




"""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 __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=True						,	__a=False						,	__a=True						,	__a=99						,	__a=32						,	__a=5						,	__a=4						,	__a=37						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=512						,	__a=16						,	__a=2						,	__a=0.02						,	__a=3						,	__a=4						,	__a=None						,	):
								__lowerCamelCase					:	Optional[Any]	       =				parent
								__lowerCamelCase					:	Optional[int]	       =				batch_size
								__lowerCamelCase					:	Optional[int]	       =				seq_length
								__lowerCamelCase					:	Union[str, Any]	       =				is_training
								__lowerCamelCase					:	Any	       =				use_input_mask
								__lowerCamelCase					:	Dict	       =				use_token_type_ids
								__lowerCamelCase					:	Union[str, Any]	       =				use_labels
								__lowerCamelCase					:	int	       =				vocab_size
								__lowerCamelCase					:	Optional[int]	       =				hidden_size
								__lowerCamelCase					:	Optional[int]	       =				num_hidden_layers
								__lowerCamelCase					:	int	       =				num_attention_heads
								__lowerCamelCase					:	Tuple	       =				intermediate_size
								__lowerCamelCase					:	Any	       =				hidden_act
								__lowerCamelCase					:	Optional[Any]	       =				hidden_dropout_prob
								__lowerCamelCase					:	Tuple	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	Tuple	       =				max_position_embeddings
								__lowerCamelCase					:	str	       =				type_vocab_size
								__lowerCamelCase					:	Any	       =				type_sequence_label_size
								__lowerCamelCase					:	Dict	       =				initializer_range
								__lowerCamelCase					:	Any	       =				num_labels
								__lowerCamelCase					:	Dict	       =				num_choices
								__lowerCamelCase					:	Union[str, Any]	       =				scope
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

								__lowerCamelCase					:	Optional[Any]	       =				None
								if self.use_input_mask:
											__lowerCamelCase					:	Union[str, Any]	       =				random_attention_mask([self.batch_size, self.seq_length]     )

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

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

								__lowerCamelCase					:	str	       =				self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def     snake_case_				(    self     ):
								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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	int	       =				BioGptModel(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	List[str]	       =				model(__a						,	attention_mask=__a     )
								__lowerCamelCase					:	str	       =				model(__a     )
								self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	):
								__lowerCamelCase					:	List[str]	       =				BioGptForCausalLM(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Dict	       =				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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	*__a     ):
								__lowerCamelCase					:	int	       =				BioGptModel(config=__a     )
								model.to(__a     )
								model.eval()

								# create attention mask
								__lowerCamelCase					:	int	       =				torch.ones(input_ids.shape						,	dtype=torch.long						,	device=__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.seq_length // 2
								__lowerCamelCase					:	Union[str, Any]	       =				0

								# first forward pass
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				model(__a						,	attention_mask=__a     ).to_tuple()

								# create hypothetical next token and extent to next_input_ids
								__lowerCamelCase					:	List[Any]	       =				ids_tensor((self.batch_size, 1)						,	config.vocab_size     )

								# change a random masked slice from input_ids
								__lowerCamelCase					:	int	       =				ids_tensor((1,)						,	__a     ).item() + 1
								__lowerCamelCase					:	List[Any]	       =				ids_tensor((self.batch_size, 1)						,	config.vocab_size     ).squeeze(-1     )
								__lowerCamelCase					:	Union[str, Any]	       =				random_other_next_tokens

								# append to next input_ids and attn_mask
								__lowerCamelCase					:	Union[str, Any]	       =				torch.cat([input_ids, next_tokens]						,	dim=-1     )
								__lowerCamelCase					:	Optional[int]	       =				torch.cat(
								    [attn_mask, torch.ones((attn_mask.shape[0], 1)						,	dtype=torch.long						,	device=__a     )]						,	dim=1						,	)

								# get two different outputs
								__lowerCamelCase					:	Dict	       =				model(__a						,	attention_mask=__a     )['last_hidden_state']
								__lowerCamelCase					:	Optional[Any]	       =				model(__a						,	past_key_values=__a						,	attention_mask=__a     )['last_hidden_state']

								# select random slice
								__lowerCamelCase					:	Union[str, Any]	       =				ids_tensor((1,)						,	output_from_past.shape[-1]     ).item()
								__lowerCamelCase					:	List[Any]	       =				output_from_no_past[:, -1, random_slice_idx].detach()
								__lowerCamelCase					:	int	       =				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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	*__a     ):
								__lowerCamelCase					:	Tuple	       =				BioGptModel(config=__a     ).to(__a     ).eval()

								__lowerCamelCase					:	int	       =				torch.ones(input_ids.shape						,	dtype=torch.long						,	device=__a     )

								# first forward pass
								__lowerCamelCase					:	List[Any]	       =				model(__a						,	attention_mask=__a						,	use_cache=__a     )

								__lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				outputs.to_tuple()

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

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

								__lowerCamelCase					:	str	       =				model(__a						,	attention_mask=__a     )['last_hidden_state']
								__lowerCamelCase					:	List[Any]	       =				model(__a						,	attention_mask=__a						,	past_key_values=__a     )[
								    'last_hidden_state'
								]

