




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 	_UpperCamelCase					( unittest.TestCase						):
      def __init__(	self      :Optional[int]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Optional[Any]=7       ,					lowerCamelCase      :Union[str, Any]=3       ,					lowerCamelCase      :List[str]=18       ,					lowerCamelCase      :str=30       ,					lowerCamelCase      :Optional[int]=400       ,					lowerCamelCase      :Tuple=True       ,					lowerCamelCase      :List[str]=None       ,					lowerCamelCase      :Dict=True       ,					lowerCamelCase      :Optional[Any]=None       ,					lowerCamelCase      :Optional[int]=True       ,					)   ->	Union[str, Any]:
       UpperCAmelCase__							       =  size if size is not None else {"""shortest_edge""": 20}
       UpperCAmelCase__							       =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
       UpperCAmelCase__							       =  parent
       UpperCAmelCase__							       =  batch_size
       UpperCAmelCase__							       =  num_channels
       UpperCAmelCase__							       =  image_size
       UpperCAmelCase__							       =  min_resolution
       UpperCAmelCase__							       =  max_resolution
       UpperCAmelCase__							       =  do_resize
       UpperCAmelCase__							       =  size
       UpperCAmelCase__							       =  do_center_crop
       UpperCAmelCase__							       =  crop_size
       UpperCAmelCase__							       =  do_flip_channel_order







      def 		UpperCAmelCase_  (	self      :Any					)   ->	Dict:
       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 	_UpperCamelCase					( UpperCAmelCase__		,				unittest.TestCase						):
      UpperCAmelCase_						=						MobileViTImageProcessor if is_vision_available() else None
      def 		UpperCAmelCase_  (	self      :Any					)   ->	str:
       UpperCAmelCase__							       =  MobileViTImageProcessingTester(self					)
      @property
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	Tuple:
       return self.image_processor_tester.prepare_image_processor_dict()
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	Dict:
       UpperCAmelCase__							       =  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 		UpperCAmelCase_  (	self      :List[Any]					)   ->	str:
       UpperCAmelCase__							       =  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__							       =  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 		UpperCAmelCase_  (	self      :str					)   ->	List[str]:
       pass
      def 		UpperCAmelCase_  (	self      :int					)   ->	Dict:
       UpperCAmelCase__							       =  self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       UpperCAmelCase__							       =  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__							       =  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__							       =  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 		UpperCAmelCase_  (	self      :Dict					)   ->	Tuple:
       UpperCAmelCase__							       =  self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       UpperCAmelCase__							       =  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__							       =  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__							       =  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 		UpperCAmelCase_  (	self      :Union[str, Any]					)   ->	Tuple:
       UpperCAmelCase__							       =  self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       UpperCAmelCase__							       =  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__							       =  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__							       =  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__)
