

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
     import torch


@require_torch
@require_optimum
@slow
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'hf-internal-testing/tiny-random-t5'
        A__   				=       AutoTokenizer.from_pretrained(lowercase_		)
        A__   				=       AutoModelForSeqaSeqLM.from_pretrained(lowercase_		)

        A__   				=       tokenizer('This is me',return_tensors='pt'		)

        A__   				=       model.to_bettertransformer()

        self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()		)		)

        A__   				=       model.generate(**lowercase_		)

        A__   				=       model.reverse_bettertransformer()

        self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()		)		)

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(lowercase_		)

            A__   				=       AutoModelForSeqaSeqLM.from_pretrained(lowercase_		)

            self.assertFalse(
                any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()		)		)

            A__   				=       model_reloaded.generate(**lowercase_		)
            self.assertTrue(torch.allclose(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Union[str, Any]		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'hf-internal-testing/tiny-random-t5'
        A__   				=       AutoModelForSeqaSeqLM.from_pretrained(lowercase_		)

        A__   				=       model.to_bettertransformer()

        with tempfile.TemporaryDirectory() as tmpdirname:
            with self.assertRaises(lowercase_		):
                model.save_pretrained(lowercase_		)

            A__   				=       model.reverse_bettertransformer()
            model.save_pretrained(lowercase_		)




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

