
import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor





class       _snake_case					(     unittest.TestCase				):







			def __init__(				self						,   _a						,   _a=7						,   _a=3						,   _a=18						,   _a=30						,   _a=400						,   _a=True						,   _a=None						,   _a=True						,   _a=[0.5, 0.5, 0.5]						,   _a=[0.5, 0.5, 0.5]						,   ):
				__magic_name__		:		int	   =     size if size is not None else {"height": 18, "width": 18}
				__magic_name__		:		Union[str, Any]	   =     parent
				__magic_name__		:		int	   =     batch_size
				__magic_name__		:		List[Any]	   =     num_channels
				__magic_name__		:		str	   =     image_size
				__magic_name__		:		Optional[int]	   =     min_resolution
				__magic_name__		:		List[str]	   =     max_resolution
				__magic_name__		:		str	   =     do_resize
				__magic_name__		:		Optional[int]	   =     size
				__magic_name__		:		Tuple	   =     do_normalize
				__magic_name__		:		Optional[Any]	   =     image_mean
				__magic_name__		:		int	   =     image_std







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}







@require_torch
@require_vision
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    DPTImageProcessor if is_vision_available() else None







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     DPTImageProcessingTester(self		)







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.image_processor_tester.prepare_image_processor_dict()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(_a						,   "image_mean"		)		)
				self.assertTrue(hasattr(_a						,   "image_std"		)		)
				self.assertTrue(hasattr(_a						,   "do_normalize"		)		)
				self.assertTrue(hasattr(_a						,   "do_resize"		)		)
				self.assertTrue(hasattr(_a						,   "size"		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size						,   {"height": 18, "width": 18}		)

				__magic_name__		:		List[str]	   =     self.image_processing_class.from_dict(self.image_processor_dict						,   size=42		)
				self.assertEqual(image_processor.size						,   {"height": 42, "width": 42}		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processing
				__magic_name__		:		str	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				__magic_name__		:		Optional[int]	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   Image.Image		)

				# Test not batched input
				__magic_name__		:		Optional[Any]	   =     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["height"],
				        self.image_processor_tester.size["width"],
				    )						,   )

				# Test batched
				__magic_name__		:		str	   =     image_processing(_a						,   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["height"],
				        self.image_processor_tester.size["width"],
				    )						,   )







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processing
				__magic_name__		:		str	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				__magic_name__		:		Optional[int]	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a						,   numpify=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   np.ndarray		)

				# Test not batched input
				__magic_name__		:		Optional[Any]	   =     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["height"],
				        self.image_processor_tester.size["width"],
				    )						,   )

				# Test batched
				__magic_name__		:		List[Any]	   =     image_processing(_a						,   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["height"],
				        self.image_processor_tester.size["width"],
				    )						,   )







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Initialize image_processing
				__magic_name__		:		int	   =     self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				__magic_name__		:		Union[str, Any]	   =     prepare_image_inputs(self.image_processor_tester						,   equal_resolution=_a						,   torchify=_a		)
				for image in image_inputs:
					self.assertIsInstance(_a						,   torch.Tensor		)

				# Test not batched input
				__magic_name__		:		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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,   )

				# Test batched
				__magic_name__		:		Any	   =     image_processing(_a						,   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["height"],
				        self.image_processor_tester.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,
)
