



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  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=7	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=99	,__lowerCamelCase=32	,__lowerCamelCase=5	,__lowerCamelCase=4	,__lowerCamelCase=37	,__lowerCamelCase="gelu"	,__lowerCamelCase=0.1	,__lowerCamelCase=0.1	,__lowerCamelCase=5_12	,__lowerCamelCase=16	,__lowerCamelCase=2	,__lowerCamelCase=0.02	,__lowerCamelCase=3	,__lowerCamelCase=4	,__lowerCamelCase=None	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  parent
						lowerCAmelCase__  :       str				=  batch_size
						lowerCAmelCase__  :       List[Any]				=  seq_length
						lowerCAmelCase__  :       str				=  is_training
						lowerCAmelCase__  :       Tuple				=  use_input_mask
						lowerCAmelCase__  :       List[Any]				=  use_token_type_ids
						lowerCAmelCase__  :       Tuple				=  use_labels
						lowerCAmelCase__  :       Dict				=  vocab_size
						lowerCAmelCase__  :       List[str]				=  hidden_size
						lowerCAmelCase__  :       Union[str, Any]				=  num_hidden_layers
						lowerCAmelCase__  :       Optional[Any]				=  num_attention_heads
						lowerCAmelCase__  :       Any				=  intermediate_size
						lowerCAmelCase__  :       Optional[int]				=  hidden_act
						lowerCAmelCase__  :       Dict				=  hidden_dropout_prob
						lowerCAmelCase__  :       Dict				=  attention_probs_dropout_prob
						lowerCAmelCase__  :       Optional[Any]				=  max_position_embeddings
						lowerCAmelCase__  :       List[str]				=  type_vocab_size
						lowerCAmelCase__  :       Optional[int]				=  type_sequence_label_size
						lowerCAmelCase__  :       Dict				=  initializer_range
						lowerCAmelCase__  :       Union[str, Any]				=  num_labels
						lowerCAmelCase__  :       List[str]				=  num_choices
						lowerCAmelCase__  :       Union[str, Any]				=  scope



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size				)

						lowerCAmelCase__  :       List[Any]				=  None
						if self.use_input_mask:
										lowerCAmelCase__  :       List[Any]				=  random_attention_mask([self.batch_size, self.seq_length]				)

						lowerCAmelCase__  :       Optional[Any]				=  None
						if self.use_token_type_ids:
										lowerCAmelCase__  :       List[Any]				=  ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size				)

						lowerCAmelCase__  :       Tuple				=  None
						lowerCAmelCase__  :       Optional[Any]				=  None
						lowerCAmelCase__  :       str				=  None
						if self.use_labels:
										lowerCAmelCase__  :       List[str]				=  ids_tensor([self.batch_size]	,self.type_sequence_label_size				)
										lowerCAmelCase__  :       Optional[int]				=  ids_tensor([self.batch_size, self.seq_length]	,self.num_labels				)
										lowerCAmelCase__  :       str				=  ids_tensor([self.batch_size]	,self.num_choices				)

						lowerCAmelCase__  :       Dict				=  self.get_config()

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



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""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=A__	,initializer_range=self.initializer_range	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  BioGptModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(A__	,attention_mask=A__				)
						lowerCAmelCase__  :       Optional[Any]				=  model(A__				)
						self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  BioGptForCausalLM(config=A__				)
						model.to(A__				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(A__	,attention_mask=A__	,token_type_ids=A__	,labels=A__				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,*__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  BioGptModel(config=A__				)
						model.to(A__				)
						model.eval()

						# create attention mask
						lowerCAmelCase__  :       str				=  torch.ones(input_ids.shape	,dtype=torch.long	,device=A__				)
						lowerCAmelCase__  :       str				=  self.seq_length // 2
						lowerCAmelCase__  :       Any				=  0

						# first forward pass
						lowerCAmelCase__  :       List[str]				=  model(A__	,attention_mask=A__				).to_tuple()

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

						# change a random masked slice from input_ids
						lowerCAmelCase__  :       str				=  ids_tensor((1,)	,A__				).item() + 1
						lowerCAmelCase__  :       List[str]				=  ids_tensor((self.batch_size, 1)	,config.vocab_size				).squeeze(-1				)
						lowerCAmelCase__  :       Dict				=  random_other_next_tokens

						# append to next input_ids and attn_mask
						lowerCAmelCase__  :       Tuple				=  torch.cat([input_ids, next_tokens]	,dim=-1				)
						lowerCAmelCase__  :       int				=  torch.cat(
						    [attn_mask, torch.ones((attn_mask.shape[0], 1)	,dtype=torch.long	,device=A__				)]	,dim=1	,)

