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			):
  '''simple docstring'''


  def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					int=7      ,	SCREAMING_SNAKE_CASE__    :					Any=3      ,	SCREAMING_SNAKE_CASE__    :					Tuple=18      ,	SCREAMING_SNAKE_CASE__    :					str=30      ,	SCREAMING_SNAKE_CASE__    :					str=4_00      ,	SCREAMING_SNAKE_CASE__    :					Tuple=True      ,	SCREAMING_SNAKE_CASE__    :					str=None      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=True      ,	)    ->				List[str]:
         A						:     Union[str, Any]         =size if size is not None else {'shortest_edge': 20}
         A						:     Tuple         =crop_size if crop_size is not None else {'height': 18, 'width': 18}
         A						:     Optional[Any]         =parent
         A						:     str         =batch_size
         A						:     Dict         =num_channels
         A						:     str         =image_size
         A						:     List[Any]         =min_resolution
         A						:     List[str]         =max_resolution
         A						:     Optional[Any]         =do_resize
         A						:     Any         =size
         A						:     List[Any]         =do_center_crop
         A						:     Dict         =crop_size
         A						:     str         =do_flip_channel_order







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[Any]:
         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			):
  '''simple docstring'''


  lowercase		:     int            =				MobileViTImageProcessor if is_vision_available() else None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				int:
         A						:     int         =MobileViTImageProcessingTester(self     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				str:
         return self.image_processor_tester.prepare_image_processor_dict()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Optional[Any]:
         A						:     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    :					Any     )    ->				Tuple:
         A						:     Dict         =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}     )

         A						:     List[str]         =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    :					Tuple     )    ->				Optional[int]:
         pass







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[Any]:
         # Initialize image_processing
         A						:     int         =self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         A						:     List[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
         A						:     Optional[int]         =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
         A						:     str         =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    :					Optional[int]     )    ->				str:
         # Initialize image_processing
         A						:     List[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         A						:     Any         =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
         A						:     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
         A						:     List[str]         =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    :					int     )    ->				Optional[Any]:
         # Initialize image_processing
         A						:     Dict         =self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         A						:     str         =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
         A						:     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
         A						:     Optional[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'],
             )      ,	)







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def A__  (			*lowercase:     Tuple,		lowercase:     Optional[Union[Dict, Any]] = None,		lowercase:     Dict=True,		lowercase:     Any=2					)				->		List[Any]:
       from .. import __version__

       A						:     Optional[Any]         =take_from
       A						:     Union[str, Any]         =()
       if not isinstance(args[0],		lowercase					):
              A						:     List[str]         =(args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(lowercase					).base_version					) >= version.parse(lowercase					):
                     raise ValueError(
                         F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                         F' version {__version__} is >= {version_name}'					)

              A						:     Tuple         =None
              if isinstance(lowercase,		lowercase					) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowercase					),)
                     A						:     Union[str, Any]         =F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(lowercase,		lowercase					):
                     values += (getattr(lowercase,		lowercase					),)
                     A						:     Optional[Any]         =F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     A						:     List[Any]         =F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     A						:     List[Any]         =warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message,		lowercase,		stacklevel=lowercase					)

       if isinstance(lowercase,		lowercase					) and len(lowercase					) > 0:
              A						:     Any         =inspect.getouterframes(inspect.currentframe()					)[1]
              A						:     int         =call_frame.filename
              A						:     int         =call_frame.lineno
              A						:     Optional[int]         =call_frame.function
              A  ,  A						:     int         =next(iter(deprecated_kwargs.items()					)					)
              raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'					)

       if len(lowercase					) == 0:
              return
       elif len(lowercase					) == 1:
              return values[0]
       return values






