

"""simple docstring"""






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


_lowerCAmelCase       						=		logging.get_logger(__name__)

_lowerCAmelCase       						=		{
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}







class 						__UpperCamelCase     (     __snake_case    ):
  _UpperCAmelCase        =		"audio-spectrogram-transformer"

  def __init__(		self     ,_A=768     ,_A=12     ,_A=12     ,_A=3072     ,_A="gelu"     ,_A=0.0     ,_A=0.0     ,_A=0.0_2     ,_A=1E-12     ,_A=16     ,_A=True     ,_A=10     ,_A=10     ,_A=1024     ,_A=128     ,**_A     ,):
      '''simple docstring'''

      super().__init__(**__lowerCamelCase   )

      _lowerCAmelCase							: List[Any]	=     hidden_size
      _lowerCAmelCase							: Union[str, Any]	=     num_hidden_layers
      _lowerCAmelCase							: int	=     num_attention_heads
      _lowerCAmelCase							: List[Any]	=     intermediate_size
      _lowerCAmelCase							: Any	=     hidden_act
      _lowerCAmelCase							: int	=     hidden_dropout_prob
      _lowerCAmelCase							: Optional[Any]	=     attention_probs_dropout_prob
      _lowerCAmelCase							: List[Any]	=     initializer_range
      _lowerCAmelCase							: Union[str, Any]	=     layer_norm_eps
      _lowerCAmelCase							: Dict	=     patch_size
      _lowerCAmelCase							: Any	=     qkv_bias
      _lowerCAmelCase							: List[Any]	=     frequency_stride
      _lowerCAmelCase							: str	=     time_stride
      _lowerCAmelCase							: List[str]	=     max_length
      _lowerCAmelCase							: List[Any]	=     num_mel_bins








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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

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

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   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(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

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

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

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

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)