



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		):
			UpperCamelCase     =	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			UpperCamelCase     =	TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _lowerCamelCase			(		self  :    str					,						A  :    Any					,						A  :    str					,						A  :    Optional[Any])  ->       List[str]:



						"""simple docstring"""






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



			def        _lowerCamelCase			(		self  :    Optional[Any]					,						A  :    str					,						A  :    str)  ->       List[Any]:



						"""simple docstring"""






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

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

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



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



						"""simple docstring"""






						_UpperCAmelCase									=  pipeline('translation_en_to_ro'					,						model='patrickvonplaten/t5-tiny-random'					,						framework='pt')
						_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    Optional[Any])  ->       str:



						"""simple docstring"""






						_UpperCAmelCase									=  pipeline('translation_en_to_ro'					,						model='patrickvonplaten/t5-tiny-random'					,						framework='tf')
						_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    Dict)  ->       Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  pipeline('translation_en_to_de'					,						model='patrickvonplaten/t5-tiny-random'					,						framework='pt')
						_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    str)  ->       int:



						"""simple docstring"""






						_UpperCAmelCase									=  pipeline('translation_en_to_de'					,						model='patrickvonplaten/t5-tiny-random'					,						framework='tf')
						_UpperCAmelCase									=  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      __lowerCAmelCase       (     unittest.TestCase		):



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



						"""simple docstring"""






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

						# but we do for this one
						_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    Tuple)  ->       Optional[int]:



						"""simple docstring"""






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

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

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

						_UpperCAmelCase									=  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
						_UpperCAmelCase									=  pipeline(task='translation'					,						model=A					,						tokenizer=A					,						src_lang='en_XX'					,						tgt_lang='ar_AR')
						_UpperCAmelCase									=  translator('This is a test')
						self.assertEqual(A					,						[{'translation_text': 'هذا إختبار'}])



			@require_torch
			def        _lowerCamelCase			(		self  :    Dict)  ->       int:



						"""simple docstring"""






						_UpperCAmelCase									=  'patrickvonplaten/t5-tiny-random'
						_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    str)  ->       List[str]:



						"""simple docstring"""






						_UpperCAmelCase									=  'patrickvonplaten/t5-tiny-random'
						with pytest.warns(A					,						match=R'.*translation_en_to_de.*'):
									_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    Dict)  ->       str:



						"""simple docstring"""






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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  =			logging.get_logger(__name__)

UpperCAmelCase__  =			{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}







class      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	'''vilt'''



			def __init__(		self  :    List[Any]					,						A  :    Dict=3_05_22					,						A  :    Union[str, Any]=2					,						A  :    Tuple=2					,						A  :    List[Any]=40					,						A  :    Optional[int]=7_68					,						A  :    Any=12					,						A  :    Optional[int]=12					,						A  :    Optional[Any]=30_72					,						A  :    Optional[Any]="gelu"					,						A  :    str=0.0					,						A  :    Any=0.0					,						A  :    Union[str, Any]=0.0_2					,						A  :    Union[str, Any]=1E-12					,						A  :    Optional[Any]=3_84					,						A  :    Dict=32					,						A  :    int=3					,						A  :    int=True					,						A  :    Union[str, Any]=-1					,						A  :    int=False					,						A  :    int=-1					,						**A  :    Tuple					,						)  ->       Dict:



						"""simple docstring"""






						super().__init__(tie_word_embeddings=A					,						**A)

						_UpperCAmelCase									=  vocab_size
						_UpperCAmelCase									=  type_vocab_size
						_UpperCAmelCase									=  modality_type_vocab_size
						_UpperCAmelCase									=  max_position_embeddings

						_UpperCAmelCase									=  hidden_size
						_UpperCAmelCase									=  num_hidden_layers
						_UpperCAmelCase									=  num_attention_heads
						_UpperCAmelCase									=  intermediate_size
						_UpperCAmelCase									=  hidden_act
						_UpperCAmelCase									=  hidden_dropout_prob
						_UpperCAmelCase									=  attention_probs_dropout_prob
						_UpperCAmelCase									=  initializer_range
						_UpperCAmelCase									=  layer_norm_eps

						_UpperCAmelCase									=  image_size
						_UpperCAmelCase									=  patch_size
						_UpperCAmelCase									=  num_channels
						_UpperCAmelCase									=  qkv_bias
						_UpperCAmelCase									=  max_image_length
						_UpperCAmelCase									=  num_images
