



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

UpperCamelCase__:     List[str]										=    {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """beit"""







	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  Optional[Any]=8192	,    __snake_case    :  Optional[Any]=768	,    __snake_case    :  str=12	,    __snake_case    :  str=12	,    __snake_case    :  Optional[Any]=3072	,    __snake_case    :  Union[str, Any]="gelu"	,    __snake_case    :  Union[str, Any]=0.0	,    __snake_case    :  List[str]=0.0	,    __snake_case    :  str=0.02	,    __snake_case    :  Any=1E-12	,    __snake_case    :  Union[str, Any]=224	,    __snake_case    :  Dict=16	,    __snake_case    :  str=3	,    __snake_case    :  Any=False	,    __snake_case    :  List[str]=False	,    __snake_case    :  str=False	,    __snake_case    :  Optional[Any]=False	,    __snake_case    :  Any=0.1	,    __snake_case    :  List[Any]=0.1	,    __snake_case    :  Union[str, Any]=True	,    __snake_case    :  int=[3, 5, 7, 11]	,    __snake_case    :  Optional[Any]=[1, 2, 3, 6]	,    __snake_case    :  int=True	,    __snake_case    :  Tuple=0.4	,    __snake_case    :  Union[str, Any]=256	,    __snake_case    :  Optional[int]=1	,    __snake_case    :  str=False	,    __snake_case    :  Tuple=255	,    **__snake_case    :  Tuple	,    )    ->	Optional[int]:
								super().__init__(**__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	vocab_size
								UpperCAmelCase				: Union[str, Any]											=	hidden_size
								UpperCAmelCase				: Any											=	num_hidden_layers
								UpperCAmelCase				: Optional[Any]											=	num_attention_heads
								UpperCAmelCase				: Any											=	intermediate_size
								UpperCAmelCase				: Any											=	hidden_act
								UpperCAmelCase				: Union[str, Any]											=	hidden_dropout_prob
								UpperCAmelCase				: Optional[int]											=	attention_probs_dropout_prob
								UpperCAmelCase				: List[Any]											=	initializer_range
								UpperCAmelCase				: Tuple											=	layer_norm_eps

								UpperCAmelCase				: str											=	image_size
								UpperCAmelCase				: Tuple											=	patch_size
								UpperCAmelCase				: Tuple											=	num_channels
								UpperCAmelCase				: Tuple											=	use_mask_token
								UpperCAmelCase				: Any											=	use_absolute_position_embeddings
								UpperCAmelCase				: List[Any]											=	use_relative_position_bias
								UpperCAmelCase				: Union[str, Any]											=	use_shared_relative_position_bias
								UpperCAmelCase				: Optional[Any]											=	layer_scale_init_value
								UpperCAmelCase				: Tuple											=	drop_path_rate
								UpperCAmelCase				: Any											=	use_mean_pooling
								# decode head attributes (semantic segmentation)
								UpperCAmelCase				: str											=	out_indices
								UpperCAmelCase				: int											=	pool_scales
								# auxiliary head attributes (semantic segmentation)
								UpperCAmelCase				: Optional[Any]											=	use_auxiliary_head
								UpperCAmelCase				: List[Any]											=	auxiliary_loss_weight
								UpperCAmelCase				: List[Any]											=	auxiliary_channels
								UpperCAmelCase				: int											=	auxiliary_num_convs
								UpperCAmelCase				: int											=	auxiliary_concat_input
								UpperCAmelCase				: str											=	semantic_loss_ignore_index

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= version.parse("""1.11"""    )







	@property
	def   A			(		self    :  int	)    ->	Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]	)







	@property
	def   A			(		self    :  List[str]	)    ->	float:
								return 1E-4










'''simple docstring'''






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   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

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

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	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(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

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

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

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

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





