


'''simple docstring'''



import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    BioGptForCausalLM,
	    BioGptForSequenceClassification,
	    BioGptForTokenClassification,
	    BioGptModel,
	    BioGptTokenizer,
	)
	from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST






class 	UpperCamelCase__:




				def __init__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			List[str]=13				,   lowerCAmelCase      :			Optional[int]=7				,   lowerCAmelCase      :			Optional[int]=True				,   lowerCAmelCase      :			Optional[Any]=True				,   lowerCAmelCase      :			Any=False				,   lowerCAmelCase      :			Dict=True				,   lowerCAmelCase      :			Union[str, Any]=99				,   lowerCAmelCase      :			Optional[int]=32				,   lowerCAmelCase      :			Any=5				,   lowerCAmelCase      :			List[Any]=4				,   lowerCAmelCase      :			Optional[int]=37				,   lowerCAmelCase      :			str="gelu"				,   lowerCAmelCase      :			Any=0.1				,   lowerCAmelCase      :			List[str]=0.1				,   lowerCAmelCase      :			int=512				,   lowerCAmelCase      :			Union[str, Any]=16				,   lowerCAmelCase      :			int=2				,   lowerCAmelCase      :			Union[str, Any]=0.02				,   lowerCAmelCase      :			Optional[int]=3				,   lowerCAmelCase      :			Tuple=4				,   lowerCAmelCase      :			Any=None				,   )->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     seq_length
										UpperCAmelCase					    =     is_training
										UpperCAmelCase					    =     use_input_mask
										UpperCAmelCase					    =     use_token_type_ids
										UpperCAmelCase					    =     use_labels
										UpperCAmelCase					    =     vocab_size
										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					    =     max_position_embeddings
										UpperCAmelCase					    =     type_vocab_size
										UpperCAmelCase					    =     type_sequence_label_size
										UpperCAmelCase					    =     initializer_range
										UpperCAmelCase					    =     num_labels
										UpperCAmelCase					    =     num_choices
										UpperCAmelCase					    =     scope




				def 					a__(		self      :			Any				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size				)

										UpperCAmelCase					    =     None
										if self.use_input_mask:
																UpperCAmelCase					    =     random_attention_mask([self.batch_size, self.seq_length]				)

										UpperCAmelCase					    =     None
										if self.use_token_type_ids:
																UpperCAmelCase					    =     ids_tensor([self.batch_size, self.seq_length]				,   self.type_vocab_size				)

										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     None
										if self.use_labels:
																UpperCAmelCase					    =     ids_tensor([self.batch_size]				,   self.type_sequence_label_size				)
																UpperCAmelCase					    =     ids_tensor([self.batch_size, self.seq_length]				,   self.num_labels				)
																UpperCAmelCase					    =     ids_tensor([self.batch_size]				,   self.num_choices				)

										UpperCAmelCase					    =     self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def 					a__(		self      :			Optional[int]				)->					Dict:

										"""simple docstring"""




										return BioGptConfig(
										    vocab_size=self.vocab_size				,   hidden_size=self.hidden_size				,   num_hidden_layers=self.num_hidden_layers				,   num_attention_heads=self.num_attention_heads				,   intermediate_size=self.intermediate_size				,   hidden_act=self.hidden_act				,   hidden_dropout_prob=self.hidden_dropout_prob				,   attention_probs_dropout_prob=self.attention_probs_dropout_prob				,   max_position_embeddings=self.max_position_embeddings				,   type_vocab_size=self.type_vocab_size				,   is_decoder=__snake_case				,   initializer_range=self.initializer_range				,   )




				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			List[Any]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptModel(config=__snake_case				)
										model.to(__snake_case				)
										model.eval()
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				)
										UpperCAmelCase					    =     model(__snake_case				)
										self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptForCausalLM(config=__snake_case				)
										model.to(__snake_case				)
										model.eval()
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				,   token_type_ids=__snake_case				,   labels=__snake_case				)
										self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[Any]				,   *lowerCAmelCase      :			str				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptModel(config=__snake_case				)
										model.to(__snake_case				)
										model.eval()

