


'''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   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=13    ,						UpperCamelCase=7    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=99    ,						UpperCamelCase=32    ,						UpperCamelCase=5    ,						UpperCamelCase=4    ,						UpperCamelCase=37    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=512    ,						UpperCamelCase=16    ,						UpperCamelCase=2    ,						UpperCamelCase=0.02    ,						UpperCamelCase=3    ,						UpperCamelCase=4    ,						UpperCamelCase=None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						seq_length
   lowerCamelCase_  				=						is_training
   lowerCamelCase_  				=						use_input_mask
   lowerCamelCase_  				=						use_token_type_ids
   lowerCamelCase_  				=						use_labels
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						max_position_embeddings
   lowerCamelCase_  				=						type_vocab_size
   lowerCamelCase_  				=						type_sequence_label_size
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_labels
   lowerCamelCase_  				=						num_choices
   lowerCamelCase_  				=						scope






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size  )

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

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

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

   lowerCamelCase_  				=						self.get_config()

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






  def 						snake_case	(					self  ):







   """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=UpperCamelCase    ,						initializer_range=self.initializer_range    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptModel(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase  )
   lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptForCausalLM(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						labels=UpperCamelCase  )
   self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptModel(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()

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

   # first forward pass
   lowerCamelCase_				,lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase  ).to_tuple()

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

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

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

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

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

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






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptModel(config=UpperCamelCase  ).to(UpperCamelCase  ).eval()

   lowerCamelCase_  				=						torch.ones(input_ids.shape    ,						dtype=torch.long    ,						device=UpperCamelCase  )

   # first forward pass
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						use_cache=UpperCamelCase  )

   lowerCamelCase_				,lowerCamelCase_  				=						outputs.to_tuple()

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

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

   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase  )["last_hidden_state"]
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						past_key_values=UpperCamelCase  )[
       "last_hidden_state"
   ]

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






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						*UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptForCausalLM(UpperCamelCase  )
   model.to(UpperCamelCase  )
   if gradient_checkpointing:
    model.gradient_checkpointing_enable()

   lowerCamelCase_  				=						model(UpperCamelCase    ,						labels=UpperCamelCase  )
   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    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptModel(UpperCamelCase  )
   lowerCamelCase_  				=						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    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.num_labels
   lowerCamelCase_  				=						BioGptForTokenClassification(UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase  )
   self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels)  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_
   )				,(
       lowerCamelCase_
   )				,(
       lowerCamelCase_
   )				,(
       lowerCamelCase_
   )				,(
       lowerCamelCase_
   )				,(
       lowerCamelCase_
   )				,(
       lowerCamelCase_
   )				,
   )  				=						config_and_inputs
   lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": input_mask}
   return config, inputs_dict





@require_torch
class   snake_case					(					lowercase    ,		lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						hidden_size=37  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    lowerCamelCase_  				=						type
    self.model_tester.create_and_check_model(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_forward_and_backwards(*UpperCamelCase    ,						gradient_checkpointing=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCamelCase  )






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptForCausalLM.from_pretrained("microsoft/biogpt"  )
   model.to(UpperCamelCase  )
   lowerCamelCase_  				=						BioGptTokenizer.from_pretrained("microsoft/biogpt"  )

   lowerCamelCase_  				=						"left"

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

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

   lowerCamelCase_  				=						tokenizer(UpperCamelCase    ,						return_tensors="pt"    ,						padding=UpperCamelCase  )
   lowerCamelCase_  				=						inputs["input_ids"].to(UpperCamelCase  )

   lowerCamelCase_  				=						model.generate(
       input_ids=UpperCamelCase    ,						attention_mask=inputs["attention_mask"].to(UpperCamelCase  )    ,						)

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

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

   lowerCamelCase_  				=						tokenizer.batch_decode(UpperCamelCase    ,						skip_special_tokens=UpperCamelCase  )
   lowerCamelCase_  				=						tokenizer.decode(output_non_padded[0]    ,						skip_special_tokens=UpperCamelCase  )
   lowerCamelCase_  				=						tokenizer.decode(output_padded[0]    ,						skip_special_tokens=UpperCamelCase  )

   lowerCamelCase_  				=						[
       "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(UpperCamelCase    ,						UpperCamelCase  )
   self.assertListEqual(UpperCamelCase    ,						[non_padded_sentence, padded_sentence]  )






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_  				=						BioGptModel.from_pretrained(UpperCamelCase  )
    self.assertIsNotNone(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_  				=						3
   lowerCamelCase_  				=						input_dict["input_ids"]
   lowerCamelCase_  				=						input_ids.ne(1  ).to(UpperCamelCase  )
   lowerCamelCase_  				=						ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size  )
   lowerCamelCase_  				=						BioGptForSequenceClassification(UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						labels=UpperCamelCase  )
   self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)  )







  def 						snake_case	(					self  ):







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





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptForCausalLM.from_pretrained("microsoft/biogpt"  )
   lowerCamelCase_  				=						torch.tensor([[2, 4805, 9, 656, 21]]  )
   lowerCamelCase_  				=						model(UpperCamelCase  )[0]

   lowerCamelCase_  				=						4_2384

   lowerCamelCase_  				=						torch.Size((1, 5, vocab_size)  )
   self.assertEqual(output.shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						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]    ,						UpperCamelCase    ,						atol=1e-4  )  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						BioGptTokenizer.from_pretrained("microsoft/biogpt"  )
   lowerCamelCase_  				=						BioGptForCausalLM.from_pretrained("microsoft/biogpt"  )
   model.to(UpperCamelCase  )

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

   lowerCamelCase_  				=						(
       "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(UpperCamelCase    ,						UpperCamelCase  )







'''simple docstring'''

from collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

 lowerCamelCase_  				=						nums[0]
 for i in range(1      ,       len(UpperCAmelCase_	)	):
  lowerCamelCase_  				=						nums[i]
  lowerCamelCase_  				=						max(UpperCAmelCase_      ,       ans + num      ,       UpperCAmelCase_	)

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     a_						:       Any      			=				int(input("""Enter number of elements : """).strip())
     a_						:       Union[str, Any]      			=				list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
     print(max_subsequence_sum(array))



