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				):






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





       '''simple docstring'''





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



class      _A :






  def __init__(self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=13 ,					SCREAMING_SNAKE_CASE_=32 ,					SCREAMING_SNAKE_CASE_=2 ,					SCREAMING_SNAKE_CASE_=3 ,					SCREAMING_SNAKE_CASE_=640 ,					SCREAMING_SNAKE_CASE_=4 ,					SCREAMING_SNAKE_CASE_="silu" ,					SCREAMING_SNAKE_CASE_=3 ,					SCREAMING_SNAKE_CASE_=32 ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=0.02 ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=10 ,					SCREAMING_SNAKE_CASE_=None ,					)		->   int:





       '''simple docstring'''





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






  def 		_a       (self						)		->   str:





       '''simple docstring'''





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

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

       UpperCamelCase__					    =   self.get_config()

       return config, pixel_values, labels, pixel_labels






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





       '''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 ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   MobileViTModel(config=_lowercase						)
       model.to(_lowercase						)
       model.eval()
       UpperCamelCase__					    =   model(_lowercase						)
       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 ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   Any:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   self.num_labels
       UpperCamelCase__					    =   MobileViTForSemanticSegmentation(_lowercase						)
       model.to(_lowercase						)
       model.eval()
       UpperCamelCase__					    =   model(_lowercase						)
       self.parent.assertEqual(
           result.logits.shape ,					(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           ) ,					)
       UpperCamelCase__					    =   model(_lowercase ,					labels=_lowercase						)
       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						)		->   Any:





       '''simple docstring'''





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



@require_torch
class      _A (     __UpperCamelCase						,			__UpperCamelCase						,			unittest.TestCase				):
  SCREAMING_SNAKE_CASE_						:						Dict    =(
      (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE_						:						List[str]    =(
      {
          "feature-extraction": MobileViTModel,
          "image-classification": MobileViTForImageClassification,
          "image-segmentation": MobileViTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE_						:						List[str]    =False
  SCREAMING_SNAKE_CASE_						:						Optional[int]    =False
  SCREAMING_SNAKE_CASE_						:						Optional[Any]    =False
  SCREAMING_SNAKE_CASE_						:						int    =False






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





       '''simple docstring'''





       UpperCamelCase__					    =   MobileViTModelTester(self						)
       UpperCamelCase__					    =   MobileViTConfigTester(self ,					config_class=_lowercase ,					has_text_modality=_lowercase						)






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





       '''simple docstring'''





       self.config_tester.run_common_tests()






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





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       pass






  @unittest.skip(reason='''MobileViT does not output attentions'''						)
  def 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       pass






  def 		_a       (self						)		->   int:





       '''simple docstring'''





       UpperCamelCase__    ,							UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCamelCase__					    =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1] ,					_lowercase						)






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





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowercase						)






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       def check_hidden_states_output(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__					    =   model_class(_lowercase						)
            model.to(_lowercase						)
            model.eval()

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

            UpperCamelCase__					    =   outputs.hidden_states

            UpperCamelCase__					    =   5
            self.assertEqual(len(_lowercase						) ,					_lowercase						)

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

       UpperCamelCase__    ,							UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

            check_hidden_states_output(_lowercase ,					_lowercase ,					_lowercase						)






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowercase						)






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase						)






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





       '''simple docstring'''





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







def 			__UpperCamelCase     (    ):
     UpperCamelCase__					    =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image



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






  @cached_property
  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





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






  @slow
  def 		_a       (self						)		->   str:





       '''simple docstring'''





       UpperCamelCase__					    =   MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''						).to(_lowercase						)

       UpperCamelCase__					    =   self.default_image_processor
       UpperCamelCase__					    =   prepare_img()
       UpperCamelCase__					    =   image_processor(images=_lowercase ,					return_tensors='''pt'''						).to(_lowercase						)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__					    =   model(**_lowercase						)

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

       UpperCamelCase__					    =   torch.tensor([-1.9364, -1.2327, -0.4653]						).to(_lowercase						)

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






  @slow
  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





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

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

       UpperCamelCase__					    =   prepare_img()
       UpperCamelCase__					    =   image_processor(images=_lowercase ,					return_tensors='''pt'''						).to(_lowercase						)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__					    =   model(**_lowercase						)
       UpperCamelCase__					    =   outputs.logits

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

       UpperCamelCase__					    =   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=_lowercase ,					)

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






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





       '''simple docstring'''





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

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

       UpperCamelCase__					    =   prepare_img()
       UpperCamelCase__					    =   image_processor(images=_lowercase ,					return_tensors='''pt'''						).to(_lowercase						)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__					    =   model(**_lowercase						)

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

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

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









'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



					def __len__(	self     :						List[str])    ->							Union[str, Any]:
								return self.config.num_train_timesteps




