

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				):
   '''simple docstring'''







   lowerCamelCase__          =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   lowerCamelCase__          =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Union[str, Any] , a							:	int , a							:	Optional[int]		)							->    Dict:

    """simple docstring"""

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

    """simple docstring"""

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

    SCREAMING_SNAKE_CASE				: Dict			       =    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 							__UpperCamelCase	(     self							:	Tuple		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: str			       =    pipeline("translation_en_to_ro" , model="patrickvonplaten/t5-tiny-random" , framework="pt"		)
    SCREAMING_SNAKE_CASE				: List[str]			       =    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 							__UpperCamelCase	(     self							:	List[Any]		)							->    List[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Any			       =    pipeline("translation_en_to_ro" , model="patrickvonplaten/t5-tiny-random" , framework="tf"		)
    SCREAMING_SNAKE_CASE				: Dict			       =    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 							__UpperCamelCase	(     self							:	int		)							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: str			       =    pipeline("translation_en_to_de" , model="patrickvonplaten/t5-tiny-random" , framework="pt"		)
    SCREAMING_SNAKE_CASE				: Dict			       =    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 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    pipeline("translation_en_to_de" , 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": (
                    "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                    " urine urine urine urine urine urine urine"
                )
            }
        ] , )






class _UpperCamelCase							(							unittest.TestCase				):
   '''simple docstring'''







   @require_torch
   @slow
   def 							__UpperCamelCase	(     self							:	str		)							->    List[Any]:

    """simple docstring"""

    with self.assertRaises(a		):
     pipeline(task="translation_cn_to_ar"		)

    # but we do for this one
    SCREAMING_SNAKE_CASE				: 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 							__UpperCamelCase	(     self							:	int		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"		)
    SCREAMING_SNAKE_CASE				: List[str]			       =    MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"		)

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    pipeline(task="translation" , model=a , tokenizer=a		)
    # Missing src_lang, tgt_lang
    with self.assertRaises(a		):
     translator("This is a test"		)

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

    SCREAMING_SNAKE_CASE				: str			       =    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				: str			       =    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 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    "patrickvonplaten/t5-tiny-random"
    SCREAMING_SNAKE_CASE				: List[str]			       =    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 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Tuple:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    "patrickvonplaten/t5-tiny-random"
    with pytest.warns(a , match=R".*translation_en_to_de.*"		):
     SCREAMING_SNAKE_CASE				: str			       =    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 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Any:

    """simple docstring"""

    with self.assertRaises(a		):
     pipeline(task="translation"		)


from __future__ import annotations

from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Dict , a							:	Any , a							:	Any		)							->    None:  # noqa: A002

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    item
				SCREAMING_SNAKE_CASE				: List[Any]			       =    next






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int]		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Node | None			       =    None
				SCREAMING_SNAKE_CASE				: List[Any]			       =    0
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    Node(a , self.head		)
				self.size += 1
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Any:

				"""simple docstring"""

				if self.head is None:
					return None
				else:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.head.item
					SCREAMING_SNAKE_CASE				: Dict			       =    self.head.next
					self.size -= 1
					return item
			def 							__UpperCamelCase	(     self							:	Any		)							->    bool:

				"""simple docstring"""

				return self.head is None
			def __str__(     self							:	Union[str, Any]		)							->    str:

				"""simple docstring"""

				if self.is_empty():
					return ""
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.head
					SCREAMING_SNAKE_CASE				: str			       =    ""
					SCREAMING_SNAKE_CASE				: list[str]			       =    []
					while iterate:
						item_list.append(str(iterate.item		)		)
						SCREAMING_SNAKE_CASE				: str			       =    iterate.next

					SCREAMING_SNAKE_CASE				: List[str]			       =    " --> ".join(a		)

					return item_str






			def __len__(     self							:	List[Any]		)							->    int:

				"""simple docstring"""

				return self.size