
'''simple docstring'''





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class 			UpperCamelCase__	(     unittest.TestCase):
 UpperCAmelCase__     :     Any    					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 UpperCAmelCase__     :     Any    					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






 def 						lowercase_    (      self	:str						,					_A	:List[Any]						,					_A	:Optional[int]						,					_A	:Tuple      )					->      int:







       '''simple docstring'''



       if isinstance(model.config						,					_A      ):
             __A						,   __A													=      list(tokenizer.lang_code_to_id.keys()      )[:2]
             __A													=      TranslationPipeline(model=_A						,					tokenizer=_A						,					src_lang=_A						,					tgt_lang=_A      )
       else:
             __A													=      TranslationPipeline(model=_A						,					tokenizer=_A      )
       return translator, ["Some string", "Some other text"]






 def 						lowercase_    (      self	:Any						,					_A	:Union[str, Any]						,					_A	:Dict      )					->      str:







       '''simple docstring'''



       __A													=      translator('Some string'      )
       self.assertEqual(_A						,					[{'translation_text': ANY(_A      )}]      )

       __A													=      translator(['Some string']      )
       self.assertEqual(_A						,					[{'translation_text': ANY(_A      )}]      )

       __A													=      translator(['Some string', 'other string']      )
       self.assertEqual(_A						,					[{'translation_text': ANY(_A      )}, {'translation_text': ANY(_A      )}]      )






 @require_torch
 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      pipeline('translation_en_to_ro'						,					model='patrickvonplaten/t5-tiny-random'						,					framework='pt'      )
       __A													=      translator('This is a test string'						,					max_length=20      )
       self.assertEqual(
           _A						,					[
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]						,					)






 @require_tf
 def 						lowercase_    (      self	:Any      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      pipeline('translation_en_to_ro'						,					model='patrickvonplaten/t5-tiny-random'						,					framework='tf'      )
       __A													=      translator('This is a test string'						,					max_length=20      )
       self.assertEqual(
           _A						,					[
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]						,					)






 @require_torch
 def 						lowercase_    (      self	:str      )					->      Any:







       '''simple docstring'''



       __A													=      pipeline('translation_en_to_de'						,					model='patrickvonplaten/t5-tiny-random'						,					framework='pt'      )
       __A													=      translator('This is a test string'						,					max_length=20      )
       self.assertEqual(
           _A						,					[
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]						,					)






 @require_tf
 def 						lowercase_    (      self	:Tuple      )					->      str:







       '''simple docstring'''



       __A													=      pipeline('translation_en_to_de'						,					model='patrickvonplaten/t5-tiny-random'						,					framework='tf'      )
       __A													=      translator('This is a test string'						,					max_length=20      )
       self.assertEqual(
           _A						,					[
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]						,					)


class 			UpperCamelCase__	(     unittest.TestCase):






 @require_torch
 @slow
 def 						lowercase_    (      self	:List[str]      )					->      List[Any]:







       '''simple docstring'''



       with self.assertRaises(_A      ):
             pipeline(task='translation_cn_to_ar'      )

       # but we do for this one
       __A													=      pipeline(task='translation_en_to_de'      )
       self.assertEqual(translator._preprocess_params['src_lang']						,					'en'      )
       self.assertEqual(translator._preprocess_params['tgt_lang']						,					'de'      )






 @require_torch
 @slow
 def 						lowercase_    (      self	:str      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'      )
       __A													=      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'      )

       __A													=      pipeline(task='translation'						,					model=_A						,					tokenizer=_A      )
       # Missing src_lang, tgt_lang
       with self.assertRaises(_A      ):
             translator('This is a test'      )

       __A													=      translator('This is a test'						,					src_lang='en_XX'						,					tgt_lang='ar_AR'      )
       self.assertEqual(_A						,					[{'translation_text': 'هذا إختبار'}]      )

       __A													=      translator('This is a test'						,					src_lang='en_XX'						,					tgt_lang='hi_IN'      )
       self.assertEqual(_A						,					[{'translation_text': 'यह एक परीक्षण है'}]      )

       # src_lang, tgt_lang can be defined at pipeline call time
       __A													=      pipeline(task='translation'						,					model=_A						,					tokenizer=_A						,					src_lang='en_XX'						,					tgt_lang='ar_AR'      )
       __A													=      translator('This is a test'      )
       self.assertEqual(_A						,					[{'translation_text': 'هذا إختبار'}]      )






 @require_torch
 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       __A													=      'patrickvonplaten/t5-tiny-random'
       __A													=      pipeline(task='translation_cn_to_ar'						,					model=_A      )
       self.assertEqual(translator._preprocess_params['src_lang']						,					'cn'      )
       self.assertEqual(translator._preprocess_params['tgt_lang']						,					'ar'      )






 @require_torch
 def 						lowercase_    (      self	:Any      )					->      Any:







       '''simple docstring'''



       __A													=      'patrickvonplaten/t5-tiny-random'
       with pytest.warns(_A						,					match=R'.*translation_en_to_de.*'      ):
             __A													=      pipeline(task='translation'						,					model=_A      )
       self.assertEqual(translator.task						,					'translation_en_to_de'      )
       self.assertEqual(translator._preprocess_params['src_lang']						,					'en'      )
       self.assertEqual(translator._preprocess_params['tgt_lang']						,					'de'      )






 @require_torch
 def 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



       with self.assertRaises(_A      ):
             pipeline(task='translation'      )









"""simple docstring"""




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



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




