


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			,						)      ->    int:
            _A				:	List[str]       =			size if size is not None else {"shortest_edge": 2_0}
            _A				:	int       =			crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _A				:	Optional[Any]       =			parent
            _A				:	Union[str, Any]       =			batch_size
            _A				:	List[str]       =			num_channels
            _A				:	List[str]       =			image_size
            _A				:	Tuple       =			min_resolution
            _A				:	Tuple       =			max_resolution
            _A				:	int       =			do_resize
            _A				:	Optional[Any]       =			size
            _A				:	str       =			do_center_crop
            _A				:	Optional[int]       =			crop_size


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            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)      ->    str:
            _A				:	Optional[Any]       =			MobileNetVaImageProcessingTester(self)


     @property
     def 			_lowerCamelCase			(						self)      ->    List[str]:
            return self.image_processor_tester.prepare_image_processor_dict()


     def 			_lowerCamelCase			(						self)      ->    int:
            _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)      ->    Dict:
            _A				:	Tuple       =			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				:	Tuple       =			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)      ->    Tuple:
            pass


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            # Initialize image_processing
            _A				:	List[Any]       =			self.image_processing_class(**self.image_processor_dict)
            # create random PIL images
            _A				:	str       =			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				:	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				:	Optional[int]       =			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)      ->    Union[str, Any]:
            # Initialize image_processing
            _A				:	List[str]       =			self.image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            _A				:	Optional[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				:	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(__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				:	Tuple       =			self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            _A				:	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				:	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				:	List[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"],
                )			,						)




import warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




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