





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 MobileNetVaImageProcessor

class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 def __init__(	self	:							Any							,     __A	:							int							,     __A	:							str=7							,     __A	:							Union[str, Any]=3							,     __A	:							int=1_8							,     __A	:							Optional[Any]=3_0							,     __A	:							List[Any]=4_0_0							,     __A	:							Union[str, Any]=True							,     __A	:							Any=None							,     __A	:							Tuple=True							,     __A	:							Optional[Any]=None							,     ):
        __UpperCamelCase	=  size if size is not None else {"shortest_edge": 2_0}
        __UpperCamelCase	=  crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        __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






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }






@require_torch
@require_vision
class 					snake_case					(   __lowerCamelCase      ,	unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=MobileNetVaImageProcessor if is_vision_available() else None






 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  MobileNetVaImageProcessingTester(self     )






 @property
 def 	_lowerCamelCase (	self	:							str     ):
        return self.image_processor_tester.prepare_image_processor_dict()






 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__A							,     'do_resize'     )     )
        self.assertTrue(hasattr(__A							,     'size'     )     )
        self.assertTrue(hasattr(__A							,     'do_center_crop'     )     )
        self.assertTrue(hasattr(__A							,     'center_crop'     )     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	=  self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size							,     {'shortest_edge': 2_0}     )
        self.assertEqual(image_processor.crop_size							,     {'height': 1_8, 'width': 1_8}     )

        __UpperCamelCase	=  self.image_processing_class.from_dict(self.image_processor_dict							,     size=4_2							,     crop_size=8_4     )
        self.assertEqual(image_processor.size							,     {'shortest_edge': 4_2}     )
        self.assertEqual(image_processor.crop_size							,     {'height': 8_4, 'width': 8_4}     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        pass






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        __UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A     )
        for image in image_inputs:
               self.assertIsInstance(__A							,     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(__A							,     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 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        __UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A							,     numpify=__A     )
        for image in image_inputs:
               self.assertIsInstance(__A							,     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(__A							,     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 	_lowerCamelCase (	self	:							Dict     ):
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        __UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A							,     torchify=__A     )
        for image in image_inputs:
               self.assertIsInstance(__A							,     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(__A							,     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'''
a__							:							int			='''
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a__							:							List[Any]			=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a__							:							Dict			={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
