
"""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 ,		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 ,		)					->  int:







            '''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    _lowerCAmelCase		(      self							)					->  List[Any]:







            '''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    _lowerCAmelCase		(      self							)					->  Optional[int]:







            '''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=lowercase_ ,		initializer_range=self.initializer_range ,		)




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__							)					->  int:







            '''simple docstring'''





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




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		)					->  Union[str, Any]:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptForCausalLM(config=lowercase_							)
            model.to(lowercase_							)
            model.eval()
            lowerCamelCase_       							=		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    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		*UpperCamelCase__							)					->  Tuple:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptModel(config=lowercase_							)
            model.to(lowercase_							)
            model.eval()

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

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

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




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		*UpperCamelCase__							)					->  List[str]:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptModel(config=lowercase_							).to(lowercase_							).eval()

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

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

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




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		*UpperCamelCase__ ,		UpperCamelCase__=False							)					->  Tuple:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptForCausalLM(lowercase_							)
            model.to(lowercase_							)
            if gradient_checkpointing:
                   model.gradient_checkpointing_enable()

            lowerCamelCase_       							=		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    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		*UpperCamelCase__							)					->  Optional[int]:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptModel(lowercase_							)
            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    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		*UpperCamelCase__							)					->  Any:







            '''simple docstring'''





            lowerCamelCase_       							=		self.num_labels
            lowerCamelCase_       							=		BioGptForTokenClassification(lowercase_							)
            model.to(lowercase_							)
            model.eval()
            lowerCamelCase_       							=		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    _lowerCAmelCase		(      self							)					->  Optional[Any]:







            '''simple docstring'''





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





@require_torch
class 					lowerCAmelCase			(				UpperCamelCase_     ,						UpperCamelCase_     ,						UpperCamelCase_     ,						unittest.TestCase					):
     """simple docstring"""







     __lowercase						:List[Any]          =    (
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     __lowercase						:Optional[Any]          =    (BioGptForCausalLM,) if is_torch_available() else ()
     __lowercase						:Any          =    (
         {
             'feature-extraction': BioGptModel,
             'text-classification': BioGptForSequenceClassification,
             'text-generation': BioGptForCausalLM,
             'token-classification': BioGptForTokenClassification,
             'zero-shot': BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __lowercase						:Any          =    False




     def    _lowerCAmelCase		(      self							)					->  Optional[Any]:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptModelTester(self							)
            lowerCamelCase_       							=		ConfigTester(self ,		config_class=lowercase_ ,		hidden_size=37							)




     def    _lowerCAmelCase		(      self							)					->  Any:







            '''simple docstring'''





            self.config_tester.run_common_tests()




     def    _lowerCAmelCase		(      self							)					->  int:







            '''simple docstring'''





            lowerCamelCase_       							=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase_							)




     def    _lowerCAmelCase		(      self							)					->  int:







            '''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(*lowercase_							)




     def    _lowerCAmelCase		(      self							)					->  List[str]:







            '''simple docstring'''





            lowerCamelCase_       							=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowercase_							)




     def    _lowerCAmelCase		(      self							)					->  Optional[int]:







            '''simple docstring'''





            lowerCamelCase_       							=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*lowercase_ ,		gradient_checkpointing=lowercase_							)




     def    _lowerCAmelCase		(      self							)					->  str:







            '''simple docstring'''





            lowerCamelCase_       							=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowercase_							)




     def    _lowerCAmelCase		(      self							)					->  List[Any]:







            '''simple docstring'''





            lowerCamelCase_       							=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*lowercase_							)




     def    _lowerCAmelCase		(      self							)					->  List[Any]:







            '''simple docstring'''





            lowerCamelCase_       							=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*lowercase_							)




     @slow
     def    _lowerCAmelCase		(      self							)					->  Optional[int]:







            '''simple docstring'''





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

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

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

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

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

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




     @slow
     def    _lowerCAmelCase		(      self							)					->  List[str]:







            '''simple docstring'''





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




     def    _lowerCAmelCase		(      self							)					->  Union[str, Any]:







            '''simple docstring'''





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




     def    _lowerCAmelCase		(      self							)					->  Tuple:







            '''simple docstring'''





            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(lowercase_							)
            lowerCamelCase_       							=		ids_tensor(
                [self.model_tester.batch_size, config.num_labels] ,		self.model_tester.type_sequence_label_size							).to(torch.float							)
            lowerCamelCase_       							=		BioGptForSequenceClassification(lowercase_							)
            model.to(lowercase_							)
            model.eval()
            lowerCamelCase_       							=		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    _lowerCAmelCase		(      self							)					->  Any:







            '''simple docstring'''





            lowerCamelCase_       							=		BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''							)
            lowerCamelCase_       							=		torch.tensor([[2, 4_805, 9, 656, 21]]							)
            lowerCamelCase_       							=		model(lowercase_							)[0]

            lowerCamelCase_       							=		42_384

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

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




     @slow
     def    _lowerCAmelCase		(      self							)					->  Union[str, Any]:







            '''simple docstring'''





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

            torch.manual_seed(0							)
            lowerCamelCase_       							=		tokenizer('''COVID-19 is''' ,		return_tensors='''pt'''							).to(lowercase_							)
            lowerCamelCase_       							=		model.generate(
                **lowercase_ ,		min_length=100 ,		max_length=1_024 ,		num_beams=5 ,		early_stopping=lowercase_ ,		)
            lowerCamelCase_       							=		tokenizer.decode(output_ids[0] ,		skip_special_tokens=lowercase_							)

            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(lowercase_ ,		lowercase_							)



import numpy as np




def       a_ (       SCREAMING_SNAKE_CASE__   : np.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       return np.where(vector > 0						,     SCREAMING_SNAKE_CASE__						,     (alpha * (np.exp(SCREAMING_SNAKE_CASE__	) - 1))	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


