


"""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 			UpperCamelCase_     :

	"""simple docstring"""





	def __init__(			self      :    List[Any]      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Union[str, Any]=1_3      ,      UpperCAmelCase__      :    str=7      ,      UpperCAmelCase__      :    Tuple=True      ,      UpperCAmelCase__      :    str=True      ,      UpperCAmelCase__      :    Dict=False      ,      UpperCAmelCase__      :    Optional[int]=True      ,      UpperCAmelCase__      :    Dict=9_9      ,      UpperCAmelCase__      :    Dict=3_2      ,      UpperCAmelCase__      :    Union[str, Any]=5      ,      UpperCAmelCase__      :    Dict=4      ,      UpperCAmelCase__      :    str=3_7      ,      UpperCAmelCase__      :    str="gelu"      ,      UpperCAmelCase__      :    Tuple=0.1      ,      UpperCAmelCase__      :    str=0.1      ,      UpperCAmelCase__      :    Any=5_1_2      ,      UpperCAmelCase__      :    Any=1_6      ,      UpperCAmelCase__      :    Optional[int]=2      ,      UpperCAmelCase__      :    List[str]=0.02      ,      UpperCAmelCase__      :    int=3      ,      UpperCAmelCase__      :    List[str]=4      ,      UpperCAmelCase__      :    str=None      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    parent
						__SCREAMING_SNAKE_CASE						       =    batch_size
						__SCREAMING_SNAKE_CASE						       =    seq_length
						__SCREAMING_SNAKE_CASE						       =    is_training
						__SCREAMING_SNAKE_CASE						       =    use_input_mask
						__SCREAMING_SNAKE_CASE						       =    use_token_type_ids
						__SCREAMING_SNAKE_CASE						       =    use_labels
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    hidden_size
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    intermediate_size
						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    max_position_embeddings
						__SCREAMING_SNAKE_CASE						       =    type_vocab_size
						__SCREAMING_SNAKE_CASE						       =    type_sequence_label_size
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    num_labels
						__SCREAMING_SNAKE_CASE						       =    num_choices
						__SCREAMING_SNAKE_CASE						       =    scope




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size       )

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

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

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

						__SCREAMING_SNAKE_CASE						       =    self.get_config()

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




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Tuple:
						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   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    str       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    BioGptModel(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)       )




	def   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Tuple      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    BioGptForCausalLM(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      *UpperCAmelCase__      :    Dict       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    BioGptModel(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()

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

						# first forward pass
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__       ).to_tuple()

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

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

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

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

						# select random slice
						__SCREAMING_SNAKE_CASE						       =    ids_tensor((1,)      ,      output_from_past.shape[-1]       ).item()
						__SCREAMING_SNAKE_CASE						       =    output_from_no_past[:, -1, random_slice_idx].detach()
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[str]      ,      *UpperCAmelCase__      :    int       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    BioGptModel(config=UpperCAmelCase__       ).to(UpperCAmelCase__       ).eval()

						__SCREAMING_SNAKE_CASE						       =    torch.ones(input_ids.shape      ,      dtype=torch.long      ,      device=UpperCAmelCase__       )

						# first forward pass
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__      ,      use_cache=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    outputs.to_tuple()

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

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

						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__       )["last_hidden_state"]
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__      ,      past_key_values=UpperCAmelCase__       )[
						    "last_hidden_state"
						]

						# select random slice
						__SCREAMING_SNAKE_CASE						       =    ids_tensor((1,)      ,      output_from_past.shape[-1]       ).item()
						__SCREAMING_SNAKE_CASE						       =    output_from_no_past[:, -3:, random_slice_idx].detach()
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Union[str, Any]      ,      *UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Any=False       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    BioGptForCausalLM(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						if gradient_checkpointing:
											model.gradient_checkpointing_enable()

						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    Tuple      ,      *UpperCAmelCase__      :    Tuple       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    BioGptModel(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      *UpperCAmelCase__      :    Optional[Any]       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.num_labels
						__SCREAMING_SNAKE_CASE						       =    BioGptForTokenClassification(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    int       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.prepare_config_and_inputs()
						(
						    (
						    __SCREAMING_SNAKE_CASE  
						)       ,  (
						    __SCREAMING_SNAKE_CASE  
						)       ,  (
						    __SCREAMING_SNAKE_CASE  
						)       ,  (
						    __SCREAMING_SNAKE_CASE  
						)       ,  (
						    __SCREAMING_SNAKE_CASE  
						)       ,  (
						    __SCREAMING_SNAKE_CASE  
						)       ,  (
						    __SCREAMING_SNAKE_CASE  
						)       ,    
						)						       =    config_and_inputs
						__SCREAMING_SNAKE_CASE						       =    {"input_ids": input_ids, "attention_mask": input_mask}
						return config, inputs_dict




@require_torch
class 			UpperCamelCase_     (			UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  Tuple    =      (
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	snake_case__    :  List[str]    =      (BioGptForCausalLM,) if is_torch_available() else ()
	snake_case__    :  int    =      (
	    {
	        "feature-extraction": BioGptModel,
	        "text-classification": BioGptForSequenceClassification,
	        "text-generation": BioGptForCausalLM,
	        "token-classification": BioGptForTokenClassification,
	        "zero-shot": BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case__    :  Union[str, Any]    =      False




