


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   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  _SCREAMING_SNAKE_CASE						      =    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



  def    lowerCAmelCase__					(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    Tuple      ,			_lowerCAmelCase    :    Tuple					)				->	Optional[int]:




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



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




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

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

     snake_case_         =   translator(["Some string", "other string"]					)
     self.assertEqual(_lowerCAmelCase      ,			[{"translation_text": ANY(_lowerCAmelCase					)}, {"translation_text": ANY(_lowerCAmelCase					)}]					)



  @require_torch
  def    lowerCAmelCase__					(       self    :    Dict					)				->	Dict:




     """simple docstring"""
     snake_case_         =   pipeline("translation_en_to_ro"      ,			model="patrickvonplaten/t5-tiny-random"      ,			framework="pt"					)
     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    lowerCAmelCase__					(       self    :    Tuple					)				->	Any:




     """simple docstring"""
     snake_case_         =   pipeline("translation_en_to_ro"      ,			model="patrickvonplaten/t5-tiny-random"      ,			framework="tf"					)
     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    lowerCAmelCase__					(       self    :    Dict					)				->	Optional[Any]:




     """simple docstring"""
     snake_case_         =   pipeline("translation_en_to_de"      ,			model="patrickvonplaten/t5-tiny-random"      ,			framework="pt"					)
     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    lowerCAmelCase__					(       self    :    Any					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   pipeline("translation_en_to_de"      ,			model="patrickvonplaten/t5-tiny-random"      ,			framework="tf"					)
     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       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





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




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

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

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

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



  @require_torch
  def    lowerCAmelCase__					(       self    :    str					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   "patrickvonplaten/t5-tiny-random"
     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    lowerCAmelCase__					(       self    :    List[str]					)				->	int:




     """simple docstring"""
     snake_case_         =   "patrickvonplaten/t5-tiny-random"
     with pytest.warns(_lowerCAmelCase      ,			match=R".*translation_en_to_de.*"					):
        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    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	str:




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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE		:str											=       {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Any											=       [
       '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''CvtForImageClassification''',
       '''CvtModel''',
       '''CvtPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Any											=       [
       '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFCvtForImageClassification''',
       '''TFCvtModel''',
       '''TFCvtPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cvt import (
          CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CvtForImageClassification,
          CvtModel,
          CvtPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_cvt import (
          TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCvtForImageClassification,
          TFCvtModel,
          TFCvtPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE		:str											=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




