





'''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    __a (unittest.TestCase       ):
							__a   :				Union[str, Any]       =    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__a   :				Tuple       =    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       Union[str, Any]    ,       __magic_name__							:       Dict    ,       __magic_name__							:       List[Any]				)   ->    Union[str, Any]:







													"""simple docstring"""
													if isinstance(model.config    ,       __magic_name__				):
																			UpperCAmelCase_  ,    UpperCAmelCase_     : str         =					list(tokenizer.lang_code_to_id.keys()				)[:2]
																			UpperCAmelCase_     : Optional[int]         =					TranslationPipeline(model=__magic_name__    ,       tokenizer=__magic_name__    ,       src_lang=__magic_name__    ,       tgt_lang=__magic_name__				)
													else:
																			UpperCAmelCase_     : str         =					TranslationPipeline(model=__magic_name__    ,       tokenizer=__magic_name__				)
													return translator, ["Some string", "Some other text"]

							def       UpperCAmelCase__		(							self							:       Dict    ,       __magic_name__							:       int    ,       __magic_name__							:       Dict				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					translator('''Some string'''				)
													self.assertEqual(__magic_name__    ,       [{'''translation_text''': ANY(__magic_name__				)}]				)

													UpperCAmelCase_     : str         =					translator(['''Some string''']				)
													self.assertEqual(__magic_name__    ,       [{'''translation_text''': ANY(__magic_name__				)}]				)

													UpperCAmelCase_     : int         =					translator(['''Some string''', '''other string''']				)
													self.assertEqual(__magic_name__    ,       [{'''translation_text''': ANY(__magic_name__				)}, {'''translation_text''': ANY(__magic_name__				)}]				)

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







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					pipeline('''translation_en_to_ro'''    ,       model='''patrickvonplaten/t5-tiny-random'''    ,       framework='''pt'''				)
													UpperCAmelCase_     : Union[str, Any]         =					translator('''This is a test string'''    ,       max_length=20				)
													self.assertEqual(
													    __magic_name__    ,       [
													        {
													            '''translation_text''': (
													                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
													                ''' Beide Beide'''
													            )
													        }
													    ]    ,       )

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







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					pipeline('''translation_en_to_ro'''    ,       model='''patrickvonplaten/t5-tiny-random'''    ,       framework='''tf'''				)
													UpperCAmelCase_     : Dict         =					translator('''This is a test string'''    ,       max_length=20				)
													self.assertEqual(
													    __magic_name__    ,       [
													        {
													            '''translation_text''': (
													                '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
													                ''' Beide Beide'''
													            )
													        }
													    ]    ,       )

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







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					pipeline('''translation_en_to_de'''    ,       model='''patrickvonplaten/t5-tiny-random'''    ,       framework='''pt'''				)
													UpperCAmelCase_     : List[Any]         =					translator('''This is a test string'''    ,       max_length=20				)
													self.assertEqual(
													    __magic_name__    ,       [
													        {
													            '''translation_text''': (
													                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
													                ''' urine urine urine urine urine urine urine'''
													            )
													        }
													    ]    ,       )



							@require_tf
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					pipeline('''translation_en_to_de'''    ,       model='''patrickvonplaten/t5-tiny-random'''    ,       framework='''tf'''				)
													UpperCAmelCase_     : List[Any]         =					translator('''This is a test string'''    ,       max_length=20				)
													self.assertEqual(
													    __magic_name__    ,       [
													        {
													            '''translation_text''': (
													                '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
													                ''' urine urine urine urine urine urine urine'''
													            )
													        }
													    ]    ,       )





class    __a (unittest.TestCase       ):

							@require_torch
							@slow
							def       UpperCAmelCase__		(							self							:       str				)   ->    str:







													"""simple docstring"""
													# We don't provide a default for this pair
													with self.assertRaises(__magic_name__				):
																			pipeline(task='''translation_cn_to_ar'''				)

													# but we do for this one
													UpperCAmelCase_     : Optional[Any]         =					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       UpperCAmelCase__		(							self							:       str				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''				)
													UpperCAmelCase_     : Any         =					MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''				)

													UpperCAmelCase_     : List[str]         =					pipeline(task='''translation'''    ,       model=__magic_name__    ,       tokenizer=__magic_name__				)
													# Missing src_lang, tgt_lang
													with self.assertRaises(__magic_name__				):
																			translator('''This is a test'''				)

													UpperCAmelCase_     : Union[str, Any]         =					translator('''This is a test'''    ,       src_lang='''en_XX'''    ,       tgt_lang='''ar_AR'''				)
													self.assertEqual(__magic_name__    ,       [{'''translation_text''': '''هذا إختبار'''}]				)

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

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

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







													"""simple docstring"""
													UpperCAmelCase_     : Any         =					'''patrickvonplaten/t5-tiny-random'''
													UpperCAmelCase_     : List[Any]         =					pipeline(task='''translation_cn_to_ar'''    ,       model=__magic_name__				)
													self.assertEqual(translator._preprocess_params['''src_lang''']    ,       '''cn'''				)
													self.assertEqual(translator._preprocess_params['''tgt_lang''']    ,       '''ar'''				)

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







													"""simple docstring"""
													UpperCAmelCase_     : int         =					'''patrickvonplaten/t5-tiny-random'''
													with pytest.warns(__magic_name__    ,       match=R'''.*translation_en_to_de.*'''				):
																			UpperCAmelCase_     : Optional[Any]         =					pipeline(task='''translation'''    ,       model=__magic_name__				)
													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       UpperCAmelCase__		(							self							:       str				)   ->    Any:







													"""simple docstring"""
													with self.assertRaises(__magic_name__				):
																			pipeline(task='''translation'''				)












'''simple docstring'''







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





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					np.shape(SCREAMING_SNAKE_CASE__				)

						UpperCAmelCase_     : NDArray[floataa]         =					np.zeros((rows, 1),     dtype=SCREAMING_SNAKE_CASE__				)
						for row in reversed(range(SCREAMING_SNAKE_CASE__				)				):
												UpperCAmelCase_     : Union[str, Any]         =					0
												for col in range(row + 1,     SCREAMING_SNAKE_CASE__				):
																		total += coefficients[row, col] * x[col]

												UpperCAmelCase_     : str         =					(vector[row] - total) / coefficients[row, row]

						return x





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : List[Any]         =					np.shape(SCREAMING_SNAKE_CASE__				)
						if rows != columns:
												return np.array((),     dtype=SCREAMING_SNAKE_CASE__				)

						# augmented matrix
						UpperCAmelCase_     : NDArray[floataa]         =					np.concatenate((coefficients, vector),     axis=1				)
						UpperCAmelCase_     : Optional[Any]         =					augmented_mat.astype('''float64'''				)

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

						UpperCAmelCase_     : Optional[Any]         =					retroactive_resolution(
						    augmented_mat[:, 0:columns],     augmented_mat[:, columns : columns + 1]				)

						return x


if __name__ == "__main__":
					import doctest

					doctest.testmod()





