"""simple docstring"""







import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
							from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor



class  _lowerCamelCase			(__snake_case     ):







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							self.config_class(**self.inputs_dict						)
					self.parent.assertTrue(hasattr(__UpperCamelCase			,					'hidden_sizes'						)						)
					self.parent.assertTrue(hasattr(__UpperCamelCase			,					'neck_hidden_sizes'						)						)
					self.parent.assertTrue(hasattr(__UpperCamelCase			,					'num_attention_heads'						)						)



class  _lowerCamelCase			:







				def __init__( self    :       Dict			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       int=1_3			,					lowerCamelCase_    :       int=3_2			,					lowerCamelCase_    :       int=2			,					lowerCamelCase_    :       Union[str, Any]=3			,					lowerCamelCase_    :       List[Any]=6_4_0			,					lowerCamelCase_    :       Optional[Any]=4			,					lowerCamelCase_    :       Any="silu"			,					lowerCamelCase_    :       str=3			,					lowerCamelCase_    :       int=3_2			,					lowerCamelCase_    :       int=0.1			,					lowerCamelCase_    :       Optional[int]=0.1			,					lowerCamelCase_    :       Union[str, Any]=0.1			,					lowerCamelCase_    :       Any=0.02			,					lowerCamelCase_    :       Union[str, Any]=True			,					lowerCamelCase_    :       int=True			,					lowerCamelCase_    :       Optional[Any]=1_0			,					lowerCamelCase_    :       Any=None			,					):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							parent
					_lowercase :    str							=							batch_size
					_lowercase :    Optional[Any]							=							image_size
					_lowercase :    Union[str, Any]							=							patch_size
					_lowercase :    Optional[Any]							=							num_channels
					_lowercase :    List[str]							=							last_hidden_size
					_lowercase :    Union[str, Any]							=							num_attention_heads
					_lowercase :    Union[str, Any]							=							hidden_act
					_lowercase :    str							=							conv_kernel_size
					_lowercase :    Tuple							=							output_stride
					_lowercase :    Optional[int]							=							hidden_dropout_prob
					_lowercase :    Optional[Any]							=							attention_probs_dropout_prob
					_lowercase :    Any							=							classifier_dropout_prob
					_lowercase :    Tuple							=							use_labels
					_lowercase :    str							=							is_training
					_lowercase :    Dict							=							num_labels
					_lowercase :    Any							=							initializer_range
					_lowercase :    str							=							scope







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    int							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					_lowercase :    Any							=							None
					_lowercase :    Dict							=							None
					if self.use_labels:
						_lowercase :    Optional[int]							=							ids_tensor([self.batch_size]			,					self.num_labels						)
						_lowercase :    str							=							ids_tensor([self.batch_size, self.image_size, self.image_size]			,					self.num_labels						)

					_lowercase :    Optional[int]							=							self.get_config()

					return config, pixel_values, labels, pixel_labels







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return MobileViTConfig(
					    image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					num_attention_heads=self.num_attention_heads			,					hidden_act=self.hidden_act			,					conv_kernel_size=self.conv_kernel_size			,					output_stride=self.output_stride			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					classifier_dropout_prob=self.classifier_dropout_prob			,					initializer_range=self.initializer_range			,					)







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							MobileViTModel(config=__UpperCamelCase						)
					model.to(__UpperCamelCase						)
					model.eval()
					_lowercase :    Tuple							=							model(__UpperCamelCase						)
					self.parent.assertEqual(
					    result.last_hidden_state.shape			,					(
					        self.batch_size,
					        self.last_hidden_size,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )			,					)







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    Any							=							self.num_labels
					_lowercase :    List[Any]							=							MobileViTForImageClassification(__UpperCamelCase						)
					model.to(__UpperCamelCase						)
					model.eval()
					_lowercase :    Union[str, Any]							=							model(__UpperCamelCase			,					labels=__UpperCamelCase						)
					self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)						)







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    Tuple							=							self.num_labels
					_lowercase :    Union[str, Any]							=							MobileViTForSemanticSegmentation(__UpperCamelCase						)
					model.to(__UpperCamelCase						)
					model.eval()
					_lowercase :    Any							=							model(__UpperCamelCase						)
					self.parent.assertEqual(
					    result.logits.shape			,					(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )			,					)
					_lowercase :    Tuple							=							model(__UpperCamelCase			,					labels=__UpperCamelCase						)
					self.parent.assertEqual(
					    result.logits.shape			,					(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )			,					)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							self.prepare_config_and_inputs()
					_lowercase				,      _lowercase				,      _lowercase				,      _lowercase :    List[Any]							=							config_and_inputs
					_lowercase :    Union[str, Any]							=							{'pixel_values': pixel_values}
					return config, inputs_dict



@require_torch
class  _lowerCamelCase			(__snake_case						,							__snake_case						,							unittest.TestCase     ):
				_snake_case     =  (
				    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				_snake_case     =  (
				    {
				        "feature-extraction": MobileViTModel,
				        "image-classification": MobileViTForImageClassification,
				        "image-segmentation": MobileViTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							MobileViTModelTester(self						)
					_lowercase :    Union[str, Any]							=							MobileViTConfigTester(self			,					config_class=__UpperCamelCase			,					has_text_modality=__UpperCamelCase						)







