





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  _a  (					unittest.TestCase				):






							"""simple docstring"""

							snake_case_							=    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							snake_case_							=    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def   A_	(   self					:  Tuple				,						a					:  Any				,						a					:  Dict				,						a					:  str     )						->Optional[Any]:
													if isinstance(model.config				,						a     ):
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       list(tokenizer.lang_code_to_id.keys()     )[:2]
																			SCREAMING_SNAKE_CASE__  :					Dict =       TranslationPipeline(model=a				,						tokenizer=a				,						src_lang=a				,						tgt_lang=a     )
													else:
																			SCREAMING_SNAKE_CASE__  :					int =       TranslationPipeline(model=a				,						tokenizer=a     )
													return translator, ["Some string", "Some other text"]

							def   A_	(   self					:  Union[str, Any]				,						a					:  List[str]				,						a					:  Optional[Any]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					List[str] =       translator("Some string"     )
													self.assertEqual(a				,						[{"translation_text": ANY(a     )}]     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       translator(["Some string"]     )
													self.assertEqual(a				,						[{"translation_text": ANY(a     )}]     )

													SCREAMING_SNAKE_CASE__  :					List[str] =       translator(["Some string", "other string"]     )
													self.assertEqual(a				,						[{"translation_text": ANY(a     )}, {"translation_text": ANY(a     )}]     )

							@require_torch
							def   A_	(   self					:  Any     )						->str:
													SCREAMING_SNAKE_CASE__  :					str =       pipeline("translation_en_to_ro"				,						model="patrickvonplaten/t5-tiny-random"				,						framework="pt"     )
													SCREAMING_SNAKE_CASE__  :					int =       translator("This is a test string"				,						max_length=20     )
													self.assertEqual(
													    a				,						[
													        {
													            "translation_text": (
													                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
													                " Beide Beide"
													            )
													        }
													    ]				,						)

							@require_tf
							def   A_	(   self					:  Optional[int]     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       pipeline("translation_en_to_ro"				,						model="patrickvonplaten/t5-tiny-random"				,						framework="tf"     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       translator("This is a test string"				,						max_length=20     )
													self.assertEqual(
													    a				,						[
													        {
													            "translation_text": (
													                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
													                " Beide Beide"
													            )
													        }
													    ]				,						)

							@require_torch
							def   A_	(   self					:  Optional[int]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       pipeline("translation_en_to_de"				,						model="patrickvonplaten/t5-tiny-random"				,						framework="pt"     )
													SCREAMING_SNAKE_CASE__  :					int =       translator("This is a test string"				,						max_length=20     )
													self.assertEqual(
													    a				,						[
													        {
													            "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]     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					int =       pipeline("translation_en_to_de"				,						model="patrickvonplaten/t5-tiny-random"				,						framework="tf"     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       translator("This is a test string"				,						max_length=20     )
													self.assertEqual(
													    a				,						[
													        {
													            "translation_text": (
													                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
													                " urine urine urine urine urine urine urine"
													            )
													        }
													    ]				,						)







class  _a  (					unittest.TestCase				):






							"""simple docstring"""

							@require_torch
							@slow
							def   A_	(   self					:  Tuple     )						->Tuple:
													# We don't provide a default for this pair
													with self.assertRaises(a     ):
																			pipeline(task="translation_cn_to_ar"     )

													# but we do for this one
													SCREAMING_SNAKE_CASE__  :					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   A_	(   self					:  List[Any]     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Any =       MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )

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

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

													SCREAMING_SNAKE_CASE__  :					Tuple =       translator("This is a test"				,						src_lang="en_XX"				,						tgt_lang="hi_IN"     )
													self.assertEqual(a				,						[{"translation_text": "यह एक परीक्षण है"}]     )

													# src_lang, tgt_lang can be defined at pipeline call time
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       pipeline(task="translation"				,						model=a				,						tokenizer=a				,						src_lang="en_XX"				,						tgt_lang="ar_AR"     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       translator("This is a test"     )
													self.assertEqual(a				,						[{"translation_text": "هذا إختبار"}]     )

