

'''simple docstring'''




from __future__ import annotations

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




def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						)				->     list[float]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    coefficient_matrix.shape
     __magic_name__       ,  __magic_name__						       =    constant_matrix.shape

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

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

     if rowsa != rowsa:
          __magic_name__						       =    (
              '''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:
          __magic_name__						       =    (
              '''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'''			)

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

     __magic_name__       ,  __magic_name__						       =    table.shape

     strictly_diagonally_dominant(a			)

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

     return [float(a			) for i in new_val]




def 						UpperCamelCase	(			a			)				->     bool:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    table.shape

     __magic_name__						       =    True

     for i in range(0		,						a			):
          __magic_name__						       =    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()









'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
      import torch






@is_pipeline_test
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE					:Any      	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Tuple			,				a__		:   int			,				a__		:   int      ):
      __magic_name__						       =    TextaTextGenerationPipeline(model=a__			,				tokenizer=a__      )
      return generator, ["Something to write", "Something else"]



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    generator('''Something there'''      )
      self.assertEqual(a__			,				[{'''generated_text''': ANY(a__      )}]      )
      # These are encoder decoder, they don't just append to incoming string
      self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

      __magic_name__						       =    generator(['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      __magic_name__						       =    generator(
          ['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				batch_size=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      with self.assertRaises(a__      ):
           generator(4      )



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''pt'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )

      __magic_name__						       =    3
      __magic_name__						       =    generator(
          '''Something there'''			,				num_return_sequences=a__			,				num_beams=a__			,				)
      __magic_name__						       =    [
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': ''''''},
      ]
      self.assertEqual(a__			,				a__      )

      __magic_name__						       =    generator('''This is a test'''			,				do_sample=a__			,				num_return_sequences=2			,				return_tensors=a__      )
      self.assertEqual(
          a__			,				[
              {'''generated_token_ids''': ANY(torch.Tensor      )},
              {'''generated_token_ids''': ANY(torch.Tensor      )},
          ]			,				)
      __magic_name__						       =    generator.model.config.eos_token_id
      __magic_name__						       =    '''<pad>'''
      __magic_name__						       =    generator(
          ['''This is a test''', '''This is a second test''']			,				do_sample=a__			,				num_return_sequences=2			,				batch_size=2			,				return_tensors=a__			,				)
      self.assertEqual(
          a__			,				[
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
          ]			,				)



 @require_tf
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''tf'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )






