


'''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      ):





			'''simple docstring'''

			A	:							List[str]							= MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			A	:							int							= TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[str]  )			->		Dict:
						if isinstance(model.config ,						lowerCAmelCase_  ):
									__A,  __A=  list(tokenizer.lang_code_to_id.keys()  )[:2]
									__A=  TranslationPipeline(model=lowerCAmelCase_ ,						tokenizer=lowerCAmelCase_ ,						src_lang=lowerCAmelCase_ ,						tgt_lang=lowerCAmelCase_  )
						else:
									__A=  TranslationPipeline(model=lowerCAmelCase_ ,						tokenizer=lowerCAmelCase_  )
						return translator, ["Some string", "Some other text"]
			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Optional[int]  )			->		List[Any]:
						__A=  translator('Some string'  )
						self.assertEqual(lowerCAmelCase_ ,						[{'translation_text': ANY(lowerCAmelCase_  )}]  )

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

						__A=  translator(['Some string', 'other string']  )
						self.assertEqual(lowerCAmelCase_ ,						[{'translation_text': ANY(lowerCAmelCase_  )}, {'translation_text': ANY(lowerCAmelCase_  )}]  )
			@require_torch
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[Any]:
						__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(
						    lowerCAmelCase_ ,						[
						        {
						            'translation_text': (
						                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
						                ' Beide Beide'
						            )
						        }
						    ] ,						)
			@require_tf
			def 				lowerCAmelCase    (     self       : Dict  )			->		List[Any]:
						__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(
						    lowerCAmelCase_ ,						[
						        {
						            'translation_text': (
						                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
						                ' Beide Beide'
						            )
						        }
						    ] ,						)
			@require_torch
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		List[str]:
						__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(
						    lowerCAmelCase_ ,						[
						        {
						            '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       : int  )			->		Optional[int]:
						__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(
						    lowerCAmelCase_ ,						[
						        {
						            '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      ):





			'''simple docstring'''

			@require_torch
			@slow
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Union[str, Any]:
						# We don't provide a default for this pair
						with self.assertRaises(lowerCAmelCase_  ):
									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 				lowerCAmelCase    (     self       : List[Any]  )			->		Union[str, Any]:
						__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=lowerCAmelCase_ ,						tokenizer=lowerCAmelCase_  )
						# Missing src_lang, tgt_lang
						with self.assertRaises(lowerCAmelCase_  ):
									translator('This is a test'  )

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

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

						# src_lang, tgt_lang can be defined at pipeline call time
						__A=  pipeline(task='translation' ,						model=lowerCAmelCase_ ,						tokenizer=lowerCAmelCase_ ,						src_lang='en_XX' ,						tgt_lang='ar_AR'  )
						__A=  translator('This is a test'  )
						self.assertEqual(lowerCAmelCase_ ,						[{'translation_text': 'هذا إختبار'}]  )
			@require_torch
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		List[str]:
						__A=  'patrickvonplaten/t5-tiny-random'
						__A=  pipeline(task='translation_cn_to_ar' ,						model=lowerCAmelCase_  )
						self.assertEqual(translator._preprocess_params['src_lang'] ,						'cn'  )
						self.assertEqual(translator._preprocess_params['tgt_lang'] ,						'ar'  )
			@require_torch
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[Any]:
						__A=  'patrickvonplaten/t5-tiny-random'
						with pytest.warns(lowerCAmelCase_ ,						match=r'.*translation_en_to_de.*'  ):
									__A=  pipeline(task='translation' ,						model=lowerCAmelCase_  )
						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       : Any  )			->		Any:
						with self.assertRaises(lowerCAmelCase_  ):
									pipeline(task='translation'  )







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  hidden_act
						__A=  intermediate_size
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  initializer_range
						__A=  layer_norm_eps
						__A=  embedding_size
						__A=  trigram_input
						__A=  use_bottleneck
						__A=  intra_bottleneck_size
						__A=  use_bottleneck_attention
						__A=  key_query_shared_bottleneck
						__A=  num_feedforward_networks
						__A=  normalization_type
						__A=  classifier_activation

						if self.use_bottleneck:
									__A=  intra_bottleneck_size
						else:
									__A=  hidden_size

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

			@property
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]  )



