




"""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 lowerCAmelCase__	:






			'''simple docstring'''



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

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

							_lowerCamelCase     :			List[str]    =    self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 					A_    (  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=lowercase  ,      initializer_range=self.initializer_range  ,      )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    BioGptModel(config=lowercase  )
							model.to(lowercase  )
							model.eval()
							_lowerCamelCase     :			List[str]    =    model(lowercase  ,      attention_mask=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    model(lowercase  )
							self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      ):
							_lowerCamelCase     :			int    =    BioGptForCausalLM(config=lowercase  )
							model.to(lowercase  )
							model.eval()
							_lowerCamelCase     :			Optional[Any]    =    model(lowercase  ,      attention_mask=lowercase  ,      token_type_ids=lowercase  ,      labels=lowercase  )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      *lowercase  ):
							_lowerCamelCase     :			List[str]    =    BioGptModel(config=lowercase  )
							model.to(lowercase  )
							model.eval()

							# create attention mask
							_lowerCamelCase     :			List[str]    =    torch.ones(input_ids.shape  ,      dtype=torch.long  ,      device=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    self.seq_length // 2
							_lowerCamelCase     :			List[str]    =    0

							# first forward pass
							_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    model(lowercase  ,      attention_mask=lowercase  ).to_tuple()

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

							# change a random masked slice from input_ids
							_lowerCamelCase     :			Tuple    =    ids_tensor((1,)  ,      lowercase  ).item() + 1
							_lowerCamelCase     :			Tuple    =    ids_tensor((self.batch_size, 1)  ,      config.vocab_size  ).squeeze(-1  )
							_lowerCamelCase     :			str    =    random_other_next_tokens

							# append to next input_ids and attn_mask
							_lowerCamelCase     :			List[str]    =    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=lowercase  )]  ,      dim=1  ,      )

							# get two different outputs
							_lowerCamelCase     :			str    =    model(lowercase  ,      attention_mask=lowercase  )['last_hidden_state']
							_lowerCamelCase     :			str    =    model(lowercase  ,      past_key_values=lowercase  ,      attention_mask=lowercase  )['last_hidden_state']

							# select random slice
							_lowerCamelCase     :			Dict    =    ids_tensor((1,)  ,      output_from_past.shape[-1]  ).item()
							_lowerCamelCase     :			Union[str, Any]    =    output_from_no_past[:, -1, random_slice_idx].detach()
							_lowerCamelCase     :			Tuple    =    output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(lowercase  ,      lowercase  ,      atol=1E-3  )  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      *lowercase  ):
							_lowerCamelCase     :			int    =    BioGptModel(config=lowercase  ).to(lowercase  ).eval()

							_lowerCamelCase     :			List[str]    =    torch.ones(input_ids.shape  ,      dtype=torch.long  ,      device=lowercase  )

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

							_lowerCamelCase, _lowerCamelCase     :			Dict    =    outputs.to_tuple()

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

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

							_lowerCamelCase     :			List[Any]    =    model(lowercase  ,      attention_mask=lowercase  )['last_hidden_state']
							_lowerCamelCase     :			Dict    =    model(lowercase  ,      attention_mask=lowercase  ,      past_key_values=lowercase  )[
							    'last_hidden_state'
							]

							# select random slice
							_lowerCamelCase     :			Optional[Any]    =    ids_tensor((1,)  ,      output_from_past.shape[-1]  ).item()
							_lowerCamelCase     :			List[str]    =    output_from_no_past[:, -3:, random_slice_idx].detach()
							_lowerCamelCase     :			int    =    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(lowercase  ,      lowercase  ,      atol=1E-3  )  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      *lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			List[str]    =    BioGptForCausalLM(lowercase  )
							model.to(lowercase  )
							if gradient_checkpointing:
											model.gradient_checkpointing_enable()

							_lowerCamelCase     :			List[Any]    =    model(lowercase  ,      labels=lowercase  )
							self.parent.assertEqual(result.loss.shape  ,      ()  )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)  )
							result.loss.backward()
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							_lowerCamelCase     :			List[str]    =    BioGptModel(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    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 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      *lowercase  ):
							_lowerCamelCase     :			Dict    =    self.num_labels
							_lowerCamelCase     :			int    =    BioGptForTokenClassification(lowercase  )
							model.to(lowercase  )
							model.eval()
							_lowerCamelCase     :			List[Any]    =    model(lowercase  ,      attention_mask=lowercase  ,      token_type_ids=lowercase  )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)  )






			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    self.prepare_config_and_inputs()
							(
							    (
							    _lowerCamelCase
							), (
							    _lowerCamelCase
							), (
							    _lowerCamelCase
							), (
							    _lowerCamelCase
							), (
							    _lowerCamelCase
							), (
							    _lowerCamelCase
							), (
							    _lowerCamelCase
							), 
							)     :			Dict    =    config_and_inputs
							_lowerCamelCase     :			str    =    {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__	(     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 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    BioGptModelTester(self  )
							_lowerCamelCase     :			Optional[Any]    =    ConfigTester(self  ,      config_class=lowercase  ,      hidden_size=37  )
			def 					A_    (  self  ):
							self.config_tester.run_common_tests()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											_lowerCamelCase     :			Tuple    =    type
											self.model_tester.create_and_check_model(*lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*lowercase  ,      gradient_checkpointing=lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase  )
			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
							model.to(lowercase  )
							_lowerCamelCase     :			List[str]    =    BioGptTokenizer.from_pretrained('microsoft/biogpt'  )

