
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_ta import TaTokenizer
else:
    lowerCamelCase_							=     None


lowerCamelCase_							=     logging.get_logger(__name__)

lowerCamelCase_							=     {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_							=     {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase_							=     {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}




class 	__A(				a__ ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			    =			PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			    =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			    =			["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__			    =			TaTokenizer

  SCREAMING_SNAKE_CASE__			    =			[]







  def __init__(self       ,			SCREAMING_SNAKE_CASE_=None       ,			SCREAMING_SNAKE_CASE_=None       ,			SCREAMING_SNAKE_CASE_="</s>"       ,			SCREAMING_SNAKE_CASE_="<unk>"       ,			SCREAMING_SNAKE_CASE_="<pad>"       ,			SCREAMING_SNAKE_CASE_=1_00       ,			SCREAMING_SNAKE_CASE_=None       ,			**SCREAMING_SNAKE_CASE_       ,			):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
            UpperCamelCase__				=				[F"<extra_id_{i}>" for i in range(__lowerCAmelCase						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            UpperCamelCase__				=				len(set(filter(lambda SCREAMING_SNAKE_CASE_						: bool("""extra_id_""" in str(__lowerCAmelCase						)						)       ,			__lowerCAmelCase						)						)						)
            if extra_tokens != extra_ids:
                 raise ValueError(
                     F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                     """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                     """ tokens"""						)

       super().__init__(
           __lowerCAmelCase       ,			tokenizer_file=__lowerCAmelCase       ,			eos_token=__lowerCAmelCase       ,			unk_token=__lowerCAmelCase       ,			pad_token=__lowerCAmelCase       ,			extra_ids=__lowerCAmelCase       ,			additional_special_tokens=__lowerCAmelCase       ,			**__lowerCAmelCase       ,			)

       UpperCamelCase__				=				vocab_file
       UpperCamelCase__				=				False if not self.vocab_file else True
       UpperCamelCase__				=				extra_ids







  @staticmethod
  def        UpperCAmelCase_					(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            UpperCamelCase__				=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
            elif init_max_model_length is None:
                 warnings.warn(
                     """This tokenizer was incorrectly instantiated with a model max length of"""
                     F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
                     """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                     """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                     F" {pretrained_model_name_or_path} automatically truncating your input to"
                     F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
                     F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
                     """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                     """ instantiate this tokenizer with `model_max_length` set to your preferred value."""       ,			__lowerCAmelCase       ,			)

       return max_model_length







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None						):
       if not self.can_save_slow_tokenizer:
            raise ValueError(
                """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                """tokenizer."""						)

       if not os.path.isdir(__lowerCAmelCase						):
            logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
            return
       UpperCamelCase__				=				os.path.join(
           __lowerCAmelCase       ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowerCAmelCase						):
            copyfile(self.vocab_file       ,			__lowerCAmelCase						)
            logger.info(F"Copy vocab file to {out_vocab_file}"						)

       return (out_vocab_file,)







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=				token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
       else:
            UpperCamelCase__				=				token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=				[self.eos_token_id]

       if token_ids_a is None:
            return len(token_ids_a + eos						) * [0]
       return len(token_ids_a + eos + token_ids_a + eos						) * [0]







  def        UpperCAmelCase_					(self						):
       return list(
           set(filter(lambda SCREAMING_SNAKE_CASE_						: bool(re.search(r"""<extra_id_\d+>"""       ,			__lowerCAmelCase						)						) is not None       ,			self.additional_special_tokens						)						)						)







  def        UpperCAmelCase_					(self						):
       return [self.convert_tokens_to_ids(__lowerCAmelCase						) for token in self.get_sentinel_tokens()]






'''simple docstring'''

import logging

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

logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return input_array.reshape((input_array.size, 1)     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    Optional[Any]  =					features[:, labels == i]
  lowercase__				:    Optional[Any]  =					data.mean(1     )
  # Centralize the data of class i
  lowercase__				:    Dict  =					data - column_reshape(UpperCAmelCase     )
  if i > 0:
   # If covariance_sum is not None
   covariance_sum += np.dot(UpperCAmelCase		,     centered_data.T     )
  else:
   # If covariance_sum is np.nan (i.e. first loop)
   lowercase__				:    List[str]  =					np.dot(UpperCAmelCase		,     centered_data.T     )

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

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):

 # Check if the features have been loaded
 if features.any():
  lowercase__				:    Optional[Any]  =					features.mean(1     )
  # Center the dataset
  lowercase__				:    List[str]  =					features - np.reshape(UpperCAmelCase		,     (data_mean.size, 1)     )
  lowercase__				:    Optional[Any]  =					np.dot(UpperCAmelCase		,     centered_data.T     ) / features.shape[1]
  lowercase__ ,							lowercase__				:    Tuple  =					np.linalg.eigh(UpperCAmelCase     )
  # Take all the columns in the reverse order (-1), and then takes only the first
  lowercase__				:    str  =					eigenvectors[:, ::-1][:, 0:dimensions]
  # Project the database on the new space
  lowercase__				:    Tuple  =					np.dot(filtered_eigenvectors.T		,     UpperCAmelCase     )
  logging.info('''Principal Component Analysis computed'''     )

  return projected_data
 else:
  logging.basicConfig(level=logging.ERROR		,     format='''%(message)s'''		,     force=UpperCAmelCase     )
  logging.error('''Dataset empty'''     )
  raise AssertionError
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 assert classes > dimensions

 # Check if features have been already loaded
 if features.any:
  lowercase__ ,							lowercase__				:    Any  =					eigh(
      covariance_between_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     covariance_within_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     )
  lowercase__				:    Optional[int]  =					eigenvectors[:, ::-1][:, :dimensions]
  lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					np.linalg.svd(UpperCAmelCase     )
  lowercase__				:    List[str]  =					svd_matrix[:, 0:dimensions]
  lowercase__				:    str  =					np.dot(filtered_svd_matrix.T		,     UpperCAmelCase     )
  logging.info('''Linear Discriminant Analysis computed'''     )

  return projected_data
 else:
  logging.basicConfig(level=logging.ERROR		,     format='''%(message)s'''		,     force=UpperCAmelCase     )
  logging.error('''Dataset empty'''     )
  raise AssertionError
def 			__UpperCamelCase       (   ):
 # Create dummy dataset with 2 classes and 3 features
 lowercase__				:    List[str]  =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
 lowercase__				:    Optional[Any]  =					np.array([0, 0, 0, 1, 1]     )
 lowercase__				:    str  =					2
 lowercase__				:    Dict  =					2

 # Assert that the function raises an AssertionError if dimensions > classes
 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    int  =					linear_discriminant_analysis(
      UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
  if isinstance(UpperCAmelCase		,     np.ndarray     ):
   raise AssertionError(
       '''Did not raise AssertionError for dimensions > classes'''     )
  assert error_info.type is AssertionError
def 			__UpperCamelCase       (   ):
 lowercase__				:    Optional[int]  =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
 lowercase__				:    int  =					2
 lowercase__				:    Any  =					np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    Dict  =					principal_component_analysis(UpperCAmelCase		,     UpperCAmelCase     )
  if not np.allclose(UpperCAmelCase		,     UpperCAmelCase     ):
   raise AssertionError
  assert error_info.type is AssertionError


if __name__ == "__main__":
  import doctest

  doctest.testmod()

