
"""simple docstring"""

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_A						=			logging.get_logger(__name__)






class 	_lowercase       (    enum.Enum		):
	lowercase_				=     0
	lowercase_				=     1






@add_end_docstrings(__UpperCAmelCase		)
class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'generated'




	def __init__(			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Union[str, Any]:
			super().__init__(*UpperCAmelCase_		,			**UpperCAmelCase_ )

			self.check_model_type(
			    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			    if self.framework == 'tf'
			    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							Optional[Any]:
			lowerCamelCase					:      Tuple          =   {}
			if truncation is not None:
					lowerCamelCase					:      Tuple          =   truncation

			lowerCamelCase					:      Union[str, Any]          =   generate_kwargs

			lowerCamelCase					:      List[str]          =   {}
			if return_tensors is not None and return_type is None:
					lowerCamelCase					:      int          =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
			if return_type is not None:
					lowerCamelCase					:      Optional[int]          =   return_type

			if clean_up_tokenization_spaces is not None:
					lowerCamelCase					:      Dict          =   clean_up_tokenization_spaces

			if stop_sequence is not None:
					lowerCamelCase					:      int          =   self.tokenizer.encode(UpperCAmelCase_		,			add_special_tokens=UpperCAmelCase_ )
					if len(UpperCAmelCase_ ) > 1:
							warnings.warn(
							    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
							    ' the stop sequence will be used as the stop sequence string in the interim.' )
					lowerCamelCase					:      str          =   stop_sequence_ids[0]

			return preprocess_params, forward_params, postprocess_params




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[str]:
			return True




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[Any]:
			lowerCamelCase					:      Optional[int]          =   self.model.config.prefix if self.model.config.prefix is not None else ''
			if isinstance(args[0]		,			UpperCAmelCase_ ):
					if self.tokenizer.pad_token_id is None:
							raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input' )
					lowerCamelCase					:      Any          =   ([prefix + arg for arg in args[0]],)
					lowerCamelCase					:      Tuple          =   True

			elif isinstance(args[0]		,			UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[int]          =   (prefix + args[0],)
					lowerCamelCase					:      Tuple          =   False
			else:
					raise ValueError(
					    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
			lowerCamelCase					:      Optional[Any]          =   self.tokenizer(*UpperCAmelCase_		,			padding=UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			return_tensors=self.framework )
			# This is produced by tokenizers but is an invalid generate kwargs
			if "token_type_ids" in inputs:
					del inputs["token_type_ids"]
			return inputs




	def __call__(			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   super().__call__(*UpperCAmelCase_		,			**UpperCAmelCase_ )
			if (
			    isinstance(args[0]		,			UpperCAmelCase_ )
			    and all(isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) for el in args[0] )
			    and all(len(UpperCAmelCase_ ) == 1 for res in result )
			):
					return [res[0] for res in result]
			return result




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=TruncationStrategy.DO_NOT_TRUNCATE		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      List[Any]          =   self._parse_and_tokenize(UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			**UpperCAmelCase_ )
			return inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Union[str, Any]:
			if self.framework == "pt":
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   model_inputs['input_ids'].shape
			elif self.framework == "tf":
					lowerCamelCase     ,   lowerCamelCase					:      Any          =   tf.shape(model_inputs['input_ids'] ).numpy()

			lowerCamelCase					:      List[Any]          =   generate_kwargs.get('min_length'		,			self.model.config.min_length )
			lowerCamelCase					:      Dict          =   generate_kwargs.get('max_length'		,			self.model.config.max_length )
			self.check_inputs(UpperCAmelCase_		,			generate_kwargs['min_length']		,			generate_kwargs['max_length'] )
			lowerCamelCase					:      Any          =   self.model.generate(**UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      str          =   output_ids.shape[0]
			if self.framework == "pt":
					lowerCamelCase					:      Tuple          =   output_ids.reshape(UpperCAmelCase_		,			out_b // in_b		,			*output_ids.shape[1:] )
			elif self.framework == "tf":
					lowerCamelCase					:      Tuple          =   tf.reshape(UpperCAmelCase_		,			(in_b, out_b // in_b, *output_ids.shape[1:]) )
			return {"output_ids": output_ids}




