


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE						    =				"AutoImageProcessor"
     __SCREAMING_SNAKE_CASE						    =				"AutoTokenizer"


     def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       List[str]:
            _A   : int       					=				None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."				,		__lowerCamelCase				,		)
                   _A   : Dict       					=				kwargs.pop("feature_extractor")

            _A   : str       					=				image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`.")
            if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`.")

            super().__init__(__lowerCamelCase				,		__lowerCamelCase)
            _A   : Tuple       					=				self.image_processor
            _A   : Dict       					=				False


     def __call__(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       str:
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*__lowerCamelCase				,		**__lowerCamelCase)

            _A   : Optional[Any]       					=				kwargs.pop("images"				,		__lowerCamelCase)
            _A   : Optional[Any]       					=				kwargs.pop("text"				,		__lowerCamelCase)
            if len(__lowerCamelCase) > 0:
                   _A   : Dict       					=				args[0]
                   _A   : str       					=				args[1:]

            if images is None and text is None:
                   raise ValueError("You need to specify either an `images` or `text` input to process.")

            if images is not None:
                   _A   : Union[str, Any]       					=				self.image_processor(__lowerCamelCase				,		*__lowerCamelCase				,		**__lowerCamelCase)
            if text is not None:
                   _A   : str       					=				self.tokenizer(__lowerCamelCase				,		**__lowerCamelCase)

            if text is None:
                   return inputs
            elif images is None:
                   return encodings
            else:
                   _A   : List[Any]       					=				encodings["input_ids"]
                   return inputs


     def 				_lowerCamelCase				(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       str:
            return self.tokenizer.batch_decode(*__lowerCamelCase				,		**__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       Optional[Any]:
            return self.tokenizer.decode(*__lowerCamelCase				,		**__lowerCamelCase)


     @contextmanager
     def 				_lowerCamelCase				(		self)    ->       int:
            warnings.warn(
                "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                "your images inputs, or in a separate call.")
            _A   : List[str]       					=				True
            _A   : Union[str, Any]       					=				self.tokenizer
            yield
            _A   : List[Any]       					=				self.image_processor
            _A   : Optional[Any]       					=				False


     @property
     def 				_lowerCamelCase				(		self)    ->       int:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,		__lowerCamelCase				,		)
            return self.image_processor_class


     @property
     def 				_lowerCamelCase				(		self)    ->       int:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,		__lowerCamelCase				,		)
            return self.image_processor




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"],
                )				,		)