										# create attention mask
										UpperCAmelCase					    =     torch.ones(input_ids.shape				,   dtype=torch.long				,   device=__snake_case				)
										UpperCAmelCase					    =     self.seq_length // 2
										UpperCAmelCase					    =     0

										# first forward pass
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				).to_tuple()

										# create hypothetical next token and extent to next_input_ids
										UpperCAmelCase					    =     ids_tensor((self.batch_size, 1)				,   config.vocab_size				)

										# change a random masked slice from input_ids
										UpperCAmelCase					    =     ids_tensor((1,)				,   __snake_case				).item() + 1
										UpperCAmelCase					    =     ids_tensor((self.batch_size, 1)				,   config.vocab_size				).squeeze(-1				)
										UpperCAmelCase					    =     random_other_next_tokens

										# append to next input_ids and attn_mask
										UpperCAmelCase					    =     torch.cat([input_ids, next_tokens]				,   dim=-1				)
										UpperCAmelCase					    =     torch.cat(
										    [attn_mask, torch.ones((attn_mask.shape[0], 1)				,   dtype=torch.long				,   device=__snake_case				)]				,   dim=1				,   )

										# get two different outputs
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				)['last_hidden_state']
										UpperCAmelCase					    =     model(__snake_case				,   past_key_values=__snake_case				,   attention_mask=__snake_case				)['last_hidden_state']

										# select random slice
										UpperCAmelCase					    =     ids_tensor((1,)				,   output_from_past.shape[-1]				).item()
										UpperCAmelCase					    =     output_from_no_past[:, -1, random_slice_idx].detach()
										UpperCAmelCase					    =     output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(__snake_case				,   __snake_case				,   atol=1E-3				)				)




				def 					a__(		self      :			int				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Any				,   *lowerCAmelCase      :			Tuple				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptModel(config=__snake_case				).to(__snake_case				).eval()

										UpperCAmelCase					    =     torch.ones(input_ids.shape				,   dtype=torch.long				,   device=__snake_case				)

										# first forward pass
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				,   use_cache=__snake_case				)

										UpperCAmelCase					    =     outputs.to_tuple()

										# create hypothetical multiple next token and extent to next_input_ids
										UpperCAmelCase					    =     ids_tensor((self.batch_size, 3)				,   config.vocab_size				)
										UpperCAmelCase					    =     ids_tensor((self.batch_size, 3)				,   2				)

										# append to next input_ids and
										UpperCAmelCase					    =     torch.cat([input_ids, next_tokens]				,   dim=-1				)
										UpperCAmelCase					    =     torch.cat([attention_mask, next_attn_mask]				,   dim=-1				)

										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				)['last_hidden_state']
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				,   past_key_values=__snake_case				)[
										    'last_hidden_state'
										]

										# select random slice
										UpperCAmelCase					    =     ids_tensor((1,)				,   output_from_past.shape[-1]				).item()
										UpperCAmelCase					    =     output_from_no_past[:, -3:, random_slice_idx].detach()
										UpperCAmelCase					    =     output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(__snake_case				,   __snake_case				,   atol=1E-3				)				)




				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Dict				,   *lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[int]=False				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptForCausalLM(__snake_case				)
										model.to(__snake_case				)
										if gradient_checkpointing:
																model.gradient_checkpointing_enable()

										UpperCAmelCase					    =     model(__snake_case				,   labels=__snake_case				)
										self.parent.assertEqual(result.loss.shape				,   ()				)
										self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)				)
										result.loss.backward()




				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Dict				,   *lowerCAmelCase      :			Optional[int]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptModel(__snake_case				)
										UpperCAmelCase					    =     model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers				)
										for key in model.state_dict().keys():
																if "c_proj" in key and "weight" in key:
																						self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]				) - model_std				)				,   0.001				)
																						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]				) - 0.0				)				,   0.01				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Any				,   *lowerCAmelCase      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.num_labels
										UpperCAmelCase					    =     BioGptForTokenClassification(__snake_case				)
										model.to(__snake_case				)
										model.eval()
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				,   token_type_ids=__snake_case				)
										self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)				)





				def 					a__(		self      :			Tuple				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.prepare_config_and_inputs()
										(
										    UpperCAmelCase     
										)					    =     config_and_inputs
										UpperCAmelCase					    =     {'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict







