




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



					def     __snake_case						(	self     :						Union[str, Any])    ->							int:
								A_									= ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size)

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

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

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

								A_									= self.get_config()

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



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



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						str      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Any      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						int      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						List[Any]      ,       )    ->							Union[str, Any]:
								A_									= BioGptForCausalLM(config=_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= 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     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						Any      ,       _lowercase     :						Tuple      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[Any]      ,       *_lowercase     :						List[Any])    ->							List[Any]:
								A_									= BioGptModel(config=_lowercase)
								model.to(_lowercase)
								model.eval()

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

								# first forward pass
								A_		, A_									= model(_lowercase      ,       attention_mask=_lowercase).to_tuple()

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

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

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

								# get two different outputs
								A_									= model(_lowercase      ,       attention_mask=_lowercase)['last_hidden_state']
								A_									= model(_lowercase      ,       past_key_values=_lowercase      ,       attention_mask=_lowercase)['last_hidden_state']

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

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

								# first forward pass
								A_									= model(_lowercase      ,       attention_mask=_lowercase      ,       use_cache=_lowercase)

								A_		, A_									= outputs.to_tuple()

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

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

								A_									= model(_lowercase      ,       attention_mask=_lowercase)['last_hidden_state']
								A_									= model(_lowercase      ,       attention_mask=_lowercase      ,       past_key_values=_lowercase)[
								    'last_hidden_state'
								]

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

								A_									= 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     __snake_case						(	self     :						int      ,       _lowercase     :						List[str]      ,       *_lowercase     :						Optional[int])    ->							Union[str, Any]:
								A_									= BioGptModel(_lowercase)
								A_									= 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     __snake_case						(	self     :						str      ,       _lowercase     :						List[Any]      ,       _lowercase     :						str      ,       _lowercase     :						Tuple      ,       _lowercase     :						Tuple      ,       _lowercase     :						Optional[Any]      ,       *_lowercase     :						Tuple)    ->							Any:
								A_									= self.num_labels
								A_									= BioGptForTokenClassification(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= 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     __snake_case						(	self     :						List[str])    ->							List[str]:
								A_									= self.prepare_config_and_inputs()
								(
								    (
								    A_
								)		, (
								    A_
								)		, (
								    A_
								)		, (
								    A_
								)		, (
								    A_
								)		, (
								    A_
								)		, (
								    A_
								)		, 
								)									= config_and_inputs
								A_									= {'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict





@require_torch
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase			,lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_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     __snake_case						(	self     :						Optional[int])    ->							Tuple:
								A_									= BioGptModelTester(self)
								A_									= ConfigTester(self      ,       config_class=_lowercase      ,       hidden_size=37)



					def     __snake_case						(	self     :						List[str])    ->							int:
								self.config_tester.run_common_tests()



					def     __snake_case						(	self     :						Optional[int])    ->							Union[str, Any]:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase)



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								A_									= self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
											A_									= type
											self.model_tester.create_and_check_model(*_lowercase)



					def     __snake_case						(	self     :						Optional[Any])    ->							int:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowercase)



					def     __snake_case						(	self     :						int)    ->							Tuple:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_forward_and_backwards(*_lowercase      ,       gradient_checkpointing=_lowercase)



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowercase)



					def     __snake_case						(	self     :						Tuple)    ->							Optional[Any]:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_weight_initialization(*_lowercase)



					def     __snake_case						(	self     :						List[Any])    ->							int:
								A_									= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_for_token_classification(*_lowercase)



					@slow
					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[Any]:
								A_									= BioGptForCausalLM.from_pretrained('microsoft/biogpt')
								model.to(_lowercase)
								A_									= BioGptTokenizer.from_pretrained('microsoft/biogpt')

								A_									= 'left'

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

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

								A_									= tokenizer(_lowercase      ,       return_tensors='pt'      ,       padding=_lowercase)
								A_									= inputs['input_ids'].to(_lowercase)

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

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

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

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

								A_									= [
								    '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     __snake_case						(	self     :						Optional[int])    ->							List[Any]:
								for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A_									= BioGptModel.from_pretrained(_lowercase)
											self.assertIsNotNone(_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()
								A_									= 3
								A_									= input_dict['input_ids']
								A_									= input_ids.ne(1).to(_lowercase)
								A_									= ids_tensor([self.model_tester.batch_size]      ,       self.model_tester.type_sequence_label_size)
								A_									= BioGptForSequenceClassification(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= model(_lowercase      ,       attention_mask=_lowercase      ,       labels=_lowercase)
								self.assertEqual(result.logits.shape      ,       (self.model_tester.batch_size, self.model_tester.num_labels))



					def     __snake_case						(	self     :						List[Any])    ->							Tuple:
								A_		, A_									= self.model_tester.prepare_config_and_inputs_for_common()
								A_									= 3
								A_									= 'multi_label_classification'
								A_									= input_dict['input_ids']
								A_									= input_ids.ne(1).to(_lowercase)
								A_									= ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]      ,       self.model_tester.type_sequence_label_size).to(torch.float)
								A_									= BioGptForSequenceClassification(_lowercase)
								model.to(_lowercase)
								model.eval()
								A_									= 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   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@slow
					def     __snake_case						(	self     :						str)    ->							Optional[Any]:
								A_									= BioGptForCausalLM.from_pretrained('microsoft/biogpt')
								A_									= torch.tensor([[2, 4_805, 9, 656, 21]])
								A_									= model(_lowercase)[0]

								A_									= 42_384

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

								A_									= 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     __snake_case						(	self     :						Tuple)    ->							str:
								A_									= BioGptTokenizer.from_pretrained('microsoft/biogpt')
								A_									= BioGptForCausalLM.from_pretrained('microsoft/biogpt')
								model.to(_lowercase)

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

								A_									= (
								    '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 json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		CTRLTokenizer
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A_									= ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))
								A_									= ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
								A_									= {'unk_token': '<unk>'}

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')
								with open(self.merges_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write('\n'.join(_lowercase))



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any])    ->							Dict:
								A_									= 'adapt react readapt apt'
								A_									= 'adapt react readapt apt'
								return input_text, output_text



					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= CTRLTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map)
								A_									= 'adapt react readapt apt'
								A_									= 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
								A_									= tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokens + [tokenizer.unk_token]

								A_									= [0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)      ,       _lowercase)




