

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 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=7					, A__=True					, A__=True					, A__=False					, A__=True					, A__=99					, A__=32					, A__=5					, A__=4					, A__=37					, A__="gelu"					, A__=0.1					, A__=0.1					, A__=512					, A__=16					, A__=2					, A__=0.0_2					, A__=3					, A__=4					, A__=None					, ):
						A__							:					List[Any]       =	parent
						A__							:					str       =	batch_size
						A__							:					List[Any]       =	seq_length
						A__							:					str       =	is_training
						A__							:					Tuple       =	use_input_mask
						A__							:					List[Any]       =	use_token_type_ids
						A__							:					Tuple       =	use_labels
						A__							:					Dict       =	vocab_size
						A__							:					List[str]       =	hidden_size
						A__							:					Union[str, Any]       =	num_hidden_layers
						A__							:					Optional[Any]       =	num_attention_heads
						A__							:					Any       =	intermediate_size
						A__							:					Optional[int]       =	hidden_act
						A__							:					Dict       =	hidden_dropout_prob
						A__							:					Dict       =	attention_probs_dropout_prob
						A__							:					Optional[Any]       =	max_position_embeddings
						A__							:					List[str]       =	type_vocab_size
						A__							:					Optional[int]       =	type_sequence_label_size
						A__							:					Dict       =	initializer_range
						A__							:					Union[str, Any]       =	num_labels
						A__							:					List[str]       =	num_choices
						A__							:					Union[str, Any]       =	scope






					def 						__A    (				self    ):
						A__							:					Tuple       =	ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size    )

						A__							:					List[Any]       =	None
						if self.use_input_mask:
							A__							:					List[Any]       =	random_attention_mask([self.batch_size, self.seq_length]    )

						A__							:					Optional[Any]       =	None
						if self.use_token_type_ids:
							A__							:					List[Any]       =	ids_tensor([self.batch_size, self.seq_length]					, self.type_vocab_size    )

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

						A__							:					Dict       =	self.get_config()

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






					def 						__A    (				self    ):
						return BioGptConfig(
						    vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, intermediate_size=self.intermediate_size					, hidden_act=self.hidden_act					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, type_vocab_size=self.type_vocab_size					, is_decoder=A__					, initializer_range=self.initializer_range					, )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__    ):
						A__							:					Optional[Any]       =	BioGptModel(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Optional[Any]       =	model(A__					, attention_mask=A__    )
						A__							:					Optional[Any]       =	model(A__    )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, ):
						A__							:					List[str]       =	BioGptForCausalLM(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Optional[Any]       =	model(A__					, attention_mask=A__					, token_type_ids=A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, *A__    ):
						A__							:					str       =	BioGptModel(config=A__    )
						model.to(A__    )
						model.eval()

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

						# first forward pass
						A__     ,     A__							:					List[str]       =	model(A__					, attention_mask=A__    ).to_tuple()

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

						# change a random masked slice from input_ids
						A__							:					str       =	ids_tensor((1,)					, A__    ).item() + 1
						A__							:					List[str]       =	ids_tensor((self.batch_size, 1)					, config.vocab_size    ).squeeze(-1    )
						A__							:					Dict       =	random_other_next_tokens

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

						# get two different outputs
						A__							:					Any       =	model(A__					, attention_mask=A__    )["""last_hidden_state"""]
						A__							:					Optional[int]       =	model(A__					, past_key_values=A__					, attention_mask=A__    )["""last_hidden_state"""]

						# select random slice
						A__							:					Optional[Any]       =	ids_tensor((1,)					, output_from_past.shape[-1]    ).item()
						A__							:					Optional[int]       =	output_from_no_past[:, -1, random_slice_idx].detach()
						A__							:					List[str]       =	output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(A__					, A__					, atol=1e-3    )    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, *A__    ):
						A__							:					Optional[Any]       =	BioGptModel(config=A__    ).to(A__    ).eval()

						A__							:					int       =	torch.ones(input_ids.shape					, dtype=torch.long					, device=A__    )

						# first forward pass
						A__							:					int       =	model(A__					, attention_mask=A__					, use_cache=A__    )

						A__     ,     A__							:					Union[str, Any]       =	outputs.to_tuple()

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

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

						A__							:					str       =	model(A__					, attention_mask=A__    )["""last_hidden_state"""]
						A__							:					Optional[int]       =	model(A__					, attention_mask=A__					, past_key_values=A__    )[
						    """last_hidden_state"""
						]

						# select random slice
						A__							:					List[str]       =	ids_tensor((1,)					, output_from_past.shape[-1]    ).item()
						A__							:					int       =	output_from_no_past[:, -3:, random_slice_idx].detach()
						A__							:					List[Any]       =	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(A__					, A__					, atol=1e-3    )    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, *A__					, A__=False    ):
						A__							:					Optional[int]       =	BioGptForCausalLM(A__    )
						model.to(A__    )
						if gradient_checkpointing:
							model.gradient_checkpointing_enable()

