'''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     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






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

  __lowerCAmelCase    							=						translator(["""Some string"""]							)
  self.assertEqual(SCREAMING_SNAKE_CASE__				,	[{"""translation_text""": ANY(SCREAMING_SNAKE_CASE__							)}]							)

  __lowerCAmelCase    							=						translator(["""Some string""", """other string"""]							)
  self.assertEqual(SCREAMING_SNAKE_CASE__				,	[{"""translation_text""": ANY(SCREAMING_SNAKE_CASE__							)}, {"""translation_text""": ANY(SCREAMING_SNAKE_CASE__							)}]							)
 @require_torch
 def a (				self       :     Any							)  ->   int:
  __lowerCAmelCase    							=						pipeline("""translation_en_to_ro"""				,	model="""patrickvonplaten/t5-tiny-random"""				,	framework="""pt"""							)
  __lowerCAmelCase    							=						translator("""This is a test string"""				,	max_length=20							)
  self.assertEqual(
      SCREAMING_SNAKE_CASE__				,	[
          {
              """translation_text""": (
                  """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                  """ Beide Beide"""
              )
          }
      ]				,	)
 @require_tf
 def a (				self       :     Union[str, Any]							)  ->   int:
  __lowerCAmelCase    							=						pipeline("""translation_en_to_ro"""				,	model="""patrickvonplaten/t5-tiny-random"""				,	framework="""tf"""							)
  __lowerCAmelCase    							=						translator("""This is a test string"""				,	max_length=20							)
  self.assertEqual(
      SCREAMING_SNAKE_CASE__				,	[
          {
              """translation_text""": (
                  """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                  """ Beide Beide"""
              )
          }
      ]				,	)
 @require_torch
 def a (				self       :     Tuple							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						pipeline("""translation_en_to_de"""				,	model="""patrickvonplaten/t5-tiny-random"""				,	framework="""pt"""							)
  __lowerCAmelCase    							=						translator("""This is a test string"""				,	max_length=20							)
  self.assertEqual(
      SCREAMING_SNAKE_CASE__				,	[
          {
              """translation_text""": (
                  """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                  """ urine urine urine urine urine urine urine"""
              )
          }
      ]				,	)





 @require_tf
 def a (				self       :     Optional[int]							)  ->   str:
  __lowerCAmelCase    							=						pipeline("""translation_en_to_de"""				,	model="""patrickvonplaten/t5-tiny-random"""				,	framework="""tf"""							)
  __lowerCAmelCase    							=						translator("""This is a test string"""				,	max_length=20							)
  self.assertEqual(
      SCREAMING_SNAKE_CASE__				,	[
          {
              """translation_text""": (
                  """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                  """ urine urine urine urine urine urine urine"""
              )
          }
      ]				,	)







class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 @require_torch
 @slow
 def a (				self       :     List[str]							)  ->   Dict:
  # We don't provide a default for this pair
  with self.assertRaises(SCREAMING_SNAKE_CASE__							):
   pipeline(task="""translation_cn_to_ar"""							)

  # but we do for this one
  __lowerCAmelCase    							=						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 a (				self       :     Optional[Any]							)  ->   Tuple:
  __lowerCAmelCase    							=						MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""							)
  __lowerCAmelCase    							=						MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""							)

  __lowerCAmelCase    							=						pipeline(task="""translation"""				,	model=SCREAMING_SNAKE_CASE__				,	tokenizer=SCREAMING_SNAKE_CASE__							)
  # Missing src_lang, tgt_lang
  with self.assertRaises(SCREAMING_SNAKE_CASE__							):
   translator("""This is a test"""							)

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

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

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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






