




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			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=7			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=False			,						lowerCamelCase__=True			,						lowerCamelCase__=99			,						lowerCamelCase__=32			,						lowerCamelCase__=5			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=512			,						lowerCamelCase__=16			,						lowerCamelCase__=2			,						lowerCamelCase__=0.02			,						lowerCamelCase__=3			,						lowerCamelCase__=4			,						lowerCamelCase__=None			,						)    ->      Any:



							'''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       lowercase_    (      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       lowercase_    (      self							)    ->      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=lowerCamelCase__			,						initializer_range=self.initializer_range			,						)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__							)
							__lowerCamelCase		       =    model(lowerCamelCase__							)
							self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptForCausalLM(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)
							self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						*lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()

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

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

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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						*lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptModel(config=lowerCamelCase__							).to(lowerCamelCase__							).eval()

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

							# first forward pass
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						use_cache=lowerCamelCase__							)

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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						*lowerCamelCase__			,						lowerCamelCase__=False							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptForCausalLM(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							if gradient_checkpointing:
											model.gradient_checkpointing_enable()

							__lowerCamelCase		       =    model(lowerCamelCase__			,						labels=lowerCamelCase__							)
							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			,						lowerCamelCase__			,						*lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptModel(lowerCamelCase__							)
							__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							)			,						0.0_01							)
															self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]							) - 0.0							)			,						0.01							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						*lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.num_labels
							__lowerCamelCase		       =    BioGptForTokenClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__							)
							self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)							)






			def       lowercase_    (      self							)    ->      str:



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







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

			"""simple docstring"""







			snake_case_   		=       (
			    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
			    if is_torch_available()
			    else ()
			)
			snake_case_   		=       (BioGptForCausalLM,) if is_torch_available() else ()
			snake_case_   		=       (
			    {
			        '''feature-extraction''': BioGptModel,
			        '''text-classification''': BioGptForSequenceClassification,
			        '''text-generation''': BioGptForCausalLM,
			        '''token-classification''': BioGptForTokenClassification,
			        '''zero-shot''': BioGptForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			snake_case_   		=       False


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



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=37							)


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



							'''simple docstring'''
							self.config_tester.run_common_tests()


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Any:



							'''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(*lowerCamelCase__							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase__			,						gradient_checkpointing=lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase__							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase__							)


			@slow
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'							)
							model.to(lowerCamelCase__							)
							__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(lowerCamelCase__			,						return_tensors='pt'			,						padding=lowerCamelCase__							)
							__lowerCamelCase		       =    inputs['input_ids'].to(lowerCamelCase__							)

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

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

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

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

							__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(lowerCamelCase__			,						lowerCamelCase__							)
							self.assertListEqual(lowerCamelCase__			,						[non_padded_sentence, padded_sentence]							)


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



							'''simple docstring'''
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    BioGptModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)


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



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






			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase       ,						__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(lowerCamelCase__							)
							__lowerCamelCase		       =    ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]			,						self.model_tester.type_sequence_label_size							).to(torch.float							)
							__lowerCamelCase		       =    BioGptForSequenceClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						labels=lowerCamelCase__							)
							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       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'							)
							__lowerCamelCase		       =    torch.tensor([[2, 4_805, 9, 656, 21]]							)
							__lowerCamelCase		       =    model(lowerCamelCase__							)[0]

							__lowerCamelCase		       =    42_384

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

							__lowerCamelCase		       =    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]			,						lowerCamelCase__			,						atol=1e-4							)							)






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



							'''simple docstring'''
							__lowerCamelCase		       =    BioGptTokenizer.from_pretrained('microsoft/biogpt'							)
							__lowerCamelCase		       =    BioGptForCausalLM.from_pretrained('microsoft/biogpt'							)
							model.to(lowerCamelCase__							)

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

							__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(lowerCamelCase__			,						lowerCamelCase__							)








from ..utils import DummyObject, requires_backends







class     __lowerCAmelCase (       metaclass=__magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''onnx''']


			def __init__(      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Optional[Any]:



							'''simple docstring'''
							requires_backends(self			,						['onnx']							)


			@classmethod
			def       lowercase_    (      cls			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							requires_backends(cls			,						['onnx']							)






			@classmethod
			def       lowercase_    (      cls			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							requires_backends(cls			,						['onnx']							)


