
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     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=13       ,       SCREAMING_SNAKE_CASE_=7       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=99       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=5       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=37       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=None       ,       ):

									"""simple docstring"""







									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 _lowercase					(self    ):

									"""simple docstring"""







									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 _lowercase					(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=SCREAMING_SNAKE_CASE_       ,       initializer_range=self.initializer_range       ,       )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptModel(config=SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_    )
									self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       labels=SCREAMING_SNAKE_CASE_    )
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       *SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptModel(config=SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()

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

									# first forward pass
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).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,)       ,       SCREAMING_SNAKE_CASE_    ).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=SCREAMING_SNAKE_CASE_    )]       ,       dim=1       ,       )

									# get two different outputs
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       past_key_values=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    )['''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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       atol=1e-3    )    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       *SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptModel(config=SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    ).eval()

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

									# first forward pass
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       use_cache=SCREAMING_SNAKE_CASE_    )

									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(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       past_key_values=SCREAMING_SNAKE_CASE_    )[
									    '''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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       atol=1e-3    )    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       *SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptForCausalLM(SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									if gradient_checkpointing:
											model.gradient_checkpointing_enable()

									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       labels=SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       *SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptModel(SCREAMING_SNAKE_CASE_    )
									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    )       ,       0.0_01    )
													self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]    ) - 0.0    )       ,       0.01    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       *SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.num_labels
									SCREAMING_SNAKE_CASE_						 = BioGptForTokenClassification(SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_    )
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.num_labels)    )







							def _lowercase					(self    ):

									"""simple docstring"""







									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  snake_case     (							__lowercase       ,			__lowercase       ,			__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      (
							    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__						     =      (BioGptForCausalLM,) if is_torch_available() else ()
							UpperCAmelCase__						     =      (
							    {
							        '''feature-extraction''': BioGptModel,
							        '''text-classification''': BioGptForSequenceClassification,
							        '''text-generation''': BioGptForCausalLM,
							        '''token-classification''': BioGptForTokenClassification,
							        '''zero-shot''': BioGptForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptModelTester(self    )
									SCREAMING_SNAKE_CASE_						 = ConfigTester(self       ,       config_class=SCREAMING_SNAKE_CASE_       ,       hidden_size=37    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.config_tester.run_common_tests()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									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(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE_       ,       gradient_checkpointing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_    )

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''    )
									model.to(SCREAMING_SNAKE_CASE_    )
									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(SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''       ,       padding=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = inputs['''input_ids'''].to(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model.generate(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=inputs['''attention_mask'''].to(SCREAMING_SNAKE_CASE_    )       ,       )

									SCREAMING_SNAKE_CASE_						 = tokenizer(sentences[0]       ,       return_tensors='''pt'''    ).input_ids.to(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = model.generate(input_ids=SCREAMING_SNAKE_CASE_    )

									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(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = model.generate(input_ids=SCREAMING_SNAKE_CASE_       ,       max_length=model.config.max_length - num_paddings    )

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

									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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       [non_padded_sentence, padded_sentence]    )

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE_						 = BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = ids_tensor([self.model_tester.batch_size]       ,       self.model_tester.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE_						 = BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       labels=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(result.logits.shape       ,       (self.model_tester.batch_size, self.model_tester.num_labels)    )







							def _lowercase					(self    ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_    )
									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(SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       labels=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(result.logits.shape       ,       (self.model_tester.batch_size, self.model_tester.num_labels)    )







@require_torch
class  snake_case     (							unittest.TestCase   ):

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''    )
									SCREAMING_SNAKE_CASE_						 = torch.tensor([[2, 48_05, 9, 6_56, 21]]    )
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_    )[0]

									SCREAMING_SNAKE_CASE_						 = 4_23_84

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

									SCREAMING_SNAKE_CASE_						 = 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]       ,       SCREAMING_SNAKE_CASE_       ,       atol=1e-4    )    )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = BioGptTokenizer.from_pretrained('''microsoft/biogpt'''    )
									SCREAMING_SNAKE_CASE_						 = BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''    )
									model.to(SCREAMING_SNAKE_CASE_    )

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

									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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a,							__a    ):
		return (preds == labels).mean()





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,			)







@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
							UpperCAmelCase__						     =      field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
							UpperCAmelCase__						     =      field(
							    default=128       ,			metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }       ,			)
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def        _lowerCamelCase ( ):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		SCREAMING_SNAKE_CASE_						 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir    )
		    and os.listdir(training_args.output_dir    )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
				    ''' --overwrite_output_dir to overcome.'''    )

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',							datefmt='''%m/%d/%Y %H:%M:%S''',							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,							)
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',							training_args.local_rank,							training_args.device,							training_args.n_gpu,							bool(training_args.local_rank != -1    ),							training_args.fpaa,							)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank    ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('''Training/evaluation parameters %s''',							__a    )

		# Set seed
		set_seed(training_args.seed    )

		try:
				SCREAMING_SNAKE_CASE_						 = processors[data_args.task_name]()
				SCREAMING_SNAKE_CASE_						 = processor.get_labels()
				SCREAMING_SNAKE_CASE_						 = len(__a    )
		except KeyError:
				raise ValueError('''Task not found: %s''' % (data_args.task_name)    )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		SCREAMING_SNAKE_CASE_						 = AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path,							num_labels=__a,							finetuning_task=data_args.task_name,							cache_dir=model_args.cache_dir,							)
		SCREAMING_SNAKE_CASE_						 = AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,							cache_dir=model_args.cache_dir,							)
		SCREAMING_SNAKE_CASE_						 = AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path    ),							config=__a,							cache_dir=model_args.cache_dir,							)

		# Get datasets
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.train,							)
		    if training_args.do_train
		    else None
		)
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.dev,							)
		    if training_args.do_eval
		    else None
		)

		def compute_metrics(__a    ) -> Dict:
				SCREAMING_SNAKE_CASE_						 = np.argmax(p.predictions,							axis=1    )
				return {"acc": simple_accuracy(__a,							p.label_ids    )}

		# Data collator
		SCREAMING_SNAKE_CASE_						 = DataCollatorWithPadding(__a,							pad_to_multiple_of=8    ) if training_args.fpaa else None

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE_						 = Trainer(
		    model=__a,							args=__a,							train_dataset=__a,							eval_dataset=__a,							compute_metrics=__a,							data_collator=__a,							)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
		SCREAMING_SNAKE_CASE_						 = {}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''    )

				SCREAMING_SNAKE_CASE_						 = trainer.evaluate()

				SCREAMING_SNAKE_CASE_						 = os.path.join(training_args.output_dir,							'''eval_results.txt'''    )
				if trainer.is_world_master():
						with open(__a,							'''w'''    ) as writer:
								logger.info('''***** Eval results *****'''    )
								for key, value in result.items():
										logger.info('''  %s = %s''',							__a,							__a    )
										writer.write('''%s = %s\n''' % (key, value)    )

								results.update(__a    )

		return results



def        _lowerCamelCase ( __a    ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()