@require_torch
class 	UpperCamelCase__(       lowerCamelCase__						,    lowerCamelCase__						,    lowerCamelCase__						,    unittest.TestCase	):
				__magic_name__    :				Tuple  		=	(
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				__magic_name__    :				Union[str, Any]  		=	(BioGptForCausalLM,) if is_torch_available() else ()
				__magic_name__    :				Optional[Any]  		=	(
				    {
				        "feature-extraction": BioGptModel,
				        "text-classification": BioGptForSequenceClassification,
				        "text-generation": BioGptForCausalLM,
				        "token-classification": BioGptForTokenClassification,
				        "zero-shot": BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			Optional[Any]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=__snake_case				,   hidden_size=37				)




				def 					a__(		self      :			str				)->					Any:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				def 					a__(		self      :			Tuple				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case				)




				def 					a__(		self      :			int				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																UpperCAmelCase					    =     type
																self.model_tester.create_and_check_model(*__snake_case				)




				def 					a__(		self      :			Any				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__snake_case				)




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_forward_and_backwards(*__snake_case				,   gradient_checkpointing=__snake_case				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__snake_case				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*__snake_case				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*__snake_case				)




				@slow
				def 					a__(		self      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
										model.to(__snake_case				)
										UpperCAmelCase					    =     BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

										UpperCAmelCase					    =     'left'

										# Define PAD Token = EOS Token = 50256
										UpperCAmelCase					    =     tokenizer.eos_token
										UpperCAmelCase					    =     model.config.eos_token_id

										# use different length sentences to test batching
										UpperCAmelCase					    =     [
										    'Hello, my dog is a little',
										    'Today, I',
										]

										UpperCAmelCase					    =     tokenizer(__snake_case				,   return_tensors='''pt'''				,   padding=__snake_case				)
										UpperCAmelCase					    =     inputs['input_ids'].to(__snake_case				)

										UpperCAmelCase					    =     model.generate(
										    input_ids=__snake_case				,   attention_mask=inputs['''attention_mask'''].to(__snake_case				)				,   )

										UpperCAmelCase					    =     tokenizer(sentences[0]				,   return_tensors='''pt'''				).input_ids.to(__snake_case				)
										UpperCAmelCase					    =     model.generate(input_ids=__snake_case				)

										UpperCAmelCase					    =     inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
										UpperCAmelCase					    =     tokenizer(sentences[1]				,   return_tensors='''pt'''				).input_ids.to(__snake_case				)
										UpperCAmelCase					    =     model.generate(input_ids=__snake_case				,   max_length=model.config.max_length - num_paddings				)

										UpperCAmelCase					    =     tokenizer.batch_decode(__snake_case				,   skip_special_tokens=__snake_case				)
										UpperCAmelCase					    =     tokenizer.decode(output_non_padded[0]				,   skip_special_tokens=__snake_case				)
										UpperCAmelCase					    =     tokenizer.decode(output_padded[0]				,   skip_special_tokens=__snake_case				)

										UpperCAmelCase					    =     [
										    'Hello, my dog is a little bit bigger than a little bit.',
										    'Today, I have a good idea of how to use the information',
										]
										self.assertListEqual(__snake_case				,   __snake_case				)
										self.assertListEqual(__snake_case				,   [non_padded_sentence, padded_sentence]				)




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

										"""simple docstring"""




										for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase					    =     BioGptModel.from_pretrained(__snake_case				)
																self.assertIsNotNone(__snake_case				)




				def 					a__(		self      :			Dict				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     3
										UpperCAmelCase					    =     input_dict['input_ids']
										UpperCAmelCase					    =     input_ids.ne(1				).to(__snake_case				)
										UpperCAmelCase					    =     ids_tensor([self.model_tester.batch_size]				,   self.model_tester.type_sequence_label_size				)
										UpperCAmelCase					    =     BioGptForSequenceClassification(__snake_case				)
										model.to(__snake_case				)
										model.eval()
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				,   labels=__snake_case				)
										self.assertEqual(result.logits.shape				,   (self.model_tester.batch_size, self.model_tester.num_labels)				)