						# get two different outputs
						lowerCAmelCase__  :       Any				=  model(A__	,attention_mask=A__				)["""last_hidden_state"""]
						lowerCAmelCase__  :       Optional[int]				=  model(A__	,past_key_values=A__	,attention_mask=A__				)["""last_hidden_state"""]

						# select random slice
						lowerCAmelCase__  :       Optional[Any]				=  ids_tensor((1,)	,output_from_past.shape[-1]				).item()
						lowerCAmelCase__  :       Optional[int]				=  output_from_no_past[:, -1, random_slice_idx].detach()
						lowerCAmelCase__  :       List[str]				=  output_from_past[:, 0, random_slice_idx].detach()

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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,*__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  BioGptModel(config=A__				).to(A__				).eval()

						lowerCAmelCase__  :       int				=  torch.ones(input_ids.shape	,dtype=torch.long	,device=A__				)

						# first forward pass
						lowerCAmelCase__  :       int				=  model(A__	,attention_mask=A__	,use_cache=A__				)

						lowerCAmelCase__  :       Union[str, Any]				=  outputs.to_tuple()

						# create hypothetical multiple next token and extent to next_input_ids
						lowerCAmelCase__  :       int				=  ids_tensor((self.batch_size, 3)	,config.vocab_size				)
						lowerCAmelCase__  :       List[str]				=  ids_tensor((self.batch_size, 3)	,2				)

						# append to next input_ids and
						lowerCAmelCase__  :       Optional[int]				=  torch.cat([input_ids, next_tokens]	,dim=-1				)
						lowerCAmelCase__  :       Union[str, Any]				=  torch.cat([attention_mask, next_attn_mask]	,dim=-1				)

						lowerCAmelCase__  :       str				=  model(A__	,attention_mask=A__				)["""last_hidden_state"""]
						lowerCAmelCase__  :       Optional[int]				=  model(A__	,attention_mask=A__	,past_key_values=A__				)[
						    """last_hidden_state"""
						]

						# select random slice
						lowerCAmelCase__  :       List[str]				=  ids_tensor((1,)	,output_from_past.shape[-1]				).item()
						lowerCAmelCase__  :       int				=  output_from_no_past[:, -3:, random_slice_idx].detach()
						lowerCAmelCase__  :       List[Any]				=  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(A__	,A__	,atol=1e-3				)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,*__lowerCamelCase	,__lowerCamelCase=False				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  BioGptForCausalLM(A__				)
						model.to(A__				)
						if gradient_checkpointing:
										model.gradient_checkpointing_enable()

						lowerCAmelCase__  :       str				=  model(A__	,labels=A__				)
						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 	lowerCAmelCase__   (self	,__lowerCamelCase	,*__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  BioGptModel(A__				)
						lowerCAmelCase__  :       str				=  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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,*__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.num_labels
						lowerCAmelCase__  :       str				=  BioGptForTokenClassification(A__				)
						model.to(A__				)
						model.eval()
						lowerCAmelCase__  :       Any				=  model(A__	,attention_mask=A__	,token_type_ids=A__				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.num_labels)				)



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.prepare_config_and_inputs()
						(
						    lowerCAmelCase__     
						)  :       List[str]				=  config_and_inputs
						lowerCAmelCase__  :       int				=  {"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		snake_case_			=(BioGptForCausalLM,) if is_torch_available() else ()
		snake_case_			=(
		    {
		        '''feature-extraction''': BioGptModel,
		        '''text-classification''': BioGptForSequenceClassification,
		        '''text-generation''': BioGptForCausalLM,
		        '''token-classification''': BioGptForTokenClassification,
		        '''zero-shot''': BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  BioGptModelTester(self				)
						lowerCAmelCase__  :       Optional[int]				=  ConfigTester(self	,config_class=A__	,hidden_size=37				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						self.config_tester.run_common_tests()



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase__  :       Optional[Any]				=  type
										self.model_tester.create_and_check_model(*A__				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A__				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*A__	,gradient_checkpointing=A__				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A__				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*A__				)



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*A__				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
						model.to(A__				)
						lowerCAmelCase__  :       Tuple				=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

						lowerCAmelCase__  :       Optional[Any]				=  """left"""

						# Define PAD Token = EOS Token = 50256
						lowerCAmelCase__  :       Dict				=  tokenizer.eos_token
						lowerCAmelCase__  :       Dict				=  model.config.eos_token_id

