
"""simple docstring"""

import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_A            = set()
_A            = get_logger(__name__)
def 		UpperCAmelCase					(		a_ = None			):





		'''simple docstring'''


		def decorator(a_			):
				global _emitted_deprecation_warnings

				if inspect.isclass(a_			):
						lowerCamelCase     :			List[str]		=    deprecated_class_or_function.__init__
						lowerCamelCase     :			Dict		=    deprecated_class_or_function.__name__
				else:
						lowerCamelCase     :			Optional[Any]		=    deprecated_class_or_function
						lowerCamelCase     :			int		=    deprecated_function.__name__
						# Support deprecating __init__ class method: class name instead
						lowerCamelCase     :			Any		=    name if name != '__init__' else deprecated_function.__qualname__.split('.'			)[-2]

				lowerCamelCase     :			Optional[Any]		=    (
				    F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
				    if help_message
				    else ''
				)

				@wraps(a_			)
				def wrapper(*a_,						**a_			):
						lowerCamelCase     :			Optional[Any]		=    hash(a_			)
						if func_hash not in _emitted_deprecation_warnings:
								warnings.warn(a_,						category=a_,						stacklevel=2			)
								_emitted_deprecation_warnings.add(a_			)
						return deprecated_function(*a_,						**a_			)

				lowerCamelCase     :			int		=    'deprecated'

				if inspect.isclass(a_			):
						lowerCamelCase     :			str		=    wrapper
						return deprecated_class_or_function
				else:
						return wrapper

		return decorator






class        _lowercase   (				enum.EnumMeta		):




	def __getattribute__( cls		, UpperCAmelCase_					)		->							Any:
			lowerCamelCase     :			int		=    super().__getattribute__(UpperCAmelCase_					)
			if isinstance(UpperCAmelCase_		, enum.Enum					) and obj._on_access:
					obj._on_access()
			return obj




	def __getitem__( cls		, UpperCAmelCase_					)		->							List[str]:
			lowerCamelCase     :			List[Any]		=    super().__getitem__(UpperCAmelCase_					)
			if member._on_access:
					member._on_access()
			return member




	def __call__( cls		, UpperCAmelCase_		, UpperCAmelCase_=None		, *, UpperCAmelCase_=None		, UpperCAmelCase_=None		, UpperCAmelCase_=None		, UpperCAmelCase_=1					)		->							int:
			lowerCamelCase     :			Optional[int]		=    super().__call__(UpperCAmelCase_		, UpperCAmelCase_		, module=UpperCAmelCase_		, qualname=UpperCAmelCase_		, type=UpperCAmelCase_		, start=UpperCAmelCase_					)
			if isinstance(UpperCAmelCase_		, enum.Enum					) and obj._on_access:
					obj._on_access()
			return obj






class        _lowercase   (				enum.Enum				,       metaclass=__UpperCAmelCase		):




	def __new__( cls		, UpperCAmelCase_					)		->							List[Any]:
			lowerCamelCase     :			str		=    object.__new__(cls					)
			lowerCamelCase     :			Union[str, Any]		=    value
			lowerCamelCase     :			Optional[Any]		=    member.deprecate
			return member




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			return ""




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Union[str, Any]		=    F""" {self.help_message}""" if self.help_message else ''
			warnings.warn(
			    F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
			    + help_message		, UpperCAmelCase_		, stacklevel=3		, )









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	):
      lowerCamelCase_	:      Tuple          =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      lowerCamelCase_	:      int          =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        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							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        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   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        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   )						->       Optional[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        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   )						->       Union[str, Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        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   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        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	):







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # 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   )						->       int:
        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   )						->       int:
        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]:
        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   )						->       Any:
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation"""   )