
from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel





def __A						(   a_ :							Dict		,		a_ :							int		,		a_ :							int=None		,		a_ :							str=None     )->							Optional[int]:




		'''simple docstring'''






		if attention_mask is None:
				SCREAMING_SNAKE_CASE       :     str				= tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE		,		config.pad_token_id     )		,		tf.inta     )
		return {"input_ids": input_ids, "attention_mask": attention_mask}




@require_tf
class 				lowercase__:
	'''simple docstring'''

	UpperCamelCase				=  OPTConfig
	UpperCamelCase				=  {}
	UpperCamelCase				=  """gelu"""





	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :List[Any]=13		, lowerCamelCase_    :int=7		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :Union[str, Any]=False		, lowerCamelCase_    :str=99		, lowerCamelCase_    :int=16		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Tuple=4		, lowerCamelCase_    :List[str]=4		, lowerCamelCase_    :Optional[Any]="gelu"		, lowerCamelCase_    :List[str]=0.1		, lowerCamelCase_    :Optional[Any]=0.1		, lowerCamelCase_    :Tuple=20		, lowerCamelCase_    :Dict=2		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :Optional[Any]=0		, lowerCamelCase_    :Tuple=16		, lowerCamelCase_    :Optional[Any]=16		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= parent
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= seq_length
			SCREAMING_SNAKE_CASE       :     List[str]				= is_training
			SCREAMING_SNAKE_CASE       :     List[str]				= use_labels
			SCREAMING_SNAKE_CASE       :     str				= vocab_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[int]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_act
			SCREAMING_SNAKE_CASE       :     List[Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     List[Any]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     str				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= eos_token_id
			SCREAMING_SNAKE_CASE       :     Tuple				= pad_token_id
			SCREAMING_SNAKE_CASE       :     Tuple				= bos_token_id
			SCREAMING_SNAKE_CASE       :     List[str]				= embed_dim
			SCREAMING_SNAKE_CASE       :     int				= word_embed_proj_dim
			SCREAMING_SNAKE_CASE       :     List[Any]				= False





	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)		, 1					)
			SCREAMING_SNAKE_CASE       :     Any				= tf.concat([input_ids, eos_tensor]		, axis=1					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= self.config_cls(
			    vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, ffn_dim=self.intermediate_size		, dropout=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, eos_token_id=self.eos_token_id		, bos_token_id=self.bos_token_id		, pad_token_id=self.pad_token_id		, embed_dim=self.embed_dim		, word_embed_proj_dim=self.word_embed_proj_dim		, is_encoder_decoder=lowercase_		, **self.config_updates		, )
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= prepare_opt_inputs_dict(lowercase_		, lowercase_					)
			return config, inputs_dict







	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= TFOPTModel(config=lowercase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= inputs_dict["input_ids"]

			SCREAMING_SNAKE_CASE       :     Any				= input_ids[:1, :]
			SCREAMING_SNAKE_CASE       :     int				= inputs_dict["attention_mask"][:1, :]
			SCREAMING_SNAKE_CASE       :     List[Any]				= 1

			# first forward pass
			SCREAMING_SNAKE_CASE       :     Dict				= model(lowercase_		, attention_mask=lowercase_		, use_cache=lowercase_					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ids_tensor((self.batch_size, 3)		, config.vocab_size					)
			SCREAMING_SNAKE_CASE       :     str				= tf.cast(ids_tensor((self.batch_size, 3)		, 2					)		, tf.inta					)

			# append to next input_ids and
			SCREAMING_SNAKE_CASE       :     int				= tf.concat([input_ids, next_tokens]		, axis=-1					)
			SCREAMING_SNAKE_CASE       :     List[str]				= tf.concat([attention_mask, next_attn_mask]		, axis=-1					)

