



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor



class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=7      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=18      ,     _SCREAMING_SNAKE_CASE=30      ,     _SCREAMING_SNAKE_CASE=400      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=True      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	size if size is not None else {"""shortest_edge""": 20}
    UpperCAmelCase	:					Optional[int]	=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    UpperCAmelCase	:					Union[str, Any]	=	parent
    UpperCAmelCase	:					List[str]	=	batch_size
    UpperCAmelCase	:					List[Any]	=	num_channels
    UpperCAmelCase	:					Union[str, Any]	=	image_size
    UpperCAmelCase	:					Dict	=	min_resolution
    UpperCAmelCase	:					int	=	max_resolution
    UpperCAmelCase	:					Dict	=	do_resize
    UpperCAmelCase	:					int	=	size
    UpperCAmelCase	:					Optional[int]	=	do_center_crop
    UpperCAmelCase	:					List[Any]	=	crop_size
    UpperCAmelCase	:					Optional[int]	=	do_flip_channel_order




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_flip_channel_order": self.do_flip_channel_order,
    }



@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[str]						=						MobileViTImageProcessor if is_vision_available() else None

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	MobileViTImageProcessingTester(self						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return self.image_processor_tester.prepare_image_processor_dict()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_resize"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """size"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_center_crop"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """center_crop"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_flip_channel_order"""						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size      ,     {"""shortest_edge""": 20}						)
    self.assertEqual(image_processor.crop_size      ,     {"""height""": 18, """width""": 18}						)

    UpperCAmelCase	:					Optional[Any]	=	self.image_processing_class.from_dict(self.image_processor_dict      ,     size=42      ,     crop_size=84						)
    self.assertEqual(image_processor.size      ,     {"""shortest_edge""": 42}						)
    self.assertEqual(image_processor.crop_size      ,     {"""height""": 84, """width""": 84}						)

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





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    UpperCAmelCase	:					Union[str, Any]	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE						)
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     Image.Image						)

    # Test not batched input
    UpperCAmelCase	:					Optional[Any]	=	image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

    # Test batched
    UpperCAmelCase	:					List[Any]	=	image_processing(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    UpperCAmelCase	:					Tuple	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE      ,     numpify=_SCREAMING_SNAKE_CASE						)
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						)

    # Test not batched input
    UpperCAmelCase	:					List[str]	=	image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

    # Test batched
    UpperCAmelCase	:					int	=	image_processing(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    UpperCAmelCase	:					Union[str, Any]	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE      ,     torchify=_SCREAMING_SNAKE_CASE						)
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     torch.Tensor						)

    # Test not batched input
    UpperCAmelCase	:					Union[str, Any]	=	image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

    # Test batched
    UpperCAmelCase	:					List[Any]	=	image_processing(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
