().readlines()]
              snake_case				,					snake_case       :   Optional[int]							=      pack_examples(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
              print(f"""packed {split} split from {len(__lowerCamelCase    )} examples -> {len(__lowerCamelCase    )}."""    )
              Path(save_path / f"""{split}.source"""    ).open("w"    ).write("\n".join(__lowerCamelCase    )    )
              Path(save_path / f"""{split}.target"""    ).open("w"    ).write("\n".join(__lowerCamelCase    )    )
       for split in ["val", "test"]:
              snake_case				,					snake_case       :   Optional[int]							=      data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
              shutil.copyfile(__lowerCamelCase				,					save_path / f"""{split}.source"""    )
              shutil.copyfile(__lowerCamelCase				,					save_path / f"""{split}.target"""    )


def 			UpperCamelCase					(    ):
       snake_case       :   Tuple							=      argparse.ArgumentParser()
       parser.add_argument("--tok_name"				,					type=__lowerCamelCase				,					help="like facebook/bart-large-cnn,t5-base, etc."    )
       parser.add_argument("--max_seq_len"				,					type=__lowerCamelCase				,					default=128    )
       parser.add_argument("--data_dir"				,					type=__lowerCamelCase    )
       parser.add_argument("--save_path"				,					type=__lowerCamelCase    )
       snake_case       :   Dict							=      parser.parse_args()
       snake_case       :   Tuple							=      AutoTokenizer.from_pretrained(args.tok_name    )
       return pack_data_dir(__lowerCamelCase				,					Path(args.data_dir    )				,					args.max_seq_len				,					args.save_path    )


if __name__ == "__main__":
   packer_cli()







import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray    ):
       return input_array.reshape((input_array.size, 1)    )


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):
       snake_case       :   Any							=      np.nan
       for i in range(__lowerCamelCase    ):
              snake_case       :   List[str]							=      features[:, labels == i]
              snake_case       :   Dict							=      data.mean(1    )
              # Centralize the data of class i
              snake_case       :   Optional[Any]							=      data - column_reshape(__lowerCamelCase    )
              if i > 0:
                     # If covariance_sum is not None
                     covariance_sum += np.dot(__lowerCamelCase				,					centered_data.T    )
              else:
                     # If covariance_sum is np.nan (i.e. first loop)
                     snake_case       :   Optional[Any]							=      np.dot(__lowerCamelCase				,					centered_data.T    )

       return covariance_sum / features.shape[1]


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):
       snake_case       :   Optional[Any]							=      features.mean(1    )
       snake_case       :   Tuple							=      np.nan
       for i in range(__lowerCamelCase    ):
              snake_case       :   Tuple							=      features[:, labels == i]
              snake_case       :   Tuple							=      data.shape[1]
              snake_case       :   List[str]							=      data.mean(1    )
              if i > 0:
                     # If covariance_sum is not None
                     covariance_sum += device_data * np.dot(
                         column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )				,					(column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )).T				,					)
              else:
                     # If covariance_sum is np.nan (i.e. first loop)
                     snake_case       :   Optional[int]							=      device_data * np.dot(
                         column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )				,					(column_reshape(__lowerCamelCase    ) - column_reshape(__lowerCamelCase    )).T				,					)

       return covariance_sum / features.shape[1]


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int    ):

       # Check if the features have been loaded
       if features.any():
              snake_case       :   Tuple							=      features.mean(1    )
              # Center the dataset
              snake_case       :   List[str]							=      features - np.reshape(__lowerCamelCase				,					(data_mean.size, 1)    )
              snake_case       :   Optional[Any]							=      np.dot(__lowerCamelCase				,					centered_data.T    ) / features.shape[1]
              snake_case				,					snake_case       :   Dict							=      np.linalg.eigh(__lowerCamelCase    )
              # Take all the columns in the reverse order (-1), and then takes only the first
              snake_case       :   Optional[Any]							=      eigenvectors[:, ::-1][:, 0:dimensions]
              # Project the database on the new space
              snake_case       :   Union[str, Any]							=      np.dot(filtered_eigenvectors.T				,					__lowerCamelCase    )
              logging.info("Principal Component Analysis computed"    )

              return projected_data
       else:
              logging.basicConfig(level=logging.ERROR				,					format="%(message)s"				,					force=__lowerCamelCase    )
              logging.error("Dataset empty"    )
              raise AssertionError


def 			UpperCamelCase					(    __lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : np.ndarray				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int    ):
       assert classes > dimensions

       # Check if features have been already loaded
       if features.any:
              snake_case				,					snake_case       :   str							=      eigh(
                  covariance_between_classes(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )				,					covariance_within_classes(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )				,					)
              snake_case       :   str							=      eigenvectors[:, ::-1][:, :dimensions]
              snake_case				,					snake_case				,					snake_case       :   int							=      np.linalg.svd(__lowerCamelCase    )
              snake_case       :   List[Any]							=      svd_matrix[:, 0:dimensions]
              snake_case       :   Optional[Any]							=      np.dot(filtered_svd_matrix.T				,					__lowerCamelCase    )
              logging.info("Linear Discriminant Analysis computed"    )

              return projected_data
       else:
              logging.basicConfig(level=logging.ERROR				,					format="%(message)s"				,					force=__lowerCamelCase    )
              logging.error("Dataset empty"    )
              raise AssertionError


def 			UpperCamelCase					(    ):
       # Create dummy dataset with 2 classes and 3 features
       snake_case       :   str							=      np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]    )
       snake_case       :   Union[str, Any]							=      np.array([0, 0, 0, 1, 1]    )
       snake_case       :   List[Any]							=      2
       snake_case       :   Any							=      2

       # Assert that the function raises an AssertionError if dimensions > classes
       with pytest.raises(__lowerCamelCase    ) as error_info:
              snake_case       :   str							=      linear_discriminant_analysis(
                  __lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
              if isinstance(__lowerCamelCase				,					np.ndarray    ):
                     raise AssertionError(
                         "Did not raise AssertionError for dimensions > classes"    )
              assert error_info.type is AssertionError


def 			UpperCamelCase					(    ):
       snake_case       :   List[str]							=      np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]    )
       snake_case       :   List[str]							=      2
       snake_case       :   int							=      np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]    )

       with pytest.raises(__lowerCamelCase    ) as error_info:
              snake_case       :   Union[str, Any]							=      principal_component_analysis(__lowerCamelCase				,					__lowerCamelCase    )
              if not np.allclose(__lowerCamelCase				,					__lowerCamelCase    ):
                     raise AssertionError
              assert error_info.type is AssertionError


if __name__ == "__main__":
   import doctest

   doctest.testmod()