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     3
										UpperCAmelCase					    =     'multi_label_classification'
										UpperCAmelCase					    =     input_dict['input_ids']
										UpperCAmelCase					    =     input_ids.ne(1				).to(__snake_case				)
										UpperCAmelCase					    =     ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]				,   self.model_tester.type_sequence_label_size				).to(torch.float				)
										UpperCAmelCase					    =     BioGptForSequenceClassification(__snake_case				)
										model.to(__snake_case				)
										model.eval()
										UpperCAmelCase					    =     model(__snake_case				,   attention_mask=__snake_case				,   labels=__snake_case				)
										self.assertEqual(result.logits.shape				,   (self.model_tester.batch_size, self.model_tester.num_labels)				)







@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				@slow
				def 					a__(		self      :			Dict				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
										UpperCAmelCase					    =     torch.tensor([[2, 4805, 9, 656, 21]]				)
										UpperCAmelCase					    =     model(__snake_case				)[0]

										UpperCAmelCase					    =     42384

										UpperCAmelCase					    =     torch.Size((1, 5, vocab_size)				)
										self.assertEqual(output.shape				,   __snake_case				)

										UpperCAmelCase					    =     torch.tensor(
										    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]				)

										self.assertTrue(torch.allclose(output[:, :3, :3]				,   __snake_case				,   atol=1E-4				)				)





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

										"""simple docstring"""




										UpperCAmelCase					    =     BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)
										UpperCAmelCase					    =     BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
										model.to(__snake_case				)

										torch.manual_seed(0				)
										UpperCAmelCase					    =     tokenizer('''COVID-19 is'''				,   return_tensors='''pt'''				).to(__snake_case				)
										UpperCAmelCase					    =     model.generate(
										    **__snake_case				,   min_length=100				,   max_length=1024				,   num_beams=5				,   early_stopping=__snake_case				,   )
										UpperCAmelCase					    =     tokenizer.decode(output_ids[0]				,   skip_special_tokens=__snake_case				)

										UpperCAmelCase					    =     (
										    'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
										    ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
										    ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
										    ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
										    ' more than 800,000 deaths.'
										)
										self.assertEqual(__snake_case				,   __snake_case				)









'''simple docstring'''



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


_lowercase      :      int            =					logging.get_logger(__name__)

_lowercase      :      Optional[Any]            =					{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Union[str, Any]  		=	"openai-gpt"
				__magic_name__    :				Optional[int]  		=	{
				    "max_position_embeddings": "n_positions",
				    "hidden_size": "n_embd",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}




				def __init__(		self      :			Optional[int]				,   lowerCAmelCase      :			Optional[Any]=40478				,   lowerCAmelCase      :			str=512				,   lowerCAmelCase      :			List[Any]=768				,   lowerCAmelCase      :			Tuple=12				,   lowerCAmelCase      :			int=12				,   lowerCAmelCase      :			List[str]="gelu"				,   lowerCAmelCase      :			List[str]=0.1				,   lowerCAmelCase      :			List[Any]=0.1				,   lowerCAmelCase      :			Dict=0.1				,   lowerCAmelCase      :			Tuple=1E-5				,   lowerCAmelCase      :			Tuple=0.02				,   lowerCAmelCase      :			Optional[int]="cls_index"				,   lowerCAmelCase      :			Dict=True				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Dict=True				,   lowerCAmelCase      :			int=0.1				,   **lowerCAmelCase      :			Optional[int]				,   )->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     vocab_size
										UpperCAmelCase					    =     n_positions
										UpperCAmelCase					    =     n_embd
										UpperCAmelCase					    =     n_layer
										UpperCAmelCase					    =     n_head
										UpperCAmelCase					    =     afn
										UpperCAmelCase					    =     resid_pdrop
										UpperCAmelCase					    =     embd_pdrop
										UpperCAmelCase					    =     attn_pdrop
										UpperCAmelCase					    =     layer_norm_epsilon
										UpperCAmelCase					    =     initializer_range
										UpperCAmelCase					    =     summary_type
										UpperCAmelCase					    =     summary_use_proj
										UpperCAmelCase					    =     summary_activation
										UpperCAmelCase					    =     summary_first_dropout
										UpperCAmelCase					    =     summary_proj_to_labels
										super().__init__(**lowerCAmelCase				)