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    BioGptModelTester(self       )
						__SCREAMING_SNAKE_CASE						       =    ConfigTester(self      ,      config_class=UpperCAmelCase__      ,      hidden_size=3_7       )




	def   UpperCAmelCase_				(			self      :    int       )	->     Any:
						self.config_tester.run_common_tests()




	def   UpperCAmelCase_				(			self      :    Any       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__SCREAMING_SNAKE_CASE						       =    type
											self.model_tester.create_and_check_model(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Any       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*UpperCAmelCase__      ,      gradient_checkpointing=UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCAmelCase__       )




	@slow
	def   UpperCAmelCase_				(			self      :    Dict       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
						model.to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )

						__SCREAMING_SNAKE_CASE						       =    "left"

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

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

						__SCREAMING_SNAKE_CASE						       =    tokenizer(UpperCAmelCase__      ,      return_tensors="pt"      ,      padding=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    inputs["input_ids"].to(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    model.generate(
						    input_ids=UpperCAmelCase__      ,      attention_mask=inputs["attention_mask"].to(UpperCAmelCase__       )      ,      )

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

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

						__SCREAMING_SNAKE_CASE						       =    tokenizer.batch_decode(UpperCAmelCase__      ,      skip_special_tokens=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    tokenizer.decode(output_non_padded[0]      ,      skip_special_tokens=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    tokenizer.decode(output_padded[0]      ,      skip_special_tokens=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    [
						    "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   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     str:
						for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__SCREAMING_SNAKE_CASE						       =    BioGptModel.from_pretrained(UpperCAmelCase__       )
											self.assertIsNotNone(UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Any       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()
						__SCREAMING_SNAKE_CASE						       =    3
						__SCREAMING_SNAKE_CASE						       =    input_dict["input_ids"]
						__SCREAMING_SNAKE_CASE						       =    input_ids.ne(1       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size       )
						__SCREAMING_SNAKE_CASE						       =    BioGptForSequenceClassification(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      attention_mask=UpperCAmelCase__      ,      labels=UpperCAmelCase__       )
						self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)       )




	def   UpperCAmelCase_				(			self      :    str       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()
						__SCREAMING_SNAKE_CASE						       =    3
						__SCREAMING_SNAKE_CASE						       =    "multi_label_classification"
						__SCREAMING_SNAKE_CASE						       =    input_dict["input_ids"]
						__SCREAMING_SNAKE_CASE						       =    input_ids.ne(1       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size       ).to(torch.float       )
						__SCREAMING_SNAKE_CASE						       =    BioGptForSequenceClassification(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    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 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	@slow
	def   UpperCAmelCase_				(			self      :    Any       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
						__SCREAMING_SNAKE_CASE						       =    torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )[0]

						__SCREAMING_SNAKE_CASE						       =    4_2_3_8_4

						__SCREAMING_SNAKE_CASE						       =    torch.Size((1, 5, vocab_size)       )
						self.assertEqual(output.shape      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    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   UpperCAmelCase_				(			self      :    str       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )
						__SCREAMING_SNAKE_CASE						       =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
						model.to(UpperCAmelCase__       )

						torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    tokenizer("COVID-19 is"      ,      return_tensors="pt"       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    model.generate(
						    **UpperCAmelCase__      ,      min_length=1_0_0      ,      max_length=1_0_2_4      ,      num_beams=5      ,      early_stopping=UpperCAmelCase__      ,      )
						__SCREAMING_SNAKE_CASE						       =    tokenizer.decode(output_ids[0]      ,      skip_special_tokens=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    (
						    "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"""







a__    :							Optional[Any]            =					'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    input("Enter message: "       )
					__SCREAMING_SNAKE_CASE						       =    input("Enter key [alphanumeric]: "       )
					__SCREAMING_SNAKE_CASE						       =    input("Encrypt/Decrypt [e/d]: "       )

					if mode.lower().startswith("e"       ):
										__SCREAMING_SNAKE_CASE						       =    "encrypt"
										__SCREAMING_SNAKE_CASE						       =    encrypt_message(lowerCAmelCase_  ,						lowerCAmelCase_       )
					elif mode.lower().startswith("d"       ):
										__SCREAMING_SNAKE_CASE						       =    "decrypt"
										__SCREAMING_SNAKE_CASE						       =    decrypt_message(lowerCAmelCase_  ,						lowerCAmelCase_       )

					print(f"""\n{mode.title()}ed message:"""       )
					print(lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return translate_message(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"encrypt"       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return translate_message(lowerCAmelCase_  ,						lowerCAmelCase_  ,						"decrypt"       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    []
					__SCREAMING_SNAKE_CASE						       =    0
					__SCREAMING_SNAKE_CASE						       =    key.upper()

					for symbol in message:
										__SCREAMING_SNAKE_CASE						       =    LETTERS.find(symbol.upper()       )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]       )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]       )

															num %= len(lowerCAmelCase_       )

															if symbol.isupper():
																				translated.append(LETTERS[num]       )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()       )

															key_index += 1
															if key_index == len(lowerCAmelCase_       ):
																				__SCREAMING_SNAKE_CASE						       =    0
										else:
															translated.append(lowerCAmelCase_       )
					return "".join(lowerCAmelCase_       )


if __name__ == "__main__":
		main()
