



"""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"		)





"""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