			SCREAMING_SNAKE_CASE       :     Dict				= model(lowercase_		, attention_mask=lowercase_					)[0]
			SCREAMING_SNAKE_CASE       :     str				= model(lowercase_		, attention_mask=lowercase_		, past_key_values=lowercase_					)[0]

			self.parent.assertEqual(next_tokens.shape[1]		, output_from_past.shape[1]					)

			# select random slice
			SCREAMING_SNAKE_CASE       :     Optional[int]				= int(ids_tensor((1,)		, output_from_past.shape[-1]					)					)
			SCREAMING_SNAKE_CASE       :     int				= output_from_no_past[:, -3:, random_slice_idx]
			SCREAMING_SNAKE_CASE       :     List[Any]				= output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(lowercase_		, lowercase_		, rtol=1E-3					)





@require_tf
class 				lowercase__(				snake_case__		,						snake_case__		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	UpperCamelCase				=  (TFOPTForCausalLM,) if is_tf_available() else ()
	UpperCamelCase				=  (
	    {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
	)
	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  10





	def   __lowerCAmelCase (						self    :str					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= TFOPTModelTester(self					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= ConfigTester(self		, config_class=lowercase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			self.config_tester.run_common_tests()





	def   __lowerCAmelCase (						self    :List[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*lowercase_					)







	def   __lowerCAmelCase (						self    :List[Any]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs_for_common()

			def _get_word_embedding_weight(lowerCamelCase_    :str		, lowerCamelCase_    :List[Any]					):
					if hasattr(lowercase_		, '''weight'''					):
							return embedding_layer.weight
					else:
							# Here we build the word embeddings weights if not exists.
							# And then we retry to get the attribute once built.
							model.build()
							if hasattr(lowercase_		, '''weight'''					):
									return embedding_layer.weight
							else:
									return None

			for model_class in self.all_model_classes:
					for size in [config.vocab_size - 10, config.vocab_size + 10]:
							# build the embeddings
							SCREAMING_SNAKE_CASE       :     List[str]				= model_class(config=lowercase_					)
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= _get_word_embedding_weight(lowercase_		, model.get_input_embeddings()					)
							SCREAMING_SNAKE_CASE       :     Any				= _get_word_embedding_weight(lowercase_		, model.get_output_embeddings()					)

							# reshape the embeddings
							model.resize_token_embeddings(lowercase_					)
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= _get_word_embedding_weight(lowercase_		, model.get_input_embeddings()					)
							SCREAMING_SNAKE_CASE       :     Tuple				= _get_word_embedding_weight(lowercase_		, model.get_output_embeddings()					)

							# check that the resized embeddings size matches the desired size.
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= size if size is not None else config.vocab_size

							self.assertEqual(new_input_embeddings.shape[0]		, lowercase_					)

							# check that weights remain the same after resizing
							SCREAMING_SNAKE_CASE       :     Any				= True
							for pa, pa in zip(old_input_embeddings.value()		, new_input_embeddings.value()					):
									if tf.math.reduce_sum(tf.math.abs(pa - pa					)					) > 0:
											SCREAMING_SNAKE_CASE       :     Optional[int]				= False
							self.assertTrue(lowercase_					)

							if old_output_embeddings is not None and new_output_embeddings is not None:
									self.assertEqual(new_output_embeddings.shape[0]		, lowercase_					)

									SCREAMING_SNAKE_CASE       :     List[Any]				= True
									for pa, pa in zip(old_output_embeddings.value()		, new_output_embeddings.value()					):
											if tf.math.reduce_sum(tf.math.abs(pa - pa					)					) > 0:
													SCREAMING_SNAKE_CASE       :     Any				= False
									self.assertTrue(lowercase_					)





def __A						(   a_ :							Any     )->							Optional[Any]:




		'''simple docstring'''






		return tf.constant(__SCREAMING_SNAKE_CASE		,		dtype=tf.intaa     )




