



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					:



			def __init__(		self     :						Optional[int]				,	A     :						str				,	A     :						Optional[Any]=13				,	A     :						Tuple=7				,	A     :						Dict=True				,	A     :						Any=True				,	A     :						int=False				,	A     :						Optional[Any]=True				,	A     :						Optional[int]=99				,	A     :						str=32				,	A     :						Tuple=5				,	A     :						Dict=4				,	A     :						List[str]=37				,	A     :						List[str]="gelu"				,	A     :						Any=0.1				,	A     :						List[Any]=0.1				,	A     :						Tuple=5_12				,	A     :						Dict=16				,	A     :						List[str]=2				,	A     :						Optional[Any]=0.0_2				,	A     :						str=3				,	A     :						Tuple=4				,	A     :						int=None				,	)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     seq_length
						_UpperCAmelCase	 =     is_training
						_UpperCAmelCase	 =     use_input_mask
						_UpperCAmelCase	 =     use_token_type_ids
						_UpperCAmelCase	 =     use_labels
						_UpperCAmelCase	 =     vocab_size
						_UpperCAmelCase	 =     hidden_size
						_UpperCAmelCase	 =     num_hidden_layers
						_UpperCAmelCase	 =     num_attention_heads
						_UpperCAmelCase	 =     intermediate_size
						_UpperCAmelCase	 =     hidden_act
						_UpperCAmelCase	 =     hidden_dropout_prob
						_UpperCAmelCase	 =     attention_probs_dropout_prob
						_UpperCAmelCase	 =     max_position_embeddings
						_UpperCAmelCase	 =     type_vocab_size
						_UpperCAmelCase	 =     type_sequence_label_size
						_UpperCAmelCase	 =     initializer_range
						_UpperCAmelCase	 =     num_labels
						_UpperCAmelCase	 =     num_choices
						_UpperCAmelCase	 =     scope



			def 					_lowerCamelCase (		self     :						List[Any])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)

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

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

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

						_UpperCAmelCase	 =     self.get_config()

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



			def 					_lowerCamelCase (		self     :						List[str])     ->					Dict:



						"""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 					_lowerCamelCase (		self     :						str				,	A     :						Optional[Any]				,	A     :						int				,	A     :						Any				,	A     :						List[Any]				,	A     :						Tuple				,	A     :						List[str]				,	A     :						Dict)     ->					str:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						Optional[Any]				,	A     :						List[str]				,	A     :						Union[str, Any]				,	A     :						int				,	A     :						Optional[int]				,	A     :						Dict				,	A     :						Tuple				,	A     :						Union[str, Any]				,	A     :						Optional[int]				,	)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     BioGptForCausalLM(config=lowerCamelCase_)
						model.to(lowerCamelCase_)
						model.eval()
						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Dict				,	A     :						str				,	A     :						Optional[int]				,	A     :						Tuple				,	A     :						Dict				,	A     :						List[Any]				,	*A     :						Union[str, Any])     ->					Optional[int]:



						"""simple docstring"""






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

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

						# first forward pass
						_UpperCAmelCase	 =     model(lowerCamelCase_				,	attention_mask=lowerCamelCase_).to_tuple()

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

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

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

						# get two different outputs
						_UpperCAmelCase	 =     model(lowerCamelCase_				,	attention_mask=lowerCamelCase_)["""last_hidden_state"""]
						_UpperCAmelCase	 =     model(lowerCamelCase_				,	past_key_values=lowerCamelCase_				,	attention_mask=lowerCamelCase_)["""last_hidden_state"""]

						# select random slice
						_UpperCAmelCase	 =     ids_tensor((1,)				,	output_from_past.shape[-1]).item()
						_UpperCAmelCase	 =     output_from_no_past[:, -1, random_slice_idx].detach()
						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						Optional[Any]				,	A     :						Any				,	A     :						int				,	A     :						Tuple				,	*A     :						int)     ->					List[Any]:



						"""simple docstring"""