							_lowerCamelCase     :			Optional[int]    =    'left'

							# Define PAD Token = EOS Token = 50256
							_lowerCamelCase     :			Tuple    =    tokenizer.eos_token
							_lowerCamelCase     :			Optional[int]    =    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     :			Optional[Any]    =    tokenizer(lowercase  ,      return_tensors='pt'  ,      padding=lowercase  )
							_lowerCamelCase     :			Dict    =    inputs['input_ids'].to(lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    model.generate(
							    input_ids=lowercase  ,      attention_mask=inputs['attention_mask'].to(lowercase  )  ,      )

							_lowerCamelCase     :			Union[str, Any]    =    tokenizer(sentences[0]  ,      return_tensors='pt'  ).input_ids.to(lowercase  )
							_lowerCamelCase     :			List[Any]    =    model.generate(input_ids=lowercase  )

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

							_lowerCamelCase     :			int    =    tokenizer.batch_decode(lowercase  ,      skip_special_tokens=lowercase  )
							_lowerCamelCase     :			List[Any]    =    tokenizer.decode(output_non_padded[0]  ,      skip_special_tokens=lowercase  )
							_lowerCamelCase     :			int    =    tokenizer.decode(output_padded[0]  ,      skip_special_tokens=lowercase  )

							_lowerCamelCase     :			str    =    [
							    '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(lowercase  ,      lowercase  )
							self.assertListEqual(lowercase  ,      [non_padded_sentence, padded_sentence]  )
			@slow
			def 					A_    (  self  ):
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase     :			List[Any]    =    BioGptModel.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase     :			Union[str, Any]    =    3
							_lowerCamelCase     :			Optional[int]    =    input_dict['input_ids']
							_lowerCamelCase     :			Tuple    =    input_ids.ne(1  ).to(lowercase  )
							_lowerCamelCase     :			Any    =    ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size  )
							_lowerCamelCase     :			Tuple    =    BioGptForSequenceClassification(lowercase  )
							model.to(lowercase  )
							model.eval()
							_lowerCamelCase     :			str    =    model(lowercase  ,      attention_mask=lowercase  ,      labels=lowercase  )
							self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)  )






			def 					A_    (  self  ):
							_lowerCamelCase, _lowerCamelCase     :			List[Any]    =    self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase     :			int    =    3
							_lowerCamelCase     :			List[Any]    =    'multi_label_classification'
							_lowerCamelCase     :			Tuple    =    input_dict['input_ids']
							_lowerCamelCase     :			List[Any]    =    input_ids.ne(1  ).to(lowercase  )
							_lowerCamelCase     :			str    =    ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]  ,      self.model_tester.type_sequence_label_size  ).to(torch.float  )
							_lowerCamelCase     :			Union[str, Any]    =    BioGptForSequenceClassification(lowercase  )
							model.to(lowercase  )
							model.eval()
							_lowerCamelCase     :			List[str]    =    model(lowercase  ,      attention_mask=lowercase  ,      labels=lowercase  )
							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 					A_    (  self  ):
							_lowerCamelCase     :			str    =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
							_lowerCamelCase     :			List[str]    =    torch.tensor([[2, 4805, 9, 656, 21]]  )
							_lowerCamelCase     :			List[str]    =    model(lowercase  )[0]

							_lowerCamelCase     :			str    =    42384

							_lowerCamelCase     :			int    =    torch.Size((1, 5, vocab_size)  )
							self.assertEqual(output.shape  ,      lowercase  )

							_lowerCamelCase     :			str    =    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]  ,      lowercase  ,      atol=1E-4  )  )






			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    BioGptTokenizer.from_pretrained('microsoft/biogpt'  )
							_lowerCamelCase     :			Tuple    =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
							model.to(lowercase  )

							torch.manual_seed(0  )
							_lowerCamelCase     :			Union[str, Any]    =    tokenizer('COVID-19 is'  ,      return_tensors='pt'  ).to(lowercase  )
							_lowerCamelCase     :			int    =    model.generate(
							    **lowercase  ,      min_length=100  ,      max_length=1024  ,      num_beams=5  ,      early_stopping=lowercase  ,      )
							_lowerCamelCase     :			Any    =    tokenizer.decode(output_ids[0]  ,      skip_special_tokens=lowercase  )

							_lowerCamelCase     :			List[str]    =    (
							    '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(lowercase  ,      lowercase  )





"""simple docstring"""






import math







def      _snake_case			(				lowercase__		):
				return math.sqrt(lowercase__		) * math.sqrt(lowercase__		) == num







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			List[Any]    =    n
				while left <= right:
								_lowerCamelCase     :			str    =    (left + right) // 2
								if mid**2 == n:
												return True
								elif mid**2 > n:
												_lowerCamelCase     :			str    =    mid - 1
								else:
												_lowerCamelCase     :			Optional[int]    =    mid + 1
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()