@require_tf
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  99





	def   __lowerCAmelCase (						self    :Any					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.ones((4, 1)		, dtype=tf.intaa					) * 2
			SCREAMING_SNAKE_CASE       :     List[Any]				= tf.concat([ids_tensor((4, 6)		, self.vocab_size - 3					) + 3, eos_column_vector]		, axis=1					)
			SCREAMING_SNAKE_CASE       :     Tuple				= input_ids.shape[0]
			SCREAMING_SNAKE_CASE       :     List[str]				= OPTConfig(
			    vocab_size=self.vocab_size		, hidden_size=24		, num_hidden_layers=2		, num_attention_heads=2		, ffn_dim=32		, max_position_embeddings=48		, eos_token_id=2		, pad_token_id=1		, bos_token_id=0		, )
			return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= TFOPTModel.from_pretrained('''facebook/opt-350m'''					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= _long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]					)
			SCREAMING_SNAKE_CASE       :     int				= tf.not_equal(lowercase_		, model.config.pad_token_id					)
			with tf.GradientTape():
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(input_ids=lowercase_		, attention_mask=lowercase_					).last_hidden_state
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (1, 11, 5_12)
			self.assertEqual(output.shape		, lowercase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.constant(
			    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]					)
			self.assertTrue(np.allclose(output[:, :3, :3]		, lowercase_		, atol=4E-3					)					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.function(lowercase_		, jit_compile=lowercase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= xla_generate(lowercase_		, lowercase_					)[0]
			self.assertTrue(np.allclose(output[:, :3, :3]		, lowercase_		, atol=4E-2					)					)





@require_tf
@slow
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''simple docstring'''




			super().setUp()
			SCREAMING_SNAKE_CASE       :     Tuple				= "facebook/opt-350m"







	def   __lowerCAmelCase (						self    :List[str]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= TFOPTForCausalLM.from_pretrained(self.path_model					)
			SCREAMING_SNAKE_CASE       :     str				= GPTaTokenizer.from_pretrained(self.path_model					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [
			    "Today is a beautiful day and I want to",
			    "In the city of",
			    "Paris is the capital of France and",
			    "Computers and mobile phones have taken",
			]
			# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer(lowercase_		, return_tensors='''tf'''		, padding=lowercase_		, add_special_tokens=lowercase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= tf.math.reduce_mean(model(inputs.input_ids		, attention_mask=inputs.attention_mask					)[0]		, axis=-1					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= tf.constant(
			    [
			        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
			        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
			        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
			        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
			    ]					)
			self.assertTrue(np.allclose(lowercase_		, lowercase_		, atol=1E-4					)					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.function(lowercase_		, jit_compile=lowercase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= tf.math.reduce_mean(xla_generate(inputs.input_ids		, attention_mask=inputs.attention_mask					)[0]		, axis=-1					)
			self.assertTrue(np.allclose(lowercase_		, lowercase_		, atol=1E-4					)					)





@require_tf
@slow
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			return [
			    "Today is a beautiful day and I want",
			    "In the city of",
			    "Paris is the capital of France and",
			    "Computers and mobile phones have taken",
			]





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= "facebook/opt-125m"

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [
			    "Today is a beautiful day and I want to",
			    "In the city of New York, the city",
			    "Paris is the capital of France and the capital",
			    "Computers and mobile phones have taken over the",
			]

			SCREAMING_SNAKE_CASE       :     Dict				= []
			SCREAMING_SNAKE_CASE       :     int				= GPTaTokenizer.from_pretrained(lowercase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= TFOPTForCausalLM.from_pretrained(lowercase_					)

			for prompt in self.prompts:
					SCREAMING_SNAKE_CASE       :     str				= tokenizer(lowercase_		, return_tensors='''tf'''					).input_ids

					SCREAMING_SNAKE_CASE       :     Optional[int]				= model.generate(lowercase_		, max_length=10					)

					SCREAMING_SNAKE_CASE       :     int				= tokenizer.batch_decode(lowercase_		, skip_special_tokens=lowercase_					)
					predicted_outputs += generated_string

			self.assertListEqual(lowercase_		, lowercase_					)





	def   __lowerCAmelCase (						self    :int					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= "facebook/opt-350m"