							@require_torch
							def   A_	(   self					:  Optional[int]     )						->Optional[Any]:
													SCREAMING_SNAKE_CASE__  :					Any =       "patrickvonplaten/t5-tiny-random"
													SCREAMING_SNAKE_CASE__  :					List[Any] =       pipeline(task="translation_cn_to_ar"				,						model=a     )
													self.assertEqual(translator._preprocess_params["src_lang"]				,						"cn"     )
													self.assertEqual(translator._preprocess_params["tgt_lang"]				,						"ar"     )

							@require_torch
							def   A_	(   self					:  Dict     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       "patrickvonplaten/t5-tiny-random"
													with pytest.warns(a				,						match=r".*translation_en_to_de.*"     ):
																			SCREAMING_SNAKE_CASE__  :					Dict =       pipeline(task="translation"				,						model=a     )
													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					:  List[str]     )						->Tuple:
													with self.assertRaises(a     ):
																			pipeline(task="translation"     )






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class  _a  :






							"""simple docstring"""

							snake_case_							=    ("_front", "_back", "_len")



							@dataclass
							class  _a  :






														"""simple docstring"""

														snake_case_							=    None
														snake_case_							=    None
														snake_case_							=    None







							class  _a  :






														"""simple docstring"""

														snake_case_							=    ("_cur",)

														def __init__(   self					:  Tuple				,						a					:  Deque._Node | None     )						->None:
																				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       cur

														def __iter__(   self					:  Dict     )						->Deque._Iterator:
																				return self






														def   A_	(   self					:  int     )						->Any:
																				if self._cur is None:
																										# finished iterating
																										raise StopIteration
																				SCREAMING_SNAKE_CASE__  :					Tuple =       self._cur.val
																				SCREAMING_SNAKE_CASE__  :					int =       self._cur.next_node

																				return val







							def __init__(   self					:  str				,						a					:  Iterable[Any] | None = None     )						->None:
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					int =       0

													if iterable is not None:
																			# append every value to the deque
																			for val in iterable:
																									self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					int =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       node
																			SCREAMING_SNAKE_CASE__  :					Any =       self._back
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node  # assign new back to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  List[str]				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					str =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Any =       self._front
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node
																			SCREAMING_SNAKE_CASE__  :					int =       node  # assign new front to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  Dict				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.appendleft(a     )

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._back
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._back.prev_node  # set new back
													# drop the last node - python will deallocate memory automatically
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					Any =       self._front
													SCREAMING_SNAKE_CASE__  :					Dict =       self._front.next_node  # set new front and drop the first node
													SCREAMING_SNAKE_CASE__  :					Dict =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  str     )						->bool:
													return self._front is None

							def __len__(   self					:  List[str]     )						->int:
													return self._len

							def __eq__(   self					:  List[Any]				,						a					:  object     )						->bool:

													if not isinstance(a				,						a     ):
																			return NotImplemented

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._front
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       other._front

													# if the length of the dequeues are not the same, they are not equal
													if len(self     ) != len(a     ):
																			return False

													while me is not None and oth is not None:
																			# compare every value
																			if me.val != oth.val:
																									return False
																			SCREAMING_SNAKE_CASE__  :					Any =       me.next_node
																			SCREAMING_SNAKE_CASE__  :					Any =       oth.next_node

													return True

							def __iter__(   self					:  Any     )						->Deque._Iterator:
													return Deque._Iterator(self._front     )






							def __repr__(   self					:  Any     )						->str:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
													SCREAMING_SNAKE_CASE__  :					int =       self._front
													while aux is not None:
																			# append the values in a list to display
																			values_list.append(aux.val     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       aux.next_node

													return f"""[{', '.join(repr(a     ) for val in values_list     )}]"""


if __name__ == "__main__":
				import doctest

				doctest.testmod()