


import warnings

from .generation import GenerationMixin







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."  ,					a  ,					)




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   lowerCAmelCase__ (       unittest.TestCase):




     '''simple docstring'''


     def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=7				,		__lowerCamelCase=3				,		__lowerCamelCase=1_8				,		__lowerCamelCase=3_0				,		__lowerCamelCase=4_0_0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
            _A   : int       					=				size if size is not None else {"shortest_edge": 2_0}
            _A   : Tuple       					=				crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _A   : str       					=				parent
            _A   : Any       					=				batch_size
            _A   : Dict       					=				num_channels
            _A   : int       					=				image_size
            _A   : Union[str, Any]       					=				min_resolution
            _A   : Tuple       					=				max_resolution
            _A   : Any       					=				do_resize
            _A   : Optional[int]       					=				size
            _A   : Tuple       					=				do_center_crop
            _A   : List[str]       					=				crop_size


     def 				_lowerCamelCase				(		self)    ->       str:
            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   lowerCAmelCase__ (       a  ,					unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				MobileNetVaImageProcessor if is_vision_available() else None


     def 				_lowerCamelCase				(		self)    ->       Any:
            _A   : Dict       					=				MobileNetVaImageProcessingTester(self)


     @property
     def 				_lowerCamelCase				(		self)    ->       Dict:
            return self.image_processor_tester.prepare_image_processor_dict()


     def 				_lowerCamelCase				(		self)    ->       Tuple:
            _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(__lowerCamelCase				,		"do_resize"))
            self.assertTrue(hasattr(__lowerCamelCase				,		"size"))
            self.assertTrue(hasattr(__lowerCamelCase				,		"do_center_crop"))
            self.assertTrue(hasattr(__lowerCamelCase				,		"center_crop"))


     def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
            _A   : int       					=				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})

            _A   : Any       					=				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)    ->       Union[str, Any]:
            pass


     def 				_lowerCamelCase				(		self)    ->       List[Any]:
            # Initialize image_processing
            _A   : Optional[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=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase				,		Image.Image)

            # Test not batched input
            _A   : 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
            _A   : Dict       					=				image_processing(__lowerCamelCase				,		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:
            # Initialize image_processing
            _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            _A   : int       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		numpify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase				,		np.ndarray)

            # Test not batched input
            _A   : Tuple       					=				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(__lowerCamelCase				,		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)    ->       Tuple:
            # Initialize image_processing
            _A   : str       					=				self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            _A   : List[str]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		torchify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase				,		torch.Tensor)

            # Test not batched input
            _A   : 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
            _A   : Any       					=				image_processing(__lowerCamelCase				,		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"],
                )				,		)
