
import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

UpperCAmelCase       =					logging.get_logger(__name__)


UpperCAmelCase       =					{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
		raise ValueError(F'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()				)}.'''				)

	if tokenizer_name is None:
		lowercase	    =       TOKENIZER_CLASSES
	else:
		lowercase	    =       {tokenizer_name: getattr(__SCREAMING_SNAKE_CASE					,				tokenizer_name + 'Fast'				)}

	logger.info(F'''Loading tokenizer classes: {tokenizer_names}'''				)

	for tokenizer_name in tokenizer_names:
		lowercase	    =       TOKENIZER_CLASSES[tokenizer_name]

		lowercase	    =       True
		if checkpoint_name is None:
			lowercase	    =       list(tokenizer_class.max_model_input_sizes.keys()				)
		else:
			lowercase	    =       [checkpoint_name]

		logger.info(F'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'''				)

		for checkpoint in checkpoint_names:
			logger.info(F'''Loading {tokenizer_class.__class__.__name__} {checkpoint}'''				)

			# Load tokenizer
			lowercase	    =       tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,				force_download=__SCREAMING_SNAKE_CASE				)

			# Save fast tokenizer
			logger.info(F'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'''				)

			# For organization names we create sub-directories
			if "/" in checkpoint:
				lowercase    ,   lowercase	    =       checkpoint.split('/'				)
				lowercase	    =       os.path.join(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
			elif add_prefix:
				lowercase	    =       checkpoint
				lowercase	    =       dump_path
			else:
				lowercase	    =       None
				lowercase	    =       dump_path

			logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''				)

			if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()				)[0]:
				lowercase	    =       list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
				lowercase	    =       file_path.split(__SCREAMING_SNAKE_CASE				)[-1][0]
				if next_char == "/":
					lowercase	    =       os.path.join(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
					lowercase	    =       None

				logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''				)

			lowercase	    =       tokenizer.save_pretrained(
			    __SCREAMING_SNAKE_CASE					,				legacy_format=__SCREAMING_SNAKE_CASE					,				filename_prefix=__SCREAMING_SNAKE_CASE				)
			logger.info(F'''=> File names {file_names}'''				)

			for file_name in file_names:
				if not file_name.endswith('tokenizer.json'				):
					os.remove(__SCREAMING_SNAKE_CASE				)
					logger.info(F'''=> removing {file_name}'''				)


if __name__ == "__main__":
	UpperCAmelCase       =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
	)
	parser.add_argument(
	    '''--tokenizer_name''',
	    default=None,
	    type=str,
	    help=(
	        F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
	        '''download and convert all the checkpoints from AWS.'''
	    ),
	)
	parser.add_argument(
	    '''--checkpoint_name''',
	    default=None,
	    type=str,
	    help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
	)
	parser.add_argument(
	    '''--force_download''',
	    action='''store_true''',
	    help='''Re-download checkpoints.''',
	)
	UpperCAmelCase       =					parser.parse_args()

	convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)


'''simple docstring'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''simple docstring'''
   assert np.shape(_A     )[0] == np.shape(_A     )[1]
   # Ensure proper dimensionality.
   assert np.shape(_A     )[0] == np.shape(_A     )[0]
   # Ensure inputs are either both complex or both real
   assert np.iscomplexobj(_A     ) == np.iscomplexobj(_A     )
   UpperCAmelCase__				:					int 					=	np.iscomplexobj(_A     )
   if is_complex:
      # Ensure complex input_matrix is Hermitian
      assert np.array_equal(_A	,      input_matrix.conj().T     )

   # Set convergence to False. Will define convergence when we exceed max_iterations
   # or when we have small changes from one iteration to next.

   UpperCAmelCase__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

   while not convergence:
      # Multiple matrix by the vector.
      UpperCAmelCase__				:					str 					=	np.dot(_A	,      _A     )
      # Normalize the resulting output vector.
      UpperCAmelCase__				:					int 					=	w / np.linalg.norm(_A     )
      # Find rayleigh quotient
      # (faster than usual b/c we know vector is normalized already)
      UpperCAmelCase__				:					int 					=	vector.conj().T if is_complex else vector.T
      UpperCAmelCase__				:					Any 					=	np.dot(_A	,      np.dot(_A	,      _A     )     )

      # Check convergence.
      UpperCAmelCase__				:					List[Any] 					=	np.abs(lambda_ - lambda_previous     ) / lambda_
      iterations += 1

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

   if is_complex:
      UpperCAmelCase__				:					Tuple 					=	np.real(lambda_     )

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

      # Get eigenvalues and eigenvectors using built-in numpy
      # eigh (eigh used for symmetric or hermetian matrices).
      UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	np.linalg.eigh(_A     )
      # Last eigenvalue is the maximum one.
      UpperCAmelCase__				:					Dict 					=	eigen_values[-1]
      # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
      UpperCAmelCase__				:					Union[str, Any] 					=	eigen_vectors[:, -1]

      # Check our implementation and numpy gives close answers.
      assert np.abs(eigen_value - eigen_value_max     ) <= 1e-6
      # Take absolute values element wise of each eigenvector.
      # as they are only unique to a minus sign.
      assert np.linalg.norm(np.abs(_A     ) - np.abs(_A     )     ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()