						# use different length sentences to test batching
						lowerCAmelCase__  :       Union[str, Any]				=  [
						    """Hello, my dog is a little""",
						    """Today, I""",
						]

						lowerCAmelCase__  :       int				=  tokenizer(A__	,return_tensors='''pt'''	,padding=A__				)
						lowerCAmelCase__  :       Union[str, Any]				=  inputs["""input_ids"""].to(A__				)

						lowerCAmelCase__  :       Optional[Any]				=  model.generate(
						    input_ids=A__	,attention_mask=inputs['''attention_mask'''].to(A__				)	,)

						lowerCAmelCase__  :       str				=  tokenizer(sentences[0]	,return_tensors='''pt'''				).input_ids.to(A__				)
						lowerCAmelCase__  :       Dict				=  model.generate(input_ids=A__				)

						lowerCAmelCase__  :       int				=  inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
						lowerCAmelCase__  :       int				=  tokenizer(sentences[1]	,return_tensors='''pt'''				).input_ids.to(A__				)
						lowerCAmelCase__  :       List[Any]				=  model.generate(input_ids=A__	,max_length=model.config.max_length - num_paddings				)

						lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.batch_decode(A__	,skip_special_tokens=A__				)
						lowerCAmelCase__  :       Any				=  tokenizer.decode(output_non_padded[0]	,skip_special_tokens=A__				)
						lowerCAmelCase__  :       int				=  tokenizer.decode(output_padded[0]	,skip_special_tokens=A__				)

						lowerCAmelCase__  :       Optional[Any]				=  [
						    """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(A__	,A__				)
						self.assertListEqual(A__	,[non_padded_sentence, padded_sentence]				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       int				=  BioGptModel.from_pretrained(A__				)
										self.assertIsNotNone(A__				)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__  :       str				=  3
						lowerCAmelCase__  :       Union[str, Any]				=  input_dict["""input_ids"""]
						lowerCAmelCase__  :       List[str]				=  input_ids.ne(1				).to(A__				)
						lowerCAmelCase__  :       List[Any]				=  ids_tensor([self.model_tester.batch_size]	,self.model_tester.type_sequence_label_size				)
						lowerCAmelCase__  :       str				=  BioGptForSequenceClassification(A__				)
						model.to(A__				)
						model.eval()
						lowerCAmelCase__  :       List[Any]				=  model(A__	,attention_mask=A__	,labels=A__				)
						self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__  :       Any				=  3
						lowerCAmelCase__  :       Tuple				=  """multi_label_classification"""
						lowerCAmelCase__  :       Tuple				=  input_dict["""input_ids"""]
						lowerCAmelCase__  :       Optional[int]				=  input_ids.ne(1				).to(A__				)
						lowerCAmelCase__  :       List[Any]				=  ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]	,self.model_tester.type_sequence_label_size				).to(torch.float				)
						lowerCAmelCase__  :       List[str]				=  BioGptForSequenceClassification(A__				)
						model.to(A__				)
						model.eval()
						lowerCAmelCase__  :       Any				=  model(A__	,attention_mask=A__	,labels=A__				)
						self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)				)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		@slow
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
						lowerCAmelCase__  :       List[str]				=  torch.tensor([[2, 48_05, 9, 6_56, 21]]				)
						lowerCAmelCase__  :       Union[str, Any]				=  model(A__				)[0]

						lowerCAmelCase__  :       List[str]				=  4_23_84

						lowerCAmelCase__  :       Tuple				=  torch.Size((1, 5, vocab_size)				)
						self.assertEqual(output.shape	,A__				)

						lowerCAmelCase__  :       List[str]				=  torch.tensor(
						    [[[-9.5236, -9.8918, 10.45_57], [-11.04_69, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]				)

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



		@slow
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)
						lowerCAmelCase__  :       int				=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
						model.to(A__				)

						torch.manual_seed(0				)
						lowerCAmelCase__  :       Dict				=  tokenizer('''COVID-19 is'''	,return_tensors='''pt'''				).to(A__				)
						lowerCAmelCase__  :       Any				=  model.generate(
						    **A__	,min_length=1_00	,max_length=10_24	,num_beams=5	,early_stopping=A__	,)
						lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.decode(output_ids[0]	,skip_special_tokens=A__				)

						lowerCAmelCase__  :       Union[str, Any]				=  (
						    """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(A__	,A__				)






import warnings

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


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

__snake_case       :	str            ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

						super().__init__(
						    pad_token_id=__lowerCamelCase	,bos_token_id=__lowerCamelCase	,eos_token_id=__lowerCamelCase	,is_encoder_decoder=__lowerCamelCase	,decoder_start_token_id=__lowerCamelCase	,forced_eos_token_id=__lowerCamelCase	,**__lowerCamelCase	,)

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  self.bos_token_id
										warnings.warn(
										    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''				)

