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__				:   Dict								=			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			A__				:   str								=			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[str]:
								if isinstance(model.config      ,						UpperCAmelCase    ):
													__a					,      __a							=   list(tokenizer.lang_code_to_id.keys()    )[:2]
													__a							=   TranslationPipeline(model=UpperCAmelCase      ,						tokenizer=UpperCAmelCase      ,						src_lang=UpperCAmelCase      ,						tgt_lang=UpperCAmelCase    )
								else:
													__a							=   TranslationPipeline(model=UpperCAmelCase      ,						tokenizer=UpperCAmelCase    )
								return translator, ["Some string", "Some other text"]



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Tuple:
								__a							=   translator('Some string'    )
								self.assertEqual(UpperCAmelCase      ,						[{'translation_text': ANY(UpperCAmelCase    )}]    )

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

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



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   pipeline('translation_en_to_ro'      ,						model='patrickvonplaten/t5-tiny-random'      ,						framework='pt'    )
								__a							=   translator('This is a test string'      ,						max_length=2_0    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        {
								            '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    ) ->    Union[str, Any]:
								__a							=   pipeline('translation_en_to_ro'      ,						model='patrickvonplaten/t5-tiny-random'      ,						framework='tf'    )
								__a							=   translator('This is a test string'      ,						max_length=2_0    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        {
								            '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    ) ->    str:
								__a							=   pipeline('translation_en_to_de'      ,						model='patrickvonplaten/t5-tiny-random'      ,						framework='pt'    )
								__a							=   translator('This is a test string'      ,						max_length=2_0    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        {
								            '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    ) ->    Tuple:
								__a							=   pipeline('translation_en_to_de'      ,						model='patrickvonplaten/t5-tiny-random'      ,						framework='tf'    )
								__a							=   translator('This is a test string'      ,						max_length=2_0    )
								self.assertEqual(
								    UpperCAmelCase      ,						[
								        {
								            '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 __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								# We don't provide a default for this pair
								with self.assertRaises(UpperCAmelCase    ):
													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 __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__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=UpperCAmelCase      ,						tokenizer=UpperCAmelCase    )
								# Missing src_lang, tgt_lang
								with self.assertRaises(UpperCAmelCase    ):
													translator('This is a test'    )

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

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

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



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								__a							=   'patrickvonplaten/t5-tiny-random'
								__a							=   pipeline(task='translation_cn_to_ar'      ,						model=UpperCAmelCase    )
								self.assertEqual(translator._preprocess_params['src_lang']      ,						'cn'    )
								self.assertEqual(translator._preprocess_params['tgt_lang']      ,						'ar'    )



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   'patrickvonplaten/t5-tiny-random'
								with pytest.warns(UpperCAmelCase      ,						match=R'.*translation_en_to_de.*'    ):
													__a							=   pipeline(task='translation'      ,						model=UpperCAmelCase    )
								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:
								with self.assertRaises(UpperCAmelCase    ):
													pipeline(task='translation'    )






import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCamelCase_  :       List[Any]					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		*snake_case_       :				Optional[int]  ,		**snake_case_       :				List[Any]					):
							warnings.warn(
							    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV2ImageProcessor instead."""  ,		snake_case_  ,		)
							super().__init__(*snake_case_  ,		**snake_case_					)