								# select random slice
								__lowerCamelCase					:	List[Any]	       =				ids_tensor((1,)						,	output_from_past.shape[-1]     ).item()
								__lowerCamelCase					:	Tuple	       =				output_from_no_past[:, -3:, random_slice_idx].detach()
								__lowerCamelCase					:	Dict	       =				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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	*__a						,	__a=False     ):
								__lowerCamelCase					:	Optional[Any]	       =				BioGptForCausalLM(__a     )
								model.to(__a     )
								if gradient_checkpointing:
											model.gradient_checkpointing_enable()

								__lowerCamelCase					:	Tuple	       =				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     snake_case_				(    self						,	__a						,	*__a     ):
								__lowerCamelCase					:	str	       =				BioGptModel(__a     )
								__lowerCamelCase					:	Optional[int]	       =				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     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	*__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.num_labels
								__lowerCamelCase					:	Dict	       =				BioGptForTokenClassification(__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	List[str]	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				self.prepare_config_and_inputs()
								(
								    (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      
								)					:	List[Any]	       =				config_and_inputs
								__lowerCamelCase					:	Optional[int]	       =				{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class __lowercase(      lowercase__	,	lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						str     		= (
					    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
					    if is_torch_available()
					    else ()
					)
					__a						:						Tuple     		= (BioGptForCausalLM,) if is_torch_available() else ()
					__a						:						Optional[Any]     		= (
					    {
					        'feature-extraction': BioGptModel,
					        'text-classification': BioGptForSequenceClassification,
					        'text-generation': BioGptForCausalLM,
					        'token-classification': BioGptForTokenClassification,
					        'zero-shot': BioGptForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__a						:						Dict     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				BioGptModelTester(self     )
								__lowerCamelCase					:	int	       =				ConfigTester(self						,	config_class=__a						,	hidden_size=37     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
											__lowerCamelCase					:	List[Any]	       =				type
											self.model_tester.create_and_check_model(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_forward_and_backwards(*__a						,	gradient_checkpointing=__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_weight_initialization(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_for_token_classification(*__a     )
					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				BioGptForCausalLM.from_pretrained('microsoft/biogpt'     )
								model.to(__a     )
								__lowerCamelCase					:	Dict	       =				BioGptTokenizer.from_pretrained('microsoft/biogpt'     )

								__lowerCamelCase					:	Any	       =				'left'

								# Define PAD Token = EOS Token = 50256
								__lowerCamelCase					:	int	       =				tokenizer.eos_token
								__lowerCamelCase					:	Any	       =				model.config.eos_token_id

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

								__lowerCamelCase					:	List[Any]	       =				tokenizer(__a						,	return_tensors='pt'						,	padding=__a     )
								__lowerCamelCase					:	List[str]	       =				inputs['input_ids'].to(__a     )

								__lowerCamelCase					:	Optional[int]	       =				model.generate(
								    input_ids=__a						,	attention_mask=inputs['attention_mask'].to(__a     )						,	)

								__lowerCamelCase					:	Optional[Any]	       =				tokenizer(sentences[0]						,	return_tensors='pt'     ).input_ids.to(__a     )
								__lowerCamelCase					:	Optional[Any]	       =				model.generate(input_ids=__a     )

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

								__lowerCamelCase					:	str	       =				tokenizer.batch_decode(__a						,	skip_special_tokens=__a     )
								__lowerCamelCase					:	Dict	       =				tokenizer.decode(output_non_padded[0]						,	skip_special_tokens=__a     )
								__lowerCamelCase					:	List[str]	       =				tokenizer.decode(output_padded[0]						,	skip_special_tokens=__a     )

								__lowerCamelCase					:	Tuple	       =				[
								    '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     snake_case_				(    self     ):
								for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase					:	List[str]	       =				BioGptModel.from_pretrained(__a     )
											self.assertIsNotNone(__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase					:	List[Any]	       =				3
								__lowerCamelCase					:	Optional[int]	       =				input_dict['input_ids']
								__lowerCamelCase					:	Dict	       =				input_ids.ne(1     ).to(__a     )
								__lowerCamelCase					:	Tuple	       =				ids_tensor([self.model_tester.batch_size]						,	self.model_tester.type_sequence_label_size     )
								__lowerCamelCase					:	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)     )





					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	List[str]	       =				self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase					:	List[str]	       =				3
								__lowerCamelCase					:	Any	       =				'multi_label_classification'
								__lowerCamelCase					:	Any	       =				input_dict['input_ids']
								__lowerCamelCase					:	Optional[int]	       =				input_ids.ne(1     ).to(__a     )
								__lowerCamelCase					:	Optional[int]	       =				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					:	str	       =				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 __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				BioGptForCausalLM.from_pretrained('microsoft/biogpt'     )
								__lowerCamelCase					:	Union[str, Any]	       =				torch.tensor([[2, 4805, 9, 656, 21]]     )
								__lowerCamelCase					:	Optional[int]	       =				model(__a     )[0]

								__lowerCamelCase					:	Optional[Any]	       =				42384

								__lowerCamelCase					:	Any	       =				torch.Size((1, 5, vocab_size)     )
								self.assertEqual(output.shape						,	__a     )

								__lowerCamelCase					:	Dict	       =				torch.tensor(
								    [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]     )

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





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				BioGptTokenizer.from_pretrained('microsoft/biogpt'     )
								__lowerCamelCase					:	Tuple	       =				BioGptForCausalLM.from_pretrained('microsoft/biogpt'     )
								model.to(__a     )

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

								__lowerCamelCase					:	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     )









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



