


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

lowerCAmelCase__  :Dict             =							logging.get_logger(__name__)

class 		__a				(    enum.Enum      ):
		_a     :  List[Any]           =     0
		_a     :  str           =     1



@add_end_docstrings(UpperCAmelCase      )
class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     'generated'
		def __init__(      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									super().__init__(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)

									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      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      {}
									if truncation is not None:
																_UpperCAmelCase					=      truncation

									_UpperCAmelCase					=      generate_kwargs

									_UpperCAmelCase					=      {}
									if return_tensors is not None and return_type is None:
																_UpperCAmelCase					=      ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
																_UpperCAmelCase					=      return_type

									if clean_up_tokenization_spaces is not None:
																_UpperCAmelCase					=      clean_up_tokenization_spaces

									if stop_sequence is not None:
																_UpperCAmelCase					=      self.tokenizer.encode(_SCREAMING_SNAKE_CASE      ,   add_special_tokens=_SCREAMING_SNAKE_CASE						)
																if len(_SCREAMING_SNAKE_CASE						) > 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.'						)
																_UpperCAmelCase					=      stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									return True
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model.config.prefix if self.model.config.prefix is not None else ''''''
									if isinstance(args[0]      ,   _SCREAMING_SNAKE_CASE						):
																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'						)
																_UpperCAmelCase					=      ([prefix + arg for arg in args[0]],)
																_UpperCAmelCase					=      True

									elif isinstance(args[0]      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      (prefix + args[0],)
																_UpperCAmelCase					=      False
									else:
																raise ValueError(
																    f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''						)
									_UpperCAmelCase					=      self.tokenizer(*_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   truncation=_SCREAMING_SNAKE_CASE      ,   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      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      super().__call__(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									if (
									    isinstance(args[0]      ,   _SCREAMING_SNAKE_CASE						)
									    and all(isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for el in args[0]						)
									    and all(len(_SCREAMING_SNAKE_CASE						) == 1 for res in result						)
									):
																return [res[0] for res in result]
									return result
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=TruncationStrategy.DO_NOT_TRUNCATE      ,   **_SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self._parse_and_tokenize(_SCREAMING_SNAKE_CASE      ,   truncation=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									return inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									if self.framework == "pt":
																_UpperCAmelCase					=      model_inputs['''input_ids'''].shape
									elif self.framework == "tf":
																_UpperCAmelCase					=      tf.shape(model_inputs['input_ids']						).numpy()

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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=ReturnType.TEXT      ,   _SCREAMING_SNAKE_CASE=False						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      []
									for output_ids in model_outputs["output_ids"][0]:
																if return_type == ReturnType.TENSORS:
																							_UpperCAmelCase					=      {f'''{self.return_name}_token_ids''': output_ids}
																elif return_type == ReturnType.TEXT:
																							_UpperCAmelCase					=      {
																							    f'''{self.return_name}_text''': self.tokenizer.decode(
																							        _SCREAMING_SNAKE_CASE      ,   skip_special_tokens=_SCREAMING_SNAKE_CASE      ,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE      ,   )
																							}
																records.append(_SCREAMING_SNAKE_CASE						)
									return records



@add_end_docstrings(UpperCAmelCase      )
class 		__a				(    UpperCAmelCase      ):
		_a     :  List[Any]           =     'summary'
		def __call__(      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									return super().__call__(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	bool:




									"""simple docstring"""





									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 		__a				(    UpperCAmelCase      ):
		_a     :  int           =     'translation'
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									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      ,   *_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=TruncationStrategy.DO_NOT_TRUNCATE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None						)			->	Tuple:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase					=      super()._sanitize_parameters(**_SCREAMING_SNAKE_CASE						)
									if src_lang is not None:
																_UpperCAmelCase					=      src_lang
									if tgt_lang is not None:
																_UpperCAmelCase					=      tgt_lang
									if src_lang is None and tgt_lang is None:
																# Backward compatibility, direct arguments use is preferred.
																_UpperCAmelCase					=      kwargs.get('task'      ,   self.task						)
																_UpperCAmelCase					=      task.split('_'						)
																if task and len(_SCREAMING_SNAKE_CASE						) == 4:
																							# translation, XX, to YY
																							_UpperCAmelCase					=      items[1]
																							_UpperCAmelCase					=      items[3]
									return preprocess_params, forward_params, postprocess_params






		def __call__(      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									return super().__call__(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
