





'''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       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Optional[int]							,  __snake_case						:	Any							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											if isinstance(model.config							,  __snake_case							):
																	_snake_case       ,    _snake_case: Dict	       =		list(tokenizer.lang_code_to_id.keys()							)[:2]
																	_snake_case: List[Any]	       =		TranslationPipeline(model=__snake_case							,  tokenizer=__snake_case							,  src_lang=__snake_case							,  tgt_lang=__snake_case							)
											else:
																	_snake_case: List[str]	       =		TranslationPipeline(model=__snake_case							,  tokenizer=__snake_case							)
											return translator, ["Some string", "Some other text"]


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





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

											_snake_case: int	       =		translator(['Some string']							)
											self.assertEqual(__snake_case							,  [{'translation_text': ANY(__snake_case							)}]							)

											_snake_case: Union[str, Any]	       =		translator(['Some string', 'other string']							)
											self.assertEqual(__snake_case							,  [{'translation_text': ANY(__snake_case							)}, {'translation_text': ANY(__snake_case							)}]							)


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





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


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





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


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





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




					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





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







class       lowerCamelCase       (    unittest.TestCase	):


					@require_torch
					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





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

											# but we do for this one
											_snake_case: Union[str, 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  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'							)
											_snake_case: Tuple	       =		MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'							)

											_snake_case: Union[str, Any]	       =		pipeline(task='translation'							,  model=__snake_case							,  tokenizer=__snake_case							)
											# Missing src_lang, tgt_lang
											with self.assertRaises(__snake_case							):
																	translator('This is a test'							)

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

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

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


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		'patrickvonplaten/t5-tiny-random'
											_snake_case: Any	       =		pipeline(task='translation_cn_to_ar'							,  model=__snake_case							)
											self.assertEqual(translator._preprocess_params['src_lang']							,  'cn'							)
											self.assertEqual(translator._preprocess_params['tgt_lang']							,  'ar'							)


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		'patrickvonplaten/t5-tiny-random'
											with pytest.warns(__snake_case							,  match=R'.*translation_en_to_de.*'							):
																	_snake_case: int	       =		pipeline(task='translation'							,  model=__snake_case							)
											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  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





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












'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    lowercase_   (		lowercase__					,	lowercase__    ) ->List[str]:
						assert isinstance(lowercase__					,	lowercase__    )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'					,	[False, True]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





@pytest.mark.parametrize(
    'features'					,	[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					,	)
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: str	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: Union[str, Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: List[str]	       =		features.copy() if features else default_expected_features
						_snake_case: str	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Optional[int]	       =		CsvDatasetReader(lowercase__					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						_snake_case: Dict	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	split=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'					,	[str, list]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						if issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Optional[Any]	       =		csv_path
						elif issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Tuple	       =		[csv_path]
						_snake_case: Tuple	       =		tmp_path / 'cache'
						_snake_case: Any	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Union[str, Any]	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__=("train",)    ) ->str:
						assert isinstance(lowercase__					,	lowercase__    )
						for split in splits:
												_snake_case: Optional[int]	       =		dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'					,	[False, True]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: Optional[Any]	       =		tmp_path / 'cache'
						_snake_case: Optional[Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Dict	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__    )





@pytest.mark.parametrize(
    'features'					,	[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					,	)
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: str	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		features.copy() if features else default_expected_features
						_snake_case: int	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Tuple	       =		CsvDatasetReader({'train': csv_path}					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						if split:
												_snake_case: Optional[Any]	       =		{split: csv_path}
						else:
												_snake_case: Tuple	       =		{'train': csv_path, 'test': csv_path}
						_snake_case: Union[str, Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__					,	splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )





def    lowercase_   (		lowercase__    ) ->Union[str, Any]:
						with open(lowercase__					,	encoding='utf-8'    ) as csvfile:
												yield from csv.reader(lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: Optional[Any]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=1    ).write()

						_snake_case: Optional[int]	       =		iter_csv_file(lowercase__    )
						_snake_case: List[Any]	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						_snake_case: List[str]	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: List[str]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=2    ).write()

						_snake_case: Optional[Any]	       =		iter_csv_file(lowercase__    )
						_snake_case: Tuple	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Any:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: Any	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: str	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						with pytest.raises(lowercase__    ):
												CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=0    )





