



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class    _snake_case   (				A_					):

					"""simple docstring"""





					_UpperCamelCase		 =      ["image_processor", "tokenizer"]
					_UpperCamelCase		 =      "AutoImageProcessor"
					_UpperCamelCase		 =      "AutoTokenizer"




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

										a_									=   image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
															raise ValueError('You have to specify an image_processor.'     )
										if tokenizer is None:
															raise ValueError('You have to specify a tokenizer.'     )

										super().__init__(UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   self.image_processor




					def __call__(     self   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , UpperCAmelCase__=None   , **UpperCAmelCase__     )      ->	List[Any]:



										if text is None and images is None:
															raise ValueError('You have to specify either text or images. Both cannot be none.'     )

										if text is not None:
															a_									=   self.tokenizer(UpperCAmelCase__   , return_tensors=UpperCAmelCase__   , **UpperCAmelCase__     )

										if images is not None:
															a_									=   self.image_processor(UpperCAmelCase__   , return_tensors=UpperCAmelCase__   , **UpperCAmelCase__     )

										if text is not None and images is not None:
															a_									=   image_features.pixel_values
															return encoding
										elif text is not None:
															return encoding
										else:
															return BatchEncoding(data=dict(**UpperCAmelCase__     )   , tensor_type=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , *UpperCAmelCase__   , **UpperCAmelCase__     )      ->	List[Any]:
										return self.tokenizer.batch_decode(*UpperCAmelCase__   , **UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , *UpperCAmelCase__   , **UpperCAmelCase__     )      ->	List[str]:
										return self.tokenizer.decode(*UpperCAmelCase__   , **UpperCAmelCase__     )




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.tokenizer.model_input_names
										a_									=   self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   , UpperCAmelCase__   , )
										return self.image_processor_class




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   , UpperCAmelCase__   , )
										return self.image_processor









'''simple docstring'''
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 ConvNextImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=7   , UpperCAmelCase__=3   , UpperCAmelCase__=18   , UpperCAmelCase__=30   , UpperCAmelCase__=400   , UpperCAmelCase__=True   , UpperCAmelCase__=None   , UpperCAmelCase__=0.8_7_5   , UpperCAmelCase__=True   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , )      ->	Optional[int]:
										a_									=   size if size is not None else {'shortest_edge': 20}
										a_									=   parent
										a_									=   batch_size
										a_									=   num_channels
										a_									=   image_size
										a_									=   min_resolution
										a_									=   max_resolution
										a_									=   do_resize
										a_									=   size
										a_									=   crop_pct
										a_									=   do_normalize
										a_									=   image_mean
										a_									=   image_std




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}



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

					"""simple docstring"""





					_UpperCamelCase		 =      ConvNextImageProcessor if is_vision_available() else None




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   ConvNextImageProcessingTester(self     )




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




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_resize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'size'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'crop_pct'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_normalize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_mean'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_std'     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 20}     )

										a_									=   self.image_processing_class.from_dict(self.image_processor_dict   , size=42     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 42}     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , Image.Image     )

										# Test not batched input
										a_									=   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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , 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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , numpify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , np.ndarray     )

										# Test not batched input
										a_									=   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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , 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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , torchify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , torch.Tensor     )

										# Test not batched input
										a_									=   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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , 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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