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

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

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

						_UpperCAmelCase	 =     outputs.to_tuple()

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

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

						_UpperCAmelCase	 =     model(lowerCamelCase_				,	attention_mask=lowerCamelCase_)["""last_hidden_state"""]
						_UpperCAmelCase	 =     model(lowerCamelCase_				,	attention_mask=lowerCamelCase_				,	past_key_values=lowerCamelCase_)[
						    """last_hidden_state"""
						]

						# select random slice
						_UpperCAmelCase	 =     ids_tensor((1,)				,	output_from_past.shape[-1]).item()
						_UpperCAmelCase	 =     output_from_no_past[:, -3:, random_slice_idx].detach()
						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						int				,	A     :						Union[str, Any]				,	A     :						Union[str, Any]				,	A     :						Union[str, Any]				,	A     :						Union[str, Any]				,	*A     :						Tuple				,	A     :						Optional[int]=False)     ->					Dict:



						"""simple docstring"""






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

						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						str				,	*A     :						int)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     BioGptModel(lowerCamelCase_)
						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						str				,	A     :						Union[str, Any]				,	A     :						Any				,	A     :						str				,	A     :						str				,	A     :						Any				,	*A     :						Any)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.num_labels
						_UpperCAmelCase	 =     BioGptForTokenClassification(lowerCamelCase_)
						model.to(lowerCamelCase_)
						model.eval()
						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Optional[int])     ->					Any:



						"""simple docstring"""






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







@require_torch
class     __lowerCAmelCase					(    lowercase_       ,		lowercase_       ,		lowercase_       ,		unittest.TestCase		):
			UpperCamelCase           =  (
			    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase           =  (BioGptForCausalLM,) if is_torch_available() else ()
			UpperCamelCase           =  (
			    {
			        '''feature-extraction''': BioGptModel,
			        '''text-classification''': BioGptForSequenceClassification,
			        '''text-generation''': BioGptForCausalLM,
			        '''token-classification''': BioGptForTokenClassification,
			        '''zero-shot''': BioGptForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase           =  False



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						self.config_tester.run_common_tests()



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Tuple)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									_UpperCAmelCase	 =     type
									self.model_tester.create_and_check_model(*lowerCamelCase_)



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					List[Any]:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Any:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Dict)     ->					List[Any]:



						"""simple docstring"""






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



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



						"""simple docstring"""






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

						_UpperCAmelCase	 =     """left"""

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

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

						_UpperCAmelCase	 =     tokenizer(lowerCamelCase_				,	return_tensors='pt'				,	padding=lowerCamelCase_)
						_UpperCAmelCase	 =     inputs["""input_ids"""].to(lowerCamelCase_)

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

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

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

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

						_UpperCAmelCase	 =     [
						    """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 					_lowerCamelCase (		self     :						Dict)     ->					Optional[int]:



						"""simple docstring"""






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



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()
						_UpperCAmelCase	 =     3
						_UpperCAmelCase	 =     """multi_label_classification"""
						_UpperCAmelCase	 =     input_dict["""input_ids"""]
						_UpperCAmelCase	 =     input_ids.ne(1).to(lowerCamelCase_)
						_UpperCAmelCase	 =     ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]				,	self.model_tester.type_sequence_label_size).to(torch.float)
						_UpperCAmelCase	 =     BioGptForSequenceClassification(lowerCamelCase_)
						model.to(lowerCamelCase_)
						model.eval()
						_UpperCAmelCase	 =     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		):



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



						"""simple docstring"""






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

						_UpperCAmelCase	 =     4_23_84

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

						_UpperCAmelCase	 =     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]				,	lowerCamelCase_				,	atol=1E-4))



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



						"""simple docstring"""






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

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

						_UpperCAmelCase	 =     (
						    """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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__     = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = ["YolosFeatureExtractor"]
							UpperCAmelCase__     = ["YolosImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "YolosForObjectDetection",
							    "YolosModel",
							    "YolosPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
