






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"""



     __snake_case		:				int  					=MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __snake_case		:				Tuple  					=TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Optional[Any])							->	int:

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





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	List[Any])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		translator('Some string')
      self.assertEqual(lowerCAmelCase__  ,  [{'translation_text': ANY(lowerCAmelCase__)}])

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

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





     @require_torch
     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		pipeline('translation_en_to_ro'  ,  model='patrickvonplaten/t5-tiny-random'  ,  framework='pt')
      SCREAMING_SNAKE_CASE				    =		translator('This is a test string'  ,  max_length=2_0)
      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     __UpperCamelCase						(		self					:	str)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		pipeline('translation_en_to_ro'  ,  model='patrickvonplaten/t5-tiny-random'  ,  framework='tf')
      SCREAMING_SNAKE_CASE				    =		translator('This is a test string'  ,  max_length=2_0)
      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     __UpperCamelCase						(		self					:	Optional[int])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		pipeline('translation_en_to_de'  ,  model='patrickvonplaten/t5-tiny-random'  ,  framework='pt')
      SCREAMING_SNAKE_CASE				    =		translator('This is a test string'  ,  max_length=2_0)
      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     __UpperCamelCase						(		self					:	List[str])							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		pipeline('translation_en_to_de'  ,  model='patrickvonplaten/t5-tiny-random'  ,  framework='tf')
      SCREAMING_SNAKE_CASE				    =		translator('This is a test string'  ,  max_length=2_0)
      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     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      # 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
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	List[Any])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt')
      SCREAMING_SNAKE_CASE				    =		MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt')

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

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

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





     @require_torch
     def     __UpperCamelCase						(		self					:	str)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'patrickvonplaten/t5-tiny-random'
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Dict)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'patrickvonplaten/t5-tiny-random'
      with pytest.warns(lowerCAmelCase__  ,  match=R'.*translation_en_to_de.*'):
       SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      with self.assertRaises(lowerCAmelCase__):
       pipeline(task='translation')












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase      =				logging.get_logger(__name__)
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Union[str, Any]  					='''speech-encoder-decoder'''
     __snake_case		:				List[Any]  					=True





     def __init__(		self					:	Any  ,  **lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      if "encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError(
           f'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
           f''' `decoder` sub-configurations are passed, but only {kwargs}''')

      SCREAMING_SNAKE_CASE				    =		kwargs.pop('encoder')
      SCREAMING_SNAKE_CASE				    =		encoder_config.pop('model_type')
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('decoder')
      SCREAMING_SNAKE_CASE				    =		decoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		True





     @classmethod
     def     __UpperCamelCase						(		cls					:	str  ,  lowerCAmelCase__					:	PretrainedConfig  ,  lowerCAmelCase__					:	PretrainedConfig  ,  **lowerCAmelCase__					:	List[str])							->	PretrainedConfig:

      """simple docstring"""
      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		True

      return cls(encoder=encoder_config.to_dict()  ,  decoder=decoder_config.to_dict()  ,  **lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE				    =		self.encoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.decoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.__class__.model_type
      return output




