
"""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    _lowerCAmelCase						(						unittest.TestCase						):
       lowercase_     :     int       =							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       lowercase_     :     Dict       =							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



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



       def       _a   (      self ,						a_ ,						a_   )							->							Optional[int]:
        _UpperCAmelCase =						translator("Some string"   )
        self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}]   )

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

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



       @require_torch
       def       _a   (      self   )							->							Optional[Any]:
        _UpperCAmelCase =						pipeline("translation_en_to_ro" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="pt"   )
        _UpperCAmelCase =						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   )							->							str:
        _UpperCAmelCase =						pipeline("translation_en_to_ro" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="tf"   )
        _UpperCAmelCase =						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   )							->							int:
        _UpperCAmelCase =						pipeline("translation_en_to_de" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="pt"   )
        _UpperCAmelCase =						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   )							->							Union[str, Any]:
        _UpperCAmelCase =						pipeline("translation_en_to_de" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="tf"   )
        _UpperCAmelCase =						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    _lowerCAmelCase						(						unittest.TestCase						):



       @require_torch
       @slow
       def       _a   (      self   )							->							List[Any]:
        # 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
        _UpperCAmelCase =						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   )							->							int:
        _UpperCAmelCase =						MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"   )
        _UpperCAmelCase =						MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"   )

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

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

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



       @require_torch
       def       _a   (      self   )							->							Dict:
        _UpperCAmelCase =						"patrickvonplaten/t5-tiny-random"
        _UpperCAmelCase =						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   )							->							Tuple:
        _UpperCAmelCase =						"patrickvonplaten/t5-tiny-random"
        with pytest.warns(a_ ,						match=r".*translation_en_to_de.*"   ):
         _UpperCAmelCase =						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   )							->							int:
        with self.assertRaises(a_   ):
         pipeline(task="translation"   )






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__magic_name__		       =				{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__magic_name__		       =				['''CpmTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__magic_name__		       =				['''CpmTokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_cpm import CpmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_cpm_fast import CpmTokenizerFast

else:
			import sys

			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




