
'''simple docstring'''




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


lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)

lowercase			:				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  A				(							__snake_case						):
     __magic_name__						=		'''luke'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50267						,			SCREAMING_SNAKE_CASE=500000						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=1						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE=2						,			**SCREAMING_SNAKE_CASE						,			) ->				List[Any]:






         """simple docstring"""






         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE						,			bos_token_id=SCREAMING_SNAKE_CASE						,			eos_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

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









"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)