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=ReturnType.TEXT		,			UpperCAmelCase_=False )		->							Tuple:
			lowerCamelCase					:      Union[str, Any]          =   []
			for output_ids in model_outputs["output_ids"][0]:
					if return_type == ReturnType.TENSORS:
							lowerCamelCase					:      Any          =   {F"""{self.return_name}_token_ids""": output_ids}
					elif return_type == ReturnType.TEXT:
							lowerCamelCase					:      str          =   {
							    F"""{self.return_name}_text""": self.tokenizer.decode(
							        UpperCAmelCase_		,			skip_special_tokens=UpperCAmelCase_		,			clean_up_tokenization_spaces=UpperCAmelCase_		,			)
							}
					records.append(UpperCAmelCase_ )
			return records






@add_end_docstrings(__UpperCAmelCase		)
class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'summary'




	def __call__(			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Any:
			return super().__call__(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							bool:
			if max_length < min_length:
					logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )

			if input_length < max_length:
					logger.warning(
					    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
					    'a summarization task, where outputs shorter than the input are typically wanted, you might '
					    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})""" )






@add_end_docstrings(__UpperCAmelCase		)
class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'translation'




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			if input_length > 0.9 * max_length:
					logger.warning(
					    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
					    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)' )
			return True




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			UpperCAmelCase_=TruncationStrategy.DO_NOT_TRUNCATE		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None )		->							List[Any]:
			if getattr(self.tokenizer		,			'_build_translation_inputs'		,			UpperCAmelCase_ ):
					return self.tokenizer._build_translation_inputs(
					    *UpperCAmelCase_		,			return_tensors=self.framework		,			truncation=UpperCAmelCase_		,			src_lang=UpperCAmelCase_		,			tgt_lang=UpperCAmelCase_ )
			else:
					return super()._parse_and_tokenize(*UpperCAmelCase_		,			truncation=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Dict          =   super()._sanitize_parameters(**UpperCAmelCase_ )
			if src_lang is not None:
					lowerCamelCase					:      Dict          =   src_lang
			if tgt_lang is not None:
					lowerCamelCase					:      List[str]          =   tgt_lang
			if src_lang is None and tgt_lang is None:
					# Backward compatibility, direct arguments use is preferred.
					lowerCamelCase					:      Tuple          =   kwargs.get('task'		,			self.task )
					lowerCamelCase					:      str          =   task.split('_' )
					if task and len(UpperCAmelCase_ ) == 4:
							# translation, XX, to YY
							lowerCamelCase					:      List[Any]          =   items[1]
							lowerCamelCase					:      str          =   items[3]
			return preprocess_params, forward_params, postprocess_params




	def __call__(			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Union[str, Any]:
			return super().__call__(*UpperCAmelCase_		,			**UpperCAmelCase_ )








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase (		a_,						a_,						a_ = 1E-12,						a_ = 100,						):





		'''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_      )
		lowerCamelCase					:      Optional[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.

		lowerCamelCase					:      Union[str, Any]          =   False
		lowerCamelCase					:      List[str]          =   0
		lowerCamelCase					:      Any          =   0
		lowerCamelCase					:      Dict          =   1E12

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

				# Check convergence.
				lowerCamelCase					:      Optional[int]          =   np.abs(lambda_ - lambda_previous      ) / lambda_
				iterations += 1

				if error <= error_tol or iterations >= max_iterations:
						lowerCamelCase					:      int          =   True

				lowerCamelCase					:      Optional[Any]          =   lambda_

		if is_complex:
				lowerCamelCase					:      Any          =   np.real(lambda_      )

		return lambda_, vector
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
		lowerCamelCase					:      str          =   np.array([41, 4, 20]      )
		lowerCamelCase					:      Optional[Any]          =   real_input_matrix.astype(np.complexaaa      )
		lowerCamelCase					:      Dict          =   np.triu(1j * complex_input_matrix,						1      )
		complex_input_matrix += imag_matrix
		complex_input_matrix += -1 * imag_matrix.T
		lowerCamelCase					:      List[Any]          =   np.array([41, 4, 20]      ).astype(np.complexaaa      )

		for problem_type in ["real", "complex"]:
				if problem_type == "real":
						lowerCamelCase					:      str          =   real_input_matrix
						lowerCamelCase					:      Any          =   real_vector
				elif problem_type == "complex":
						lowerCamelCase					:      str          =   complex_input_matrix
						lowerCamelCase					:      Dict          =   complex_vector

				# Our implementation.
				lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   power_iteration(a_,						a_      )

				# Numpy implementation.

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






