




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 						A(		UpperCamelCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(A_		,							'hidden_sizes'						)						)
       self.parent.assertTrue(hasattr(A_		,							'neck_hidden_sizes'						)						)
       self.parent.assertTrue(hasattr(A_		,							'num_attention_heads'						)						)







class 						A:



 '''simple docstring'''


 def __init__(       self      :						List[Any]		,							A_      :						Any		,							A_      :						Dict=13		,							A_      :						int=32		,							A_      :						str=2		,							A_      :						Dict=3		,							A_      :						Optional[Any]=640		,							A_      :						List[Any]=4		,							A_      :						Optional[int]="silu"		,							A_      :						Dict=3		,							A_      :						Optional[int]=32		,							A_      :						Optional[Any]=0.1		,							A_      :						List[Any]=0.1		,							A_      :						List[str]=0.1		,							A_      :						List[Any]=0.02		,							A_      :						Union[str, Any]=True		,							A_      :						Tuple=True		,							A_      :						Dict=10		,							A_      :						Optional[int]=None		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					image_size
       lowerCamelCase_	  =					patch_size
       lowerCamelCase_	  =					num_channels
       lowerCamelCase_	  =					last_hidden_size
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					conv_kernel_size
       lowerCamelCase_	  =					output_stride
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					classifier_dropout_prob
       lowerCamelCase_	  =					use_labels
       lowerCamelCase_	  =					is_training
       lowerCamelCase_	  =					num_labels
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					scope

 def        a__				(       self      :						str						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       lowerCamelCase_	  =					None
       lowerCamelCase_	  =					None
       if self.use_labels:
             lowerCamelCase_	  =					ids_tensor([self.batch_size]		,							self.num_labels						)
             lowerCamelCase_	  =					ids_tensor([self.batch_size, self.image_size, self.image_size]		,							self.num_labels						)

       lowerCamelCase_	  =					self.get_config()

       return config, pixel_values, labels, pixel_labels

 def        a__				(       self      :						List[str]						)     ->			Optional[int]:

       """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        a__				(       self      :						Dict		,							A_      :						Tuple		,							A_      :						str		,							A_      :						List[str]		,							A_      :						Tuple						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					MobileViTModel(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_						)
       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        a__				(       self      :						Tuple		,							A_      :						Tuple		,							A_      :						Optional[int]		,							A_      :						int		,							A_      :						List[str]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					MobileViTForImageClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							labels=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels)						)

 def        a__				(       self      :						List[Any]		,							A_      :						List[Any]		,							A_      :						Dict		,							A_      :						Any		,							A_      :						Optional[int]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					MobileViTForSemanticSegmentation(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_						)
       self.parent.assertEqual(
           result.logits.shape		,							(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )		,							)
       lowerCamelCase_	  =					model(A_		,							labels=A_						)
       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        a__				(       self      :						str						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					config_and_inputs
       lowerCamelCase_	  =					{'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 						A(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(
     (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 UpperCamelCase		     =		(
     {
         '''feature-extraction''': MobileViTModel,
         '''image-classification''': MobileViTForImageClassification,
         '''image-segmentation''': MobileViTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False

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

       """simple docstring"""






       lowerCamelCase_	  =					MobileViTModelTester(self						)
       lowerCamelCase_	  =					MobileViTConfigTester(self		,							config_class=A_		,							has_text_modality=A_						)

 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='MobileViT does not use inputs_embeds'						)
 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       pass

 @unittest.skip(reason='MobileViT does not support input and output embeddings'						)
 def        a__				(       self      :						List[Any]						)     ->			Tuple:

       """simple docstring"""






       pass

 @unittest.skip(reason='MobileViT does not output attentions'						)
 def        a__				(       self      :						str						)     ->			str:

       """simple docstring"""






       pass

 def        a__				(       self      :						int						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					model_class(A_						)
             lowerCamelCase_	  =					inspect.signature(model.forward						)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowerCamelCase_	  =					[*signature.parameters.keys()]

             lowerCamelCase_	  =					['pixel_values']
             self.assertListEqual(arg_names[:1]		,							A_						)

 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       pass

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

       """simple docstring"""






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

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

       """simple docstring"""






       def check_hidden_states_output(A_      :						Any		,							A_      :						Optional[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					model_class(A_						)
             model.to(A_						)
             model.eval()

             with torch.no_grad():
                   lowerCamelCase_	  =					model(**self._prepare_for_class(A_		,							A_						)						)

             lowerCamelCase_	  =					outputs.hidden_states

             lowerCamelCase_	  =					5
             self.assertEqual(len(A_						)		,							A_						)

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

       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					True
             check_hidden_states_output(A_		,							A_		,							A_						)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowerCamelCase_	  =					True

             check_hidden_states_output(A_		,							A_		,							A_						)

 def        a__				(       self      :						int						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*A_						)





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

       """simple docstring"""






       for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_	  =					MobileViTModel.from_pretrained(A_						)
             self.assertIsNotNone(A_						)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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







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



 '''simple docstring'''


 @cached_property
 def        a__				(       self      :						List[str]						)     ->			Any:

       """simple docstring"""






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

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

       """simple docstring"""






       lowerCamelCase_	  =					MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'						).to(A_						)

       lowerCamelCase_	  =					self.default_image_processor
       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=A_		,							return_tensors='pt'						).to(A_						)

       # forward pass
       with torch.no_grad():
             lowerCamelCase_	  =					model(**A_						)

       # verify the logits
       lowerCamelCase_	  =					torch.Size((1, 1000)						)
       self.assertEqual(outputs.logits.shape		,							A_						)

       lowerCamelCase_	  =					torch.tensor([-1.9364, -1.2327, -0.4653]						).to(A_						)

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

 @slow
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)
       lowerCamelCase_	  =					model.to(A_						)

       lowerCamelCase_	  =					MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)

       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=A_		,							return_tensors='pt'						).to(A_						)

       # forward pass
       with torch.no_grad():
             lowerCamelCase_	  =					model(**A_						)
       lowerCamelCase_	  =					outputs.logits

       # verify the logits
       lowerCamelCase_	  =					torch.Size((1, 21, 32, 32)						)
       self.assertEqual(logits.shape		,							A_						)

       lowerCamelCase_	  =					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=A_		,							)

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





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

       """simple docstring"""






       lowerCamelCase_	  =					MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)
       lowerCamelCase_	  =					model.to(A_						)

       lowerCamelCase_	  =					MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'						)

       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=A_		,							return_tensors='pt'						).to(A_						)

       # forward pass
       with torch.no_grad():
             lowerCamelCase_	  =					model(**A_						)

       lowerCamelCase_	  =					outputs.logits.detach().cpu()

       lowerCamelCase_	  =					image_processor.post_process_semantic_segmentation(outputs=A_		,							target_sizes=[(50, 60)]						)
       lowerCamelCase_	  =					torch.Size((50, 60)						)
       self.assertEqual(segmentation[0].shape		,							A_						)

       lowerCamelCase_	  =					image_processor.post_process_semantic_segmentation(outputs=A_						)
       lowerCamelCase_	  =					torch.Size((32, 32)						)
       self.assertEqual(segmentation[0].shape		,							A_						)









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



