


"""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 					__magic_name__		(			unittest.TestCase     ):
       __A			:      List[str]        =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       __A			:      List[str]        =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






       def  __snake_case						(   self  :    str  ,     snake_case__  :    Dict  ,     snake_case__  :    Any  ,     snake_case__  :    Dict				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				TextaTextGenerationPipeline(model=snake_case__  ,     tokenizer=snake_case__				)
              return generator, ["Something to write", "Something else"]






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






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

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

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

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






       @require_torch
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






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

              lowercase		:Union[str, Any]       							=				3
              lowercase		:Optional[Any]       							=				generator(
                  '''Something there'''  ,     num_return_sequences=snake_case__  ,     num_beams=snake_case__  ,     )
              lowercase		:Dict       							=				[
                  {'''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(snake_case__  ,     snake_case__				)

              lowercase		:Tuple       							=				generator('''This is a test'''  ,     do_sample=snake_case__  ,     num_return_sequences=2  ,     return_tensors=snake_case__				)
              self.assertEqual(
                  snake_case__  ,     [
                      {'''generated_token_ids''': ANY(torch.Tensor				)},
                      {'''generated_token_ids''': ANY(torch.Tensor				)},
                  ]  ,     )
              lowercase		:List[Any]       							=				generator.model.config.eos_token_id
              lowercase		:Dict       							=				'''<pad>'''
              lowercase		:Dict       							=				generator(
                  ['''This is a test''', '''This is a second test''']  ,     do_sample=snake_case__  ,     num_return_sequences=2  ,     batch_size=2  ,     return_tensors=snake_case__  ,     )
              self.assertEqual(
                  snake_case__  ,     [
                      [
                          {'''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  :    str				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				pipeline('''text2text-generation'''  ,     model='''patrickvonplaten/t5-tiny-random'''  ,     framework='''tf'''				)
              # do_sample=False necessary for reproducibility
              lowercase		:str       							=				generator('''Something there'''  ,     do_sample=snake_case__				)
              self.assertEqual(snake_case__  ,     [{'''generated_text''': ''''''}]				)





"""simple docstring"""



import unittest

import numpy as np






def       lowerCamelCase					(a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray | None = None				,				)     ->							np.ndarray:
       lowercase		:str       							=				np.shape(a_)
       lowercase		:Dict       							=				np.shape(a_)
       lowercase		:Optional[int]       							=				np.shape(a_)

       if shape_a[0] != shape_b[0]:
              lowercase		:List[str]       							=				(
                  '''Expected the same number of rows for A and B. '''
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(a_)

       if shape_b[1] != shape_c[1]:
              lowercase		:List[str]       							=				(
                  '''Expected the same number of columns for B and C. '''
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(a_)

       lowercase		:Optional[Any]       							=				pseudo_inv
       if a_inv is None:
              try:
                     lowercase		:int       							=				np.linalg.inv(a_)
              except np.linalg.LinAlgError:
                     raise ValueError(
                         '''Input matrix A is not invertible. Cannot compute Schur complement.''')

       return mat_c - mat_b.T @ a_inv @ mat_b
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Dict       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:List[str]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              lowercase		:Any       							=				schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)

              lowercase		:int       							=				np.block([[a, b], [b.T, c]]				)

              lowercase		:Tuple       							=				np.linalg.det(snake_case__				)
              lowercase		:Any       							=				np.linalg.det(snake_case__				)
              lowercase		:List[Any]       							=				np.linalg.det(snake_case__				)

              self.assertAlmostEqual(snake_case__  ,     det_a * det_s				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:Union[str, Any]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              with self.assertRaises(snake_case__				):
                     schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)







       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:int       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[Any]       							=				np.array([[2, 1, 3], [6, 3, 5]]				)

              with self.assertRaises(snake_case__				):
                     schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

