
'''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 			UpperCamelCase__	(     unittest.TestCase):
 UpperCAmelCase__     :     Any    					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 UpperCAmelCase__     :     Any    					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






 def 						lowercase_    (      self	:str						,					_A	:List[Any]						,					_A	:Optional[int]						,					_A	:Tuple      )					->      int:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Any						,					_A	:Union[str, Any]						,					_A	:Dict      )					->      str:







       '''simple docstring'''



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

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

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






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







       '''simple docstring'''



       __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(
           _A						,					[
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]						,					)






 @require_tf
 def 						lowercase_    (      self	:Any      )					->      Union[str, Any]:







       '''simple docstring'''



       __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(
           _A						,					[
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]						,					)






 @require_torch
 def 						lowercase_    (      self	:str      )					->      Any:







       '''simple docstring'''



       __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(
           _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 						lowercase_    (      self	:Tuple      )					->      str:







       '''simple docstring'''



       __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(
           _A						,					[
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]						,					)


class 			UpperCamelCase__	(     unittest.TestCase):






 @require_torch
 @slow
 def 						lowercase_    (      self	:List[str]      )					->      List[Any]:







       '''simple docstring'''



       with self.assertRaises(_A      ):
             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 						lowercase_    (      self	:str      )					->      Optional[int]:







       '''simple docstring'''



       __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=_A						,					tokenizer=_A      )
       # Missing src_lang, tgt_lang
       with self.assertRaises(_A      ):
             translator('This is a test'      )

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

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






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







       '''simple docstring'''



       __A													=      'patrickvonplaten/t5-tiny-random'
       __A													=      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 						lowercase_    (      self	:Any      )					->      Any:







       '''simple docstring'''



       __A													=      'patrickvonplaten/t5-tiny-random'
       with pytest.warns(_A						,					match=R'.*translation_en_to_de.*'      ):
             __A													=      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 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



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













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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