						A__							:					str       =	model(A__					, labels=A__    )
						self.parent.assertEqual(result.loss.shape					, ()    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)    )
						result.loss.backward()






					def 						__A    (				self					, A__					, *A__    ):
						A__							:					Optional[Any]       =	BioGptModel(A__    )
						A__							:					str       =	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_0_1    )
								self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]    ) - 0.0    )					, 0.0_1    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, *A__    ):
						A__							:					List[str]       =	self.num_labels
						A__							:					str       =	BioGptForTokenClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					Any       =	model(A__					, attention_mask=A__					, token_type_ids=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.num_labels)    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.prepare_config_and_inputs()
						(
						    (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     
						)							:					List[str]       =	config_and_inputs
						A__							:					int       =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_torch
class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






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






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	BioGptModelTester(self    )
						A__							:					Optional[int]       =	ConfigTester(self					, config_class=A__					, hidden_size=37    )






					def 						__A    (				self    ):
						self.config_tester.run_common_tests()






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
							A__							:					Optional[Any]       =	type
							self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A__    )






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*A__					, gradient_checkpointing=A__    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A__    )






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*A__    )






					@slow
					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""    )
						model.to(A__    )
						A__							:					Tuple       =	BioGptTokenizer.from_pretrained("""microsoft/biogpt"""    )

						A__							:					Optional[Any]       =	"""left"""

						# Define PAD Token = EOS Token = 50256
						A__							:					Dict       =	tokenizer.eos_token
						A__							:					Dict       =	model.config.eos_token_id

						# use different length sentences to test batching
						A__							:					Union[str, Any]       =	[
						    """Hello, my dog is a little""",
						    """Today, I""",
						]

						A__							:					int       =	tokenizer(A__					, return_tensors="""pt"""					, padding=A__    )
						A__							:					Union[str, Any]       =	inputs["""input_ids"""].to(A__    )

						A__							:					Optional[Any]       =	model.generate(
						    input_ids=A__					, attention_mask=inputs["""attention_mask"""].to(A__    )					, )

						A__							:					str       =	tokenizer(sentences[0]					, return_tensors="""pt"""    ).input_ids.to(A__    )
						A__							:					Dict       =	model.generate(input_ids=A__    )

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

						A__							:					Union[str, Any]       =	tokenizer.batch_decode(A__					, skip_special_tokens=A__    )
						A__							:					Any       =	tokenizer.decode(output_non_padded[0]					, skip_special_tokens=A__    )
						A__							:					int       =	tokenizer.decode(output_padded[0]					, skip_special_tokens=A__    )

						A__							:					Optional[Any]       =	[
						    """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(A__					, A__    )
						self.assertListEqual(A__					, [non_padded_sentence, padded_sentence]    )






					@slow
					def 						__A    (				self    ):
						for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:					int       =	BioGptModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )






					def 						__A    (				self    ):
						A__     ,     A__							:					int       =	self.model_tester.prepare_config_and_inputs_for_common()
						A__							:					str       =	3
						A__							:					Union[str, Any]       =	input_dict["""input_ids"""]
						A__							:					List[str]       =	input_ids.ne(1    ).to(A__    )
						A__							:					List[Any]       =	ids_tensor([self.model_tester.batch_size]					, self.model_tester.type_sequence_label_size    )
						A__							:					str       =	BioGptForSequenceClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					List[Any]       =	model(A__					, attention_mask=A__					, labels=A__    )
						self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)    )






					def 						__A    (				self    ):
						A__     ,     A__							:					Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
						A__							:					Any       =	3
						A__							:					Tuple       =	"""multi_label_classification"""
						A__							:					Tuple       =	input_dict["""input_ids"""]
						A__							:					Optional[int]       =	input_ids.ne(1    ).to(A__    )
						A__							:					List[Any]       =	ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]					, self.model_tester.type_sequence_label_size    ).to(torch.float    )
						A__							:					List[str]       =	BioGptForSequenceClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					Any       =	model(A__					, attention_mask=A__					, labels=A__    )
						self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)    )



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					@slow
					def 						__A    (				self    ):
						A__							:					int       =	BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""    )
						A__							:					List[str]       =	torch.tensor([[2, 4805, 9, 656, 21]]    )
						A__							:					Union[str, Any]       =	model(A__    )[0]

						A__							:					List[str]       =	4_2384

						A__							:					Tuple       =	torch.Size((1, 5, vocab_size)    )
						self.assertEqual(output.shape					, A__    )

						A__							:					List[str]       =	torch.tensor(
						    [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]    )

						self.assertTrue(torch.allclose(output[:, :3, :3]					, A__					, atol=1e-4    )    )






					@slow
					def 						__A    (				self    ):
						A__							:					List[str]       =	BioGptTokenizer.from_pretrained("""microsoft/biogpt"""    )
						A__							:					int       =	BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""    )
						model.to(A__    )

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

						A__							:					Union[str, Any]       =	(
						    """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(A__					, A__    )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A_ :				Optional[Any]						 =    {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_ :				str						 =    ['BartphoTokenizer']

if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bartpho import BartphoTokenizer

else:
	import sys

	A_ :				Optional[int]						 =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