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					self.config_tester.run_common_tests()







				@unittest.skip(reason='MobileViT does not use inputs_embeds'						)
				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					pass







				@unittest.skip(reason='MobileViT does not support input and output embeddings'						)
				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					pass







				@unittest.skip(reason='MobileViT does not output attentions'						)
				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					pass







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase				,      _lowercase :    int							=							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase :    Tuple							=							model_class(__UpperCamelCase						)
						_lowercase :    Any							=							inspect.signature(model.forward						)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowercase :    Union[str, Any]							=							[*signature.parameters.keys()]

						_lowercase :    Optional[int]							=							['pixel_values']
						self.assertListEqual(arg_names[:1]			,					__UpperCamelCase						)







				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					pass







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    int							=							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCamelCase						)







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					def check_hidden_states_output(lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Optional[Any]						):
						_lowercase :    int							=							model_class(__UpperCamelCase						)
						model.to(__UpperCamelCase						)
						model.eval()

						with torch.no_grad():
							_lowercase :    Tuple							=							model(**self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase						)						)

						_lowercase :    Dict							=							outputs.hidden_states

						_lowercase :    List[str]							=							5
						self.assertEqual(len(__UpperCamelCase						)			,					__UpperCamelCase						)

						# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
						# with the width and height being successively divided by 2.
						_lowercase :    Tuple							=							2
						for i in range(len(__UpperCamelCase						)						):
							self.assertListEqual(
							    list(hidden_states[i].shape[-2:]						)			,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,					)
							divisor *= 2

						self.assertEqual(self.model_tester.output_stride			,					divisor // 2						)

					_lowercase				,      _lowercase :    Tuple							=							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase :    Dict							=							True
						check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase						)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						_lowercase :    Dict							=							True

						check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Tuple							=							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase						)







				@slow
				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowercase :    Tuple							=							MobileViTModel.from_pretrained(__UpperCamelCase						)
						self.assertIsNotNone(__UpperCamelCase						)



def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    int							=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image







@require_torch
@require_vision
class  _lowerCamelCase			(unittest.TestCase     ):







				@cached_property
				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'						) if is_vision_available() else None







				@slow
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'						).to(__UpperCamelCase						)

					_lowercase :    Tuple							=							self.default_image_processor
					_lowercase :    List[str]							=							prepare_img()
					_lowercase :    Dict							=							image_processor(images=__UpperCamelCase			,					return_tensors='pt'						).to(__UpperCamelCase						)

					# forward pass
					with torch.no_grad():
						_lowercase :    Union[str, Any]							=							model(**__UpperCamelCase						)

					# verify the logits
					_lowercase :    Dict							=							torch.Size((1, 1_0_0_0)						)
					self.assertEqual(outputs.logits.shape			,					__UpperCamelCase						)

					_lowercase :    Optional[Any]							=							torch.tensor([-1.9364, -1.2327, -0.4653]						).to(__UpperCamelCase						)

					self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					__UpperCamelCase			,					atol=1E-4						)						)







				@slow
				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Dict							=							MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)
					_lowercase :    str							=							model.to(__UpperCamelCase						)

					_lowercase :    List[str]							=							MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)

					_lowercase :    Dict							=							prepare_img()
					_lowercase :    int							=							image_processor(images=__UpperCamelCase			,					return_tensors='pt'						).to(__UpperCamelCase						)

					# forward pass
					with torch.no_grad():
						_lowercase :    int							=							model(**__UpperCamelCase						)
					_lowercase :    List[Any]							=							outputs.logits

					# verify the logits
					_lowercase :    Tuple							=							torch.Size((1, 2_1, 3_2, 3_2)						)
					self.assertEqual(logits.shape			,					__UpperCamelCase						)

					_lowercase :    List[Any]							=							torch.tensor(
					    [
					        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
					        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
					        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
					    ]			,					device=__UpperCamelCase			,					)

					self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,					__UpperCamelCase			,					atol=1E-4						)						)







				@slow
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)
					_lowercase :    Optional[Any]							=							model.to(__UpperCamelCase						)

					_lowercase :    List[Any]							=							MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)

					_lowercase :    Optional[int]							=							prepare_img()
					_lowercase :    str							=							image_processor(images=__UpperCamelCase			,					return_tensors='pt'						).to(__UpperCamelCase						)

					# forward pass
					with torch.no_grad():
						_lowercase :    List[str]							=							model(**__UpperCamelCase						)

					_lowercase :    Any							=							outputs.logits.detach().cpu()

					_lowercase :    str							=							image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase			,					target_sizes=[(5_0, 6_0)]						)
					_lowercase :    Dict							=							torch.Size((5_0, 6_0)						)
					self.assertEqual(segmentation[0].shape			,					__UpperCamelCase						)

					_lowercase :    Optional[int]							=							image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase						)
					_lowercase :    Optional[int]							=							torch.Size((3_2, 3_2)						)
					self.assertEqual(segmentation[0].shape			,					__UpperCamelCase						)




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