			SCREAMING_SNAKE_CASE       :     Tuple				= GPTaTokenizer.from_pretrained(lowercase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= TFOPTForCausalLM.from_pretrained(lowercase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= "left"

			# use different length sentences to test batching
			SCREAMING_SNAKE_CASE       :     Tuple				= [
			    "Hello, my dog is a little",
			    "Today, I",
			]

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer(lowercase_		, return_tensors='''tf'''		, padding=lowercase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= inputs["input_ids"]

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model.generate(input_ids=lowercase_		, attention_mask=inputs['''attention_mask''']					)

			SCREAMING_SNAKE_CASE       :     List[str]				= tokenizer(sentences[0]		, return_tensors='''tf'''					).input_ids
			SCREAMING_SNAKE_CASE       :     Any				= model.generate(input_ids=lowercase_					)

			SCREAMING_SNAKE_CASE       :     int				= inputs_non_padded.shape[-1] - tf.math.reduce_sum(
			    tf.cast(inputs['''attention_mask'''][-1]		, tf.intaa					)					)
			SCREAMING_SNAKE_CASE       :     Dict				= tokenizer(sentences[1]		, return_tensors='''tf'''					).input_ids
			SCREAMING_SNAKE_CASE       :     Any				= model.generate(input_ids=lowercase_		, max_length=model.config.max_length - num_paddings					)

			SCREAMING_SNAKE_CASE       :     str				= tokenizer.batch_decode(lowercase_		, skip_special_tokens=lowercase_					)
			SCREAMING_SNAKE_CASE       :     str				= tokenizer.decode(output_non_padded[0]		, skip_special_tokens=lowercase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer.decode(output_padded[0]		, skip_special_tokens=lowercase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [
			    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
			    "Today, I was in the middle of a conversation with a friend about the",
			]
			self.assertListEqual(lowercase_		, lowercase_					)
			self.assertListEqual(lowercase_		, [non_padded_sentence, padded_sentence]					)







	def   __lowerCAmelCase (						self    :int					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= "facebook/opt-350m"

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [
			    "Today is a beautiful day and I want to",
			    "In the city of San Francisco, the city",
			    "Paris is the capital of France and the capital",
			    "Computers and mobile phones have taken over the",
			]

			SCREAMING_SNAKE_CASE       :     Any				= []
			SCREAMING_SNAKE_CASE       :     Dict				= GPTaTokenizer.from_pretrained(lowercase_					)
			SCREAMING_SNAKE_CASE       :     int				= TFOPTForCausalLM.from_pretrained(lowercase_					)

			for prompt in self.prompts:
					SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer(lowercase_		, return_tensors='''tf'''					).input_ids

					SCREAMING_SNAKE_CASE       :     List[str]				= model.generate(lowercase_		, max_length=10					)

					SCREAMING_SNAKE_CASE       :     Dict				= tokenizer.batch_decode(lowercase_		, skip_special_tokens=lowercase_					)
					predicted_outputs += generated_string

			self.assertListEqual(lowercase_		, lowercase_					)







"""simple docstring"""






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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """luke"""





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :Union[str, Any]=5_02_67		, lowerCamelCase_    :int=50_00_00		, lowerCamelCase_    :Tuple=7_68		, lowerCamelCase_    :List[str]=2_56		, lowerCamelCase_    :Dict=12		, lowerCamelCase_    :Optional[int]=12		, lowerCamelCase_    :Optional[Any]=30_72		, lowerCamelCase_    :List[Any]="gelu"		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :List[str]=5_12		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Tuple=0.0_2		, lowerCamelCase_    :Optional[int]=1E-12		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Any=0		, lowerCamelCase_    :str=2		, **lowerCamelCase_    :List[Any]		, )							->			Optional[int]:







			'''simple docstring'''




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

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





