





"""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      lowercase_				(     unittest.TestCase							):
       snake_case__				:							Optional[Any]											=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       snake_case__				:							List[str]											=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

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

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

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				pipeline("translation_en_to_ro"   ,					model="patrickvonplaten/t5-tiny-random"   ,					framework="pt"     )
        snake_case__    							=				translator("This is a test string"   ,					max_length=20     )
        self.assertEqual(
            __SCREAMING_SNAKE_CASE   ,					[
                {
                    "translation_text": (
                        "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                        " Beide Beide"
                    )
                }
            ]   ,					)

       @require_tf
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				pipeline("translation_en_to_ro"   ,					model="patrickvonplaten/t5-tiny-random"   ,					framework="tf"     )
        snake_case__    							=				translator("This is a test string"   ,					max_length=20     )
        self.assertEqual(
            __SCREAMING_SNAKE_CASE   ,					[
                {
                    "translation_text": (
                        "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                        " Beide Beide"
                    )
                }
            ]   ,					)

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				pipeline("translation_en_to_de"   ,					model="patrickvonplaten/t5-tiny-random"   ,					framework="pt"     )
        snake_case__    							=				translator("This is a test string"   ,					max_length=20     )
        self.assertEqual(
            __SCREAMING_SNAKE_CASE   ,					[
                {
                    "translation_text": (
                        "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                        " urine urine urine urine urine urine urine"
                    )
                }
            ]   ,					)

       @require_tf
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				pipeline("translation_en_to_de"   ,					model="patrickvonplaten/t5-tiny-random"   ,					framework="tf"     )
        snake_case__    							=				translator("This is a test string"   ,					max_length=20     )
        self.assertEqual(
            __SCREAMING_SNAKE_CASE   ,					[
                {
                    "translation_text": (
                        "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                        " urine urine urine urine urine urine urine"
                    )
                }
            ]   ,					)



class      lowercase_				(     unittest.TestCase							):

       @require_torch
       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

        # but we do for this one
        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )
        snake_case__    							=				MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )

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

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

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

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

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"patrickvonplaten/t5-tiny-random"
        snake_case__    							=				pipeline(task="translation_cn_to_ar"   ,					model=__SCREAMING_SNAKE_CASE     )
        self.assertEqual(translator._preprocess_params["src_lang"]   ,					"cn"     )
        self.assertEqual(translator._preprocess_params["tgt_lang"]   ,					"ar"     )

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"patrickvonplaten/t5-tiny-random"
        with pytest.warns(__SCREAMING_SNAKE_CASE   ,					match=R".*translation_en_to_de.*"     ):
         snake_case__    							=				pipeline(task="translation"   ,					model=__SCREAMING_SNAKE_CASE     )
        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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

