



'''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 LevitImageProcessor

class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def __init__(						self			:     int		,			__snake_case			:     int		,			__snake_case			:     Dict=7		,			__snake_case			:     List[str]=3		,			__snake_case			:     Any=18		,			__snake_case			:     Optional[int]=30		,			__snake_case			:     List[Any]=400		,			__snake_case			:     Any=True		,			__snake_case			:     Union[str, Any]=None		,			__snake_case			:     List[str]=True		,			__snake_case			:     Dict=None		,			__snake_case			:     Dict=True		,			__snake_case			:     Any=[0.5, 0.5, 0.5]		,			__snake_case			:     Tuple=[0.5, 0.5, 0.5]		,			)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		size if size is not None else {'''shortest_edge''': 18}
								UpperCAmelCase :       Dict 							=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								UpperCAmelCase :       Optional[int] 							=		parent
								UpperCAmelCase :       Union[str, Any] 							=		batch_size
								UpperCAmelCase :       int 							=		num_channels
								UpperCAmelCase :       Union[str, Any] 							=		image_size
								UpperCAmelCase :       Optional[Any] 							=		min_resolution
								UpperCAmelCase :       List[str] 							=		max_resolution
								UpperCAmelCase :       List[Any] 							=		do_resize
								UpperCAmelCase :       List[Any] 							=		size
								UpperCAmelCase :       Dict 							=		do_center_crop
								UpperCAmelCase :       Dict 							=		crop_size
								UpperCAmelCase :       Union[str, Any] 							=		do_normalize
								UpperCAmelCase :       int 							=		image_mean
								UpperCAmelCase :       Union[str, Any] 							=		image_std







	def      A	(						self			:     List[str]		)			->		Tuple:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}

@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       LevitImageProcessor if is_vision_available() else None







	def      A	(						self			:     Tuple		)			->		Any:
								UpperCAmelCase :       List[Any] 							=		LevitImageProcessingTester(self		)







	@property
	def      A	(						self			:     int		)			->		int:
								return self.image_processor_tester.prepare_image_processor_dict()







	def      A	(						self			:     Optional[Any]		)			->		Tuple:
								UpperCAmelCase :       int 							=		self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(__snake_case		,			'''image_mean'''		)		)
								self.assertTrue(hasattr(__snake_case		,			'''image_std'''		)		)
								self.assertTrue(hasattr(__snake_case		,			'''do_normalize'''		)		)
								self.assertTrue(hasattr(__snake_case		,			'''do_resize'''		)		)
								self.assertTrue(hasattr(__snake_case		,			'''do_center_crop'''		)		)
								self.assertTrue(hasattr(__snake_case		,			'''size'''		)		)







	def      A	(						self			:     int		)			->		Any:
								UpperCAmelCase :       Dict 							=		self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size		,			{'''shortest_edge''': 18}		)
								self.assertEqual(image_processor.crop_size		,			{'''height''': 18, '''width''': 18}		)

								UpperCAmelCase :       List[str] 							=		self.image_processing_class.from_dict(self.image_processor_dict		,			size=42		,			crop_size=84		)
								self.assertEqual(image_processor.size		,			{'''shortest_edge''': 42}		)
								self.assertEqual(image_processor.crop_size		,			{'''height''': 84, '''width''': 84}		)







	def      A	(						self			:     Tuple		)			->		Dict:
								pass







	def      A	(						self			:     Optional[int]		)			->		Dict:
								# Initialize image_processing
								UpperCAmelCase :       Dict 							=		self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								UpperCAmelCase :       List[Any] 							=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=__snake_case		)
								for image in image_inputs:
															self.assertIsInstance(__snake_case		,			Image.Image		)

								# Test not batched input
								UpperCAmelCase :       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
								UpperCAmelCase :       Any 							=		image_processing(__snake_case		,			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      A	(						self			:     Optional[Any]		)			->		Optional[int]:
								# Initialize image_processing
								UpperCAmelCase :       List[str] 							=		self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								UpperCAmelCase :       Union[str, Any] 							=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=__snake_case		,			numpify=__snake_case		)
								for image in image_inputs:
															self.assertIsInstance(__snake_case		,			np.ndarray		)

								# Test not batched input
								UpperCAmelCase :       List[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
								UpperCAmelCase :       Tuple 							=		image_processing(__snake_case		,			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      A	(						self			:     Optional[int]		)			->		Dict:
								# Initialize image_processing
								UpperCAmelCase :       List[Any] 							=		self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								UpperCAmelCase :       Optional[Any] 							=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=__snake_case		,			torchify=__snake_case		)
								for image in image_inputs:
															self.assertIsInstance(__snake_case		,			torch.Tensor		)

								# Test not batched input
								UpperCAmelCase :       Dict 							=		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 :       Dict 							=		image_processing(__snake_case		,			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'''],
								    )		,			)












from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






