"""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	):
				A__      :   Optional[Any]  							=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				A__      :   int  							=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										if isinstance(model.config	, _lowerCamelCase      ):
																lowerCamelCase__					,      lowerCamelCase__					=list(tokenizer.lang_code_to_id.keys()      )[:2]
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang=_lowerCamelCase	, tgt_lang=_lowerCamelCase      )
										else:
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										return translator, ["Some string", "Some other text"]

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=translator("Some string"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string", "other string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}, {"translation_text": ANY(_lowerCamelCase      )}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=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    __UpperCAmelCase						(	unittest.TestCase	):

				@require_torch
				@slow
				def 		_a	(      self      ):
										# 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
										lowerCamelCase__					=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      ):
										lowerCamelCase__					=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )
										lowerCamelCase__					=MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )

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

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

										lowerCamelCase__					=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
										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										lowerCamelCase__					=translator("This is a test"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										with pytest.warns(_lowerCamelCase	, match=r".*translation_en_to_de.*"      ):
																lowerCamelCase__					=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 		_a	(      self      ):
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation"      )



def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					sequence.append(__lowerCamelCase						)

					__a							=   0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__lowerCamelCase						):
										return None

					return index


if __name__ == "__main__":
	lowerCamelCase_				:		List[str]       						=       input("""Enter numbers separated by comma:\n""").strip()
	lowerCamelCase_				:		str       						=       [int(item) for item in user_input.split(""",""")]

	lowerCamelCase_				:		Tuple       						=       input("""Enter a single number to be found in the list:\n""")
	lowerCamelCase_				:		Optional[int]       						=       int(target_input)
	lowerCamelCase_				:		List[Any]       						=       sentinel_linear_search(sequence, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print("""Not found""")




