
'''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    ):
       __lowerCAmelCase    :				Dict    =      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       __lowerCAmelCase    :				Union[str, Any]    =      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




       def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_  ):
         if isinstance(model.config   ,   snake_case_  ):
           _lowerCAmelCase				,					_lowerCAmelCase       :  Any   				=					list(tokenizer.lang_code_to_id.keys()  )[:2]
           _lowerCAmelCase       :  Tuple   				=					TranslationPipeline(model=snake_case_   ,   tokenizer=snake_case_   ,   src_lang=snake_case_   ,   tgt_lang=snake_case_  )
         else:
           _lowerCAmelCase       :  List[Any]   				=					TranslationPipeline(model=snake_case_   ,   tokenizer=snake_case_  )
         return translator, ["Some string", "Some other text"]




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

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

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




       @require_torch
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  Tuple   				=					pipeline("""translation_en_to_ro"""   ,   model="""patrickvonplaten/t5-tiny-random"""   ,   framework="""pt"""  )
         _lowerCAmelCase       :  Any   				=					translator("""This is a test string"""   ,   max_length=2_0  )
         self.assertEqual(
             snake_case_   ,   [
                 {
                     """translation_text""": (
                         """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                         """ Beide Beide"""
                     )
                 }
             ]   ,   )




       @require_tf
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  Tuple   				=					pipeline("""translation_en_to_ro"""   ,   model="""patrickvonplaten/t5-tiny-random"""   ,   framework="""tf"""  )
         _lowerCAmelCase       :  List[str]   				=					translator("""This is a test string"""   ,   max_length=2_0  )
         self.assertEqual(
             snake_case_   ,   [
                 {
                     """translation_text""": (
                         """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                         """ Beide Beide"""
                     )
                 }
             ]   ,   )




       @require_torch
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  List[Any]   				=					pipeline("""translation_en_to_de"""   ,   model="""patrickvonplaten/t5-tiny-random"""   ,   framework="""pt"""  )
         _lowerCAmelCase       :  Union[str, Any]   				=					translator("""This is a test string"""   ,   max_length=2_0  )
         self.assertEqual(
             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 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  int   				=					pipeline("""translation_en_to_de"""   ,   model="""patrickvonplaten/t5-tiny-random"""   ,   framework="""tf"""  )
         _lowerCAmelCase       :  List[Any]   				=					translator("""This is a test string"""   ,   max_length=2_0  )
         self.assertEqual(
             snake_case_   ,   [
                 {
                     """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    ):




       @require_torch
       @slow
       def 							__UpperCamelCase       (     self  ):
         # We don't provide a default for this pair
         with self.assertRaises(snake_case_  ):
           pipeline(task="""translation_cn_to_ar"""  )

         # but we do for this one
         _lowerCAmelCase       :  Any   				=					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  ):
         _lowerCAmelCase       :  List[Any]   				=					MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""  )
         _lowerCAmelCase       :  Union[str, Any]   				=					MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""  )

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

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

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

         # src_lang, tgt_lang can be defined at pipeline call time
         _lowerCAmelCase       :  List[str]   				=					pipeline(task="""translation"""   ,   model=snake_case_   ,   tokenizer=snake_case_   ,   src_lang="""en_XX"""   ,   tgt_lang="""ar_AR"""  )
         _lowerCAmelCase       :  List[str]   				=					translator("""This is a test"""  )
         self.assertEqual(snake_case_   ,   [{"""translation_text""": """هذا إختبار"""}]  )




       @require_torch
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  List[str]   				=					"""patrickvonplaten/t5-tiny-random"""
         _lowerCAmelCase       :  str   				=					pipeline(task="""translation_cn_to_ar"""   ,   model=snake_case_  )
         self.assertEqual(translator._preprocess_params["""src_lang"""]   ,   """cn"""  )
         self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,   """ar"""  )




       @require_torch
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  List[str]   				=					"""patrickvonplaten/t5-tiny-random"""
         with pytest.warns(snake_case_   ,   match=r""".*translation_en_to_de.*"""  ):
           _lowerCAmelCase       :  Dict   				=					pipeline(task="""translation"""   ,   model=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 							__UpperCamelCase       (     self  ):
         with self.assertRaises(snake_case_  ):
           pipeline(task="""translation"""  )




'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase_     			=       {
    """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_     			=       ["""DeformableDetrFeatureExtractor"""]
     UpperCamelCase_     			=       ["""DeformableDetrImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_     			=       [
         """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DeformableDetrForObjectDetection""",
         """DeformableDetrModel""",
         """DeformableDetrPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
          from .image_processing_deformable_detr import DeformableDetrImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deformable_detr import (
              DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DeformableDetrForObjectDetection,
              DeformableDetrModel,
              DeformableDetrPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


