
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      :




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: Tuple							,A					: str=13							,A					: List[str]=7							,A					: Dict=True							,A					: Optional[Any]=True							,A					: Union[str, Any]=False							,A					: Optional[int]=True							,A					: int=99							,A					: Any=32							,A					: int=5							,A					: Tuple=4							,A					: Optional[Any]=37							,A					: Dict="gelu"							,A					: int=0.1							,A					: Optional[Any]=0.1							,A					: int=5_12							,A					: Tuple=16							,A					: Any=2							,A					: int=0.02							,A					: Optional[int]=3							,A					: str=4							,A					: Tuple=None							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= seq_length
					__A   			= is_training
					__A   			= use_input_mask
					__A   			= use_token_type_ids
					__A   			= use_labels
					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= intermediate_size
					__A   			= hidden_act
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= type_vocab_size
					__A   			= type_sequence_label_size
					__A   			= initializer_range
					__A   			= num_labels
					__A   			= num_choices
					__A   			= scope






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size )

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

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

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

					__A   			= self.get_config()

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






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					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 				UpperCamelCase_  (       self					: int							,A					: Optional[Any]							,A					: Any							,A					: Any							,A					: Any							,A					: int							,A					: List[str]							,A					: List[Any] ):
					__A   			= BioGptModel(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A )
					__A   			= model(A )
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size) )






	def 				UpperCamelCase_  (       self					: Any							,A					: Tuple							,A					: str							,A					: Optional[Any]							,A					: str							,A					: Dict							,A					: Dict							,A					: Tuple							,A					: Union[str, Any]							,A					: Tuple							,):
					__A   			= BioGptForCausalLM(config=A )
					model.to(A )
					model.eval()
					__A   			= 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 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: int							,A					: Dict							,A					: Optional[Any]							,A					: List[Any]							,A					: Any							,*A					: Optional[Any] ):
					__A   			= BioGptModel(config=A )
					model.to(A )
					model.eval()

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

					# first forward pass
					__A			,							__A   			= model(A							,attention_mask=A ).to_tuple()

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

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

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

					# get two different outputs
					__A   			= model(A							,attention_mask=A )["last_hidden_state"]
					__A   			= model(A							,past_key_values=A							,attention_mask=A )["last_hidden_state"]

					# select random slice
					__A   			= ids_tensor((1,)							,output_from_past.shape[-1] ).item()
					__A   			= output_from_no_past[:, -1, random_slice_idx].detach()
					__A   			= 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 				UpperCamelCase_  (       self					: Dict							,A					: List[str]							,A					: Dict							,A					: Dict							,A					: Tuple							,A					: Optional[int]							,*A					: int ):
					__A   			= BioGptModel(config=A ).to(A ).eval()

					__A   			= torch.ones(input_ids.shape							,dtype=torch.long							,device=A )

					# first forward pass
					__A   			= model(A							,attention_mask=A							,use_cache=A )

					__A			,							__A   			= outputs.to_tuple()

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

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

					__A   			= model(A							,attention_mask=A )["last_hidden_state"]
					__A   			= model(A							,attention_mask=A							,past_key_values=A )[
					    "last_hidden_state"
					]

					# select random slice
					__A   			= ids_tensor((1,)							,output_from_past.shape[-1] ).item()
					__A   			= output_from_no_past[:, -3:, random_slice_idx].detach()
					__A   			= 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 				UpperCamelCase_  (       self					: Any							,A					: Union[str, Any]							,A					: List[Any]							,A					: str							,A					: List[Any]							,A					: Optional[int]							,*A					: str							,A					: List[Any]=False ):
					__A   			= BioGptForCausalLM(A )
					model.to(A )
					if gradient_checkpointing:
									model.gradient_checkpointing_enable()

					__A   			= 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 				UpperCamelCase_  (       self					: Tuple							,A					: str							,*A					: List[Any] ):
					__A   			= BioGptModel(A )
					__A   			= 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 )							,0.0_01 )
													self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 )							,0.01 )






	def 				UpperCamelCase_  (       self					: Dict							,A					: str							,A					: int							,A					: str							,A					: List[Any]							,A					: str							,*A					: Optional[Any] ):
					__A   			= self.num_labels
					__A   			= BioGptForTokenClassification(A )
					model.to(A )
					model.eval()
					__A   			= 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 				UpperCamelCase_  (       self					: str ):
					__A   			= self.prepare_config_and_inputs()
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= config_and_inputs
					__A   			= {"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			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 				UpperCamelCase_  (       self					: int ):
					__A   			= BioGptModelTester(self )
					__A   			= ConfigTester(self							,config_class=A							,hidden_size=37 )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					self.config_tester.run_common_tests()






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									__A   			= type
									self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_forward_and_backwards(*A							,gradient_checkpointing=A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_weight_initialization(*A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_biogpt_for_token_classification(*A )






	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
					model.to(A )
					__A   			= BioGptTokenizer.from_pretrained("microsoft/biogpt" )

					__A   			= "left"

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

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

					__A   			= tokenizer(A							,return_tensors="pt"							,padding=A )
					__A   			= inputs["input_ids"].to(A )

					__A   			= model.generate(
					    input_ids=A							,attention_mask=inputs["attention_mask"].to(A )							,)

					__A   			= tokenizer(sentences[0]							,return_tensors="pt" ).input_ids.to(A )
					__A   			= model.generate(input_ids=A )

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

					__A   			= tokenizer.batch_decode(A							,skip_special_tokens=A )
					__A   			= tokenizer.decode(output_non_padded[0]							,skip_special_tokens=A )
					__A   			= tokenizer.decode(output_padded[0]							,skip_special_tokens=A )

					__A   			= [
					    "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 				UpperCamelCase_  (       self					: str ):
					for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= BioGptModel.from_pretrained(A )
									self.assertIsNotNone(A )






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







	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()
					__A   			= 3
					__A   			= "multi_label_classification"
					__A   			= input_dict["input_ids"]
					__A   			= input_ids.ne(1 ).to(A )
					__A   			= ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]							,self.model_tester.type_sequence_label_size ).to(torch.float )
					__A   			= BioGptForSequenceClassification(A )
					model.to(A )
					model.eval()
					__A   			= 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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
					__A   			= torch.tensor([[2, 48_05, 9, 6_56, 21]] )
					__A   			= model(A )[0]

					__A   			= 4_23_84

					__A   			= torch.Size((1, 5, vocab_size) )
					self.assertEqual(output.shape							,A )

					__A   			= torch.tensor(
					    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]] )

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







	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= BioGptTokenizer.from_pretrained("microsoft/biogpt" )
					__A   			= BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
					model.to(A )

					torch.manual_seed(0 )
					__A   			= tokenizer("COVID-19 is"							,return_tensors="pt" ).to(A )
					__A   			= model.generate(
					    **A							,min_length=1_00							,max_length=10_24							,num_beams=5							,early_stopping=A							,)
					__A   			= tokenizer.decode(output_ids[0]							,skip_special_tokens=A )

					__A   			= (
					    "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 )


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
