
'''simple docstring'''





from typing import Any

import numpy as np
def 	snake_case_							(				_a  :				np.ndarray      ):
      assert np.shape(_a      )[0] == np.shape(_a      )[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(_a			,       matrix.T      ) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      snake_case_							,						snake_case_    							=				np.linalg.eigh(_a      )

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0      )      )
def 	snake_case_							(				_a  :				int      ):
      snake_case_    							=				np.random.randn(_a			,       _a      )
      snake_case_    							=				np.dot(_a			,       random_matrix.T      )
      assert _is_matrix_spd(_a      )
      return spd_matrix
def 	snake_case_							(				_a  :				np.ndarray			,       _a  :				np.ndarray			,       _a  :				int = 1_000			,       _a  :				float = 1E-8			,       ):
      assert np.shape(_a      )[0] == np.shape(_a      )[1]
      assert np.shape(_a      )[0] == np.shape(_a      )[0]
      assert _is_matrix_spd(_a      )

      # Initialize solution guess, residual, search direction.
      snake_case_    							=				np.zeros((np.shape(_a      )[0], 1)      )
      snake_case_    							=				np.copy(_a      )
      snake_case_    							=				np.copy(_a      )

      # Set initial errors in solution guess and residual.
      snake_case_    							=				1E9
      snake_case_    							=				1E9
      snake_case_    							=				1E9

      # Set iteration counter to threshold number of iterations.
      snake_case_    							=				0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            snake_case_    							=				np.dot(_a			,       _a      )

            # The main algorithm.

            # Update search direction magnitude.
            snake_case_    							=				np.dot(ra.T			,       _a      ) / np.dot(pa.T			,       _a      )
            # Update solution guess.
            snake_case_    							=				xa + alpha * pa
            # Calculate new residual.
            snake_case_    							=				ra - alpha * w
            # Calculate new Krylov subspace scale.
            snake_case_    							=				np.dot(r.T			,       _a      ) / np.dot(ra.T			,       _a      )
            # Calculate new A conjuage search direction.
            snake_case_    							=				r + beta * pa

            # Calculate errors.
            snake_case_    							=				np.linalg.norm(r - ra      )
            snake_case_    							=				np.linalg.norm(x - xa      )
            snake_case_    							=				np.maximum(_a			,       _a      )

            # Update variables.
            snake_case_    							=				np.copy(_a      )
            snake_case_    							=				np.copy(_a      )
            snake_case_    							=				np.copy(_a      )

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x
def 	snake_case_							(				):
      snake_case_    							=				3
      snake_case_    							=				_create_spd_matrix(_a      )
      snake_case_    							=				np.random.randn(_a			,       1      )
      snake_case_    							=				np.dot(_a			,       _a      )

      # Numpy solution.
      snake_case_    							=				np.linalg.solve(_a			,       _a      )

      # Our implementation.
      snake_case_    							=				conjugate_gradient(_a			,       _a      )

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true      ) <= 1E-6
      assert np.linalg.norm(x_conjugate_gradient - x_true      ) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()







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

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


if is_sentencepiece_available():
 from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
 a_ 		=  None


a_ 		=  logging.get_logger(__name__)

a_ 		=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a_ 		=  {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

a_ 		=  {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       VOCAB_FILES_NAMES
      lowerCAmelCase__      :			List[Any]     =       PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__      :			Optional[Any]     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__      :			Optional[int]     =       ["input_ids", "attention_mask"]
      lowerCAmelCase__      :			Union[str, Any]     =       XLMRobertaTokenizer







      def __init__(  self       :			str			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			Any=None			,							snake_case       :			Union[str, Any]="<s>"			,							snake_case       :			Tuple="</s>"			,							snake_case       :			List[Any]="</s>"			,							snake_case       :			Any="<s>"			,							snake_case       :			str="<unk>"			,							snake_case       :			int="<pad>"			,							snake_case       :			Any="<mask>"			,							**snake_case       :			str			,							):
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCamelCase			=    AddedToken(snake_case			,							lstrip=snake_case			,							rstrip=snake_case					) if isinstance(snake_case			,							snake_case					) else mask_token

          super().__init__(
              snake_case			,							tokenizer_file=snake_case			,							bos_token=snake_case			,							eos_token=snake_case			,							sep_token=snake_case			,							cls_token=snake_case			,							unk_token=snake_case			,							pad_token=snake_case			,							mask_token=snake_case			,							**snake_case			,							)

          __UpperCamelCase			=    vocab_file
          __UpperCamelCase			=    False if not self.vocab_file else True







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = None					):

          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCamelCase			=    [self.cls_token_id]
          __UpperCamelCase			=    [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep







      def    snake_case						(  self       :			Any			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = None					):
          __UpperCamelCase			=    [self.sep_token_id]
          __UpperCamelCase			=    [self.cls_token_id]

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







      def    snake_case						(  self       :			Dict			,							snake_case       :			str			,							snake_case       :			Optional[str] = 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(snake_case					):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory."					)
              return
          __UpperCamelCase			=    os.path.join(
              snake_case			,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

          if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case					):
              copyfile(self.vocab_file			,							snake_case					)

          return (out_vocab_file,)






