

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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.config_class(**self.inputs_dict)
									self.parent.assertTrue(hasattr(lowerCAmelCase__   ,						"hidden_sizes"))
									self.parent.assertTrue(hasattr(lowerCAmelCase__   ,						"neck_hidden_sizes"))
									self.parent.assertTrue(hasattr(lowerCAmelCase__   ,						"num_attention_heads"))







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]=13   ,						lowerCAmelCase__  :     Any=32   ,						lowerCAmelCase__  :     List[Any]=2   ,						lowerCAmelCase__  :     Optional[int]=3   ,						lowerCAmelCase__  :     List[str]=640   ,						lowerCAmelCase__  :     Any=4   ,						lowerCAmelCase__  :     Optional[int]="silu"   ,						lowerCAmelCase__  :     Optional[int]=3   ,						lowerCAmelCase__  :     Union[str, Any]=32   ,						lowerCAmelCase__  :     Dict=0.1   ,						lowerCAmelCase__  :     Tuple=0.1   ,						lowerCAmelCase__  :     int=0.1   ,						lowerCAmelCase__  :     List[str]=0.02   ,						lowerCAmelCase__  :     Tuple=True   ,						lowerCAmelCase__  :     List[Any]=True   ,						lowerCAmelCase__  :     str=10   ,						lowerCAmelCase__  :     Dict=None   ,						):
									SCREAMING_SNAKE_CASE_: Any  		=       parent
									SCREAMING_SNAKE_CASE_: List[Any]  		=       batch_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       image_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       patch_size
									SCREAMING_SNAKE_CASE_: List[Any]  		=       num_channels
									SCREAMING_SNAKE_CASE_: str  		=       last_hidden_size
									SCREAMING_SNAKE_CASE_: List[Any]  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: Dict  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       conv_kernel_size
									SCREAMING_SNAKE_CASE_: str  		=       output_stride
									SCREAMING_SNAKE_CASE_: Any  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: int  		=       classifier_dropout_prob
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       use_labels
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       is_training
									SCREAMING_SNAKE_CASE_: Any  		=       num_labels
									SCREAMING_SNAKE_CASE_: Tuple  		=       initializer_range
									SCREAMING_SNAKE_CASE_: List[str]  		=       scope



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									SCREAMING_SNAKE_CASE_: List[str]  		=       None
									SCREAMING_SNAKE_CASE_: List[Any]  		=       None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_: str  		=       ids_tensor([self.batch_size]   ,						self.num_labels)
															SCREAMING_SNAKE_CASE_: Tuple  		=       ids_tensor([self.batch_size, self.image_size, self.image_size]   ,						self.num_labels)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_config()

									return config, pixel_values, labels, pixel_labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									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        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       MobileViTModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Tuple  		=       model(lowerCAmelCase__)
									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        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.num_labels
									SCREAMING_SNAKE_CASE_: str  		=       MobileViTForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Dict  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels))



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Any  		=       self.num_labels
									SCREAMING_SNAKE_CASE_: List[Any]  		=       MobileViTForSemanticSegmentation(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(
									    result.logits.shape   ,						(
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )   ,						)
									SCREAMING_SNAKE_CASE_: List[str]  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									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        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: int  		=       {"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     (
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase   :							str										=     (
			    {
			        '''feature-extraction''': MobileViTModel,
			        '''image-classification''': MobileViTForImageClassification,
			        '''image-segmentation''': MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCAmelCase   :							Any										=     False
			_UpperCAmelCase   :							Dict										=     False
			_UpperCAmelCase   :							Union[str, Any]										=     False
			_UpperCAmelCase   :							int										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       MobileViTModelTester(self)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       MobileViTConfigTester(self   ,						config_class=lowerCAmelCase__   ,						has_text_modality=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									self.config_tester.run_common_tests()



			@unittest.skip(reason="MobileViT does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			@unittest.skip(reason="MobileViT does not support input and output embeddings")
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									pass



			@unittest.skip(reason="MobileViT does not output attentions")
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       inspect.signature(model.forward)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															SCREAMING_SNAKE_CASE_: List[Any]  		=       [*signature.parameters.keys()]

															SCREAMING_SNAKE_CASE_: List[str]  		=       ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,						lowerCAmelCase__)



			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									def check_hidden_states_output(lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     str):
															SCREAMING_SNAKE_CASE_: Dict  		=       model_class(lowerCAmelCase__)
															model.to(lowerCAmelCase__)
															model.eval()

															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.hidden_states

															SCREAMING_SNAKE_CASE_: Any  		=       5
															self.assertEqual(len(lowerCAmelCase__)   ,						lowerCAmelCase__)

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2
															for i in range(len(lowerCAmelCase__)):
																					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)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Dict  		=       True
															check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       True

															check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       MobileViTModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image



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




			"""simple docstring"""







			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       self.default_image_processor
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: List[str]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: int  		=       model(**lowerCAmelCase__)

									# verify the logits
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Size((1, 1000))
									self.assertEqual(outputs.logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       torch.tensor([-1.9364, -1.2327, -0.4653]).to(lowerCAmelCase__)

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



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.to(lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: Any  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       outputs.logits

									# verify the logits
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Size((1, 21, 32, 32))
									self.assertEqual(logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, 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=lowerCAmelCase__   ,						)

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




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: List[str]  		=       MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
									SCREAMING_SNAKE_CASE_: Dict  		=       model.to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

									SCREAMING_SNAKE_CASE_: Dict  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: int  		=       outputs.logits.detach().cpu()

									SCREAMING_SNAKE_CASE_: Any  		=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__   ,						target_sizes=[(50, 60)])
									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.Size((50, 60))
									self.assertEqual(segmentation[0].shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.Size((32, 32))
									self.assertEqual(segmentation[0].shape   ,						lowerCAmelCase__)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:							Optional[Any]													=     {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							List[str]													=     [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

       snake_case__					:							Dict													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





