

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case							:	int											=       logging.get_logger(__name__)



class     A__(lowerCAmelCase__				):
						"""simple docstring"""






						_A  :							str     =       ["""pixel_values"""]





						def __init__(  self   ,	_lowercase = True   ,	_lowercase = None   ,	_lowercase = PILImageResampling.BILINEAR   ,	_lowercase = True   ,	_lowercase = None   ,	_lowercase = True   ,	_lowercase = 1 / 255   ,	_lowercase = True   ,	_lowercase = None   ,	_lowercase = None   ,	**_lowercase   ,	)			->      int:
									super().__init__(**lowercase_  )
									a_		:		List[str]      				=      size if size is not None else {'''shortest_edge''': 256}
									a_		:		Optional[int]      				=      get_size_dict(lowercase_   ,	default_to_square=lowercase_  )
									a_		:		Dict      				=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									a_		:		Dict      				=      get_size_dict(lowercase_  )
									a_		:		List[str]      				=      do_resize
									a_		:		Optional[int]      				=      size
									a_		:		Union[str, Any]      				=      resample
									a_		:		Dict      				=      do_center_crop
									a_		:		List[str]      				=      crop_size
									a_		:		int      				=      do_rescale
									a_		:		List[str]      				=      rescale_factor
									a_		:		int      				=      do_normalize
									a_		:		int      				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									a_		:		int      				=      image_std if image_std is not None else IMAGENET_STANDARD_STD





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase   ,	_lowercase = PILImageResampling.BICUBIC   ,	_lowercase = None   ,	**_lowercase   ,	)			->      Tuple:
									a_		:		str      				=      get_size_dict(lowercase_   ,	default_to_square=lowercase_  )
									if "shortest_edge" not in size:
												raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
									a_		:		Dict      				=      get_resize_output_image_size(lowercase_   ,	size=size["""shortest_edge"""]   ,	default_to_square=lowercase_  )
									return resize(lowercase_   ,	size=lowercase_   ,	resample=lowercase_   ,	data_format=lowercase_   ,	**lowercase_  )





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase   ,	_lowercase = None   ,	**_lowercase   ,	)			->      Optional[Any]:
									a_		:		Union[str, Any]      				=      get_size_dict(lowercase_  )
									return center_crop(lowercase_   ,	size=(size["""height"""], size["""width"""])   ,	data_format=lowercase_   ,	**lowercase_  )





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase   ,	_lowercase = None   ,	**_lowercase  )			->      int:
									return rescale(lