





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      ):


	'''simple docstring'''





	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      Union[str, Any]      =		'hf-internal-testing/tiny-random-t5'
			lowercase__					:      Union[str, Any]      =		AutoTokenizer.from_pretrained(lowerCamelCase_			)
			lowercase__					:      List[str]      =		AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		tokenizer('This is me',					return_tensors='pt'			)

			lowercase__					:      Optional[int]      =		model.to_bettertransformer()

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

			lowercase__					:      Optional[int]      =		model.generate(**lowerCamelCase_			)

			lowercase__					:      Tuple      =		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_			)

					lowercase__					:      Dict      =		AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			)

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

					lowercase__					:      Dict      =		model_reloaded.generate(**lowerCamelCase_			)
					self.assertTrue(torch.allclose(lowerCamelCase_,					lowerCamelCase_			)			)





	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		'hf-internal-testing/tiny-random-t5'
			lowercase__					:      Optional[int]      =		AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			)

			lowercase__					:      List[str]      =		model.to_bettertransformer()

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

					lowercase__					:      Optional[Any]      =		model.reverse_bettertransformer()
					model.save_pretrained(lowerCamelCase_			)









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


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


