
import numpy as np


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1e-12 ,		UpperCamelCase__ = 1_0_0 ,		):
						assert np.shape(UpperCamelCase__		)[0] == np.shape(UpperCamelCase__		)[1]
						# Ensure proper dimensionality.
						assert np.shape(UpperCamelCase__		)[0] == np.shape(UpperCamelCase__		)[0]
						# Ensure inputs are either both complex or both real
						assert np.iscomplexobj(UpperCamelCase__		) == np.iscomplexobj(UpperCamelCase__		)
						UpperCamelCase__    :   Tuple					=				np.iscomplexobj(UpperCamelCase__		)
						if is_complex:
												# Ensure complex input_matrix is Hermitian
												assert np.array_equal(UpperCamelCase__ ,		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__    :   int					=				False
						UpperCamelCase__    :   int					=				0
						UpperCamelCase__    :   Tuple					=				0
						UpperCamelCase__    :   Dict					=				1e12

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

												# Check convergence.
												UpperCamelCase__    :   Any					=				np.abs(lambda_ - lambda_previous		) / lambda_
												iterations += 1

												if error <= error_tol or iterations >= max_iterations:
																		UpperCamelCase__    :   str					=				True

												UpperCamelCase__    :   str					=				lambda_

						if is_complex:
												UpperCamelCase__    :   List[Any]					=				np.real(lambda_		)

						return lambda_, vector


def 							SCREAMING_SNAKE_CASE_      (			):
						UpperCamelCase__    :   List[Any]					=				np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]		)
						UpperCamelCase__    :   int					=				np.array([4_1, 4, 2_0]		)
						UpperCamelCase__    :   Optional[int]					=				real_input_matrix.astype(np.complexaaa		)
						UpperCamelCase__    :   int					=				np.triu(1j * complex_input_matrix ,		1		)
						complex_input_matrix += imag_matrix
						complex_input_matrix += -1 * imag_matrix.T
						UpperCamelCase__    :   List[str]					=				np.array([4_1, 4, 2_0]		).astype(np.complexaaa		)

						for problem_type in ["real", "complex"]:
												if problem_type == "real":
																		UpperCamelCase__    :   int					=				real_input_matrix
																		UpperCamelCase__    :   str					=				real_vector
												elif problem_type == "complex":
																		UpperCamelCase__    :   List[str]					=				complex_input_matrix
																		UpperCamelCase__    :   List[str]					=				complex_vector

												# Our implementation.
												UpperCamelCase__    :   Optional[int]					=				power_iteration(UpperCamelCase__ ,		UpperCamelCase__		)

												# Numpy implementation.

												# Get eigenvalues and eigenvectors using built-in numpy
												# eigh (eigh used for symmetric or hermetian matrices).
												UpperCamelCase__    :   Union[str, Any]					=				np.linalg.eigh(UpperCamelCase__		)
												# Last eigenvalue is the maximum one.
												UpperCamelCase__    :   Tuple					=				eigen_values[-1]
												# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
												UpperCamelCase__    :   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(UpperCamelCase__		) - np.abs(UpperCamelCase__		)		) <= 1e-6


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				test_power_iteration()




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCamelCase      =logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(UpperCamelCase__		):
                  return ext

      raise Exception(
          f'''Unable to determine file format from file extension {path}. '''
          f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				PipelineDataFormat.from_str(
          format=UpperCamelCase__ ,		output_path=args.output ,		input_path=args.input ,		column=args.column if args.column else nlp.default_input_names ,		overwrite=args.overwrite ,		)
      return RunCommand(UpperCamelCase__ ,		UpperCamelCase__		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				parser.add_parser('''run'''						,						help='''Run a pipeline through the CLI'''  )
           run_parser.add_argument('''--task'''						,						choices=get_supported_tasks()						,						help='''Task to run'''  )
           run_parser.add_argument('''--input'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file to use for inference'''  )
           run_parser.add_argument('''--output'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file that will be used post to write results.'''  )
           run_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model to instantiate.'''  )
           run_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model\'s config to instantiate.'''  )
           run_parser.add_argument(
               '''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the tokenizer to use. (default: same as the model name)'''  )
           run_parser.add_argument(
               '''--column'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''						,						)
           run_parser.add_argument(
               '''--format'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''infer'''						,						choices=PipelineDataFormat.SUPPORTED_FORMATS						,						help='''Input format to read from'''						,						)
           run_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						default=-1						,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''						,						)
           run_parser.add_argument('''--overwrite'''						,						action='''store_true'''						,						help='''Allow overwriting the output file.'''  )
           run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				nlp(**__SCREAMING_SNAKE_CASE  ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE  )
                 if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       outputs.append(__SCREAMING_SNAKE_CASE  )
                 else:
                       outputs += output

        # Saving data
           if self._nlp.binary_output:
                 UpperCamelCase__    :   Union[str, Any]					=				self._reader.save_binary(__SCREAMING_SNAKE_CASE  )
                 logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}'''  )
           else:
                 self._reader.save(__SCREAMING_SNAKE_CASE  )


