


'''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 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    a									=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    a									=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           if isinstance(model.config   ,							SCREAMING_SNAKE_CASE):
                  _A						,				_A  :			str											=      list(tokenizer.lang_code_to_id.keys())[:2]
                  _A  :			Any											=      TranslationPipeline(model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE   ,							src_lang=SCREAMING_SNAKE_CASE   ,							tgt_lang=SCREAMING_SNAKE_CASE)
           else:
                  _A  :			Dict											=      TranslationPipeline(model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE)
           return translator, ["Some string", "Some other text"]


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple):
           _A  :			Optional[Any]											=      translator('Some string')
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'translation_text': ANY(SCREAMING_SNAKE_CASE)}])

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

           _A  :			Optional[int]											=      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    :							Optional[Any]):
           _A  :			List[Any]											=      pipeline('translation_en_to_ro'   ,							model='patrickvonplaten/t5-tiny-random'   ,							framework='pt')
           _A  :			int											=      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    :							List[str]):
           _A  :			Dict											=      pipeline('translation_en_to_ro'   ,							model='patrickvonplaten/t5-tiny-random'   ,							framework='tf')
           _A  :			List[str]											=      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    :							Dict):
           _A  :			str											=      pipeline('translation_en_to_de'   ,							model='patrickvonplaten/t5-tiny-random'   ,							framework='pt')
           _A  :			Tuple											=      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[Any]):
           _A  :			int											=      pipeline('translation_en_to_de'   ,							model='patrickvonplaten/t5-tiny-random'   ,							framework='tf')
           _A  :			Tuple											=      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 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    @require_torch
    @slow
    def      A					(						self    :							Optional[Any]):
           # 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
           _A  :			List[str]											=      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    :							List[Any]):
           _A  :			int											=      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt')
           _A  :			Tuple											=      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt')

           _A  :			int											=      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')

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

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


    @require_torch
    def      A					(						self    :							Optional[Any]):
           _A  :			Union[str, Any]											=      'patrickvonplaten/t5-tiny-random'
           _A  :			str											=      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    :							Dict):
           _A  :			List[str]											=      'patrickvonplaten/t5-tiny-random'
           with pytest.warns(SCREAMING_SNAKE_CASE   ,							match=R'.*translation_en_to_de.*'):
                  _A  :			int											=      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    :							int):
           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  pipeline(task='translation')










'''simple docstring'''







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))






