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    ):



 '''simple docstring'''
 _lowerCamelCase    :						Any					     =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 _lowerCamelCase    :						Union[str, Any]					     =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

 def        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		Union[str, Any]	)			->				Any:
        _UpperCamelCase  :							Any	    =	translator('Some string'	)
        self.assertEqual(lowerCamelCase							,      [{'translation_text': ANY(lowerCamelCase	)}]	)

        _UpperCamelCase  :							Union[str, Any]	    =	translator(['Some string']	)
        self.assertEqual(lowerCamelCase							,      [{'translation_text': ANY(lowerCamelCase	)}]	)

        _UpperCamelCase  :							Dict	    =	translator(['Some string', 'other string']	)
        self.assertEqual(lowerCamelCase							,      [{'translation_text': ANY(lowerCamelCase	)}, {'translation_text': ANY(lowerCamelCase	)}]	)

 @require_torch
 def        __snake_case       (      self							:		List[Any]	)			->				str:
        _UpperCamelCase  :							str	    =	pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'	)
        _UpperCamelCase  :							Any	    =	translator('This is a test string'							,      max_length=2_0	)
        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        __snake_case       (      self							:		Optional[Any]	)			->				Tuple:
        _UpperCamelCase  :							List[str]	    =	pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'	)
        _UpperCamelCase  :							List[Any]	    =	translator('This is a test string'							,      max_length=2_0	)
        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        __snake_case       (      self							:		List[Any]	)			->				Any:
        _UpperCamelCase  :							Union[str, Any]	    =	pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'	)
        _UpperCamelCase  :							Optional[Any]	    =	translator('This is a test string'							,      max_length=2_0	)
        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        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        _UpperCamelCase  :							Dict	    =	pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'	)
        _UpperCamelCase  :							str	    =	translator('This is a test string'							,      max_length=2_0	)
        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  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 @require_torch
 @slow
 def        __snake_case       (      self							:		Dict	)			->				List[Any]:
        # 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
        _UpperCamelCase  :							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        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        _UpperCamelCase  :							int	    =	MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'	)
        _UpperCamelCase  :							Dict	    =	MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'	)

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

        _UpperCamelCase  :							Tuple	    =	translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='ar_AR'	)
        self.assertEqual(lowerCamelCase							,      [{'translation_text': 'هذا إختبار'}]	)

        _UpperCamelCase  :							Tuple	    =	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
        _UpperCamelCase  :							List[str]	    =	pipeline(task='translation'							,      model=lowerCamelCase							,      tokenizer=lowerCamelCase							,      src_lang='en_XX'							,      tgt_lang='ar_AR'	)
        _UpperCamelCase  :							Any	    =	translator('This is a test'	)
        self.assertEqual(lowerCamelCase							,      [{'translation_text': 'هذا إختبار'}]	)

 @require_torch
 def        __snake_case       (      self							:		int	)			->				Optional[Any]:
        _UpperCamelCase  :							str	    =	'patrickvonplaten/t5-tiny-random'
        _UpperCamelCase  :							Union[str, Any]	    =	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        __snake_case       (      self							:		str	)			->				List[Any]:
        _UpperCamelCase  :							Dict	    =	'patrickvonplaten/t5-tiny-random'
        with pytest.warns(lowerCamelCase							,      match=r'.*translation_en_to_de.*'	):
               _UpperCamelCase  :							Optional[Any]	    =	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        __snake_case       (      self							:		str	)			->				Optional[Any]:
        with self.assertRaises(lowerCamelCase	):
               pipeline(task='translation'	)








from collections import Counter


def 						UpperCAmelCase__   (   UpperCAmelCase__			:list[int]       ):






 '''simple docstring'''

 return sum(socks_by_color // 2 for socks_by_color in Counter(UpperCAmelCase__       ).values()       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  List[str]										=  [int(x) for x in input('''Enter socks by color :''').rstrip().split()]
     print(F"""sock_merchant({colors}) = {sock_merchant(colors)}""")

