





'''simple docstring'''

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


lowercase__		:     Union[str, Any]			=							logging.get_logger(__name__)

lowercase__		:     Dict			=							{
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'vit_mae'





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]=768			,					lowerCAmelCase__  :						Optional[int]=12			,					lowerCAmelCase__  :						List[Any]=12			,					lowerCAmelCase__  :						Dict=3072			,					lowerCAmelCase__  :						Optional[int]="gelu"			,					lowerCAmelCase__  :						Tuple=0.0			,					lowerCAmelCase__  :						List[str]=0.0			,					lowerCAmelCase__  :						List[Any]=0.02			,					lowerCAmelCase__  :						List[str]=1e-1_2			,					lowerCAmelCase__  :						Union[str, Any]=224			,					lowerCAmelCase__  :						Dict=16			,					lowerCAmelCase__  :						Optional[Any]=3			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						str=16			,					lowerCAmelCase__  :						Union[str, Any]=512			,					lowerCAmelCase__  :						Optional[Any]=8			,					lowerCAmelCase__  :						Optional[Any]=2048			,					lowerCAmelCase__  :						Optional[int]=0.75			,					lowerCAmelCase__  :						List[str]=False			,					**lowerCAmelCase__  :						int			,					)       ->	Dict:

            '''simple docstring'''




            super().__init__(**lowerCAmelCase__	)

            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			initializer_range
            _UpperCamelCase							       =			layer_norm_eps
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			patch_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			qkv_bias
            _UpperCamelCase							       =			decoder_num_attention_heads
            _UpperCamelCase							       =			decoder_hidden_size
            _UpperCamelCase							       =			decoder_num_hidden_layers
            _UpperCamelCase							       =			decoder_intermediate_size
            _UpperCamelCase							       =			mask_ratio
            _UpperCamelCase							       =			norm_pix_loss





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     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






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

				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(**lowercase			)

				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(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

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

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







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

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