code: 
  
  
  
  """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 			UpperCAmelCase_						(       unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
        def 		snake_case_      (  self  :			List[Any]  ,			A  :			Tuple  ,			A  :			Optional[Any]  ,			A  :			Union[str, Any]		):
  
  
  
             if isinstance(model.config  ,			lowerCamelCase__		):
                  _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					list(tokenizer.lang_code_to_id.keys()		)[:2]
                  _UpperCAmelCase    :			Any              =					TranslationPipeline(model=lowerCamelCase__  ,			tokenizer=lowerCamelCase__  ,			src_lang=lowerCamelCase__  ,			tgt_lang=lowerCamelCase__		)
             else:
                  _UpperCAmelCase    :			str              =					TranslationPipeline(model=lowerCamelCase__  ,			tokenizer=lowerCamelCase__		)
             return translator, ["Some string", "Some other text"]
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			str  ,			A  :			Dict		):
             _UpperCAmelCase    :			int              =					translator("Some string"		)
             self.assertEqual(lowerCamelCase__  ,			[{"translation_text": ANY(lowerCamelCase__		)}]		)
  
             _UpperCAmelCase    :			Any              =					translator(["Some string"]		)
             self.assertEqual(lowerCamelCase__  ,			[{"translation_text": ANY(lowerCamelCase__		)}]		)
  
             _UpperCAmelCase    :			Union[str, Any]              =					translator(["Some string", "other string"]		)
             self.assertEqual(lowerCamelCase__  ,			[{"translation_text": ANY(lowerCamelCase__		)}, {"translation_text": ANY(lowerCamelCase__		)}]		)
  
  
  
        @require_torch
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			Optional[int]              =					pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"		)
             _UpperCAmelCase    :			List[str]              =					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 		snake_case_      (  self  :			List[str]		):
             _UpperCAmelCase    :			List[Any]              =					pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"		)
             _UpperCAmelCase    :			int              =					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 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			List[Any]              =					pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"		)
             _UpperCAmelCase    :			Tuple              =					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 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Dict              =					pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"		)
             _UpperCAmelCase    :			int              =					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 			UpperCAmelCase_						(       unittest.TestCase		):
  
  
  
        @require_torch
        @slow
        def 		snake_case_      (  self  :			Optional[int]		):
  
  
  
             with self.assertRaises(lowerCamelCase__		):
                  pipeline(task="translation_cn_to_ar"		)
  
             # but we do for this one
             _UpperCAmelCase    :			List[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 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Any              =					MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"		)
             _UpperCAmelCase    :			int              =					MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"		)
  
             _UpperCAmelCase    :			Tuple              =					pipeline(task="translation"  ,			model=lowerCamelCase__  ,			tokenizer=lowerCamelCase__		)
             # Missing src_lang, tgt_lang
             with self.assertRaises(lowerCamelCase__		):
                  translator("This is a test"		)
  
             _UpperCAmelCase    :			Optional[Any]              =					translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"		)
             self.assertEqual(lowerCamelCase__  ,			[{"translation_text": "هذا إختبار"}]		)
  
             _UpperCAmelCase    :			List[str]              =					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
             _UpperCAmelCase    :			Any              =					pipeline(task="translation"  ,			model=lowerCamelCase__  ,			tokenizer=lowerCamelCase__  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"		)
             _UpperCAmelCase    :			Optional[int]              =					translator("This is a test"		)
             self.assertEqual(lowerCamelCase__  ,			[{"translation_text": "هذا إختبار"}]		)
  
  
  
        @require_torch
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			str              =					"patrickvonplaten/t5-tiny-random"
             _UpperCAmelCase    :			List[Any]              =					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 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Dict              =					"patrickvonplaten/t5-tiny-random"
             with pytest.warns(lowerCamelCase__  ,			match=R".*translation_en_to_de.*"		):
                  _UpperCAmelCase    :			List[Any]              =					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 		snake_case_      (  self  :			Optional[Any]		):
  
  
  
             with self.assertRaises(lowerCamelCase__		):
                  pipeline(task="translation"		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import sys
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if not numbers:
            return 0
       if target == 0 and target in numbers:
            return 0
       if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
           isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
            raise ValueError("numbers must be an iterable of integers"       )
  
       _UpperCAmelCase    :			List[Any]              =					0
       _UpperCAmelCase    :			str              =					sys.maxsize
  
       while right < len(SCREAMING_SNAKE_CASE__       ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                 _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
                 curr_sum -= numbers[left]
                 left += 1
            right += 1
  
       return 0 if min_len == sys.maxsize else min_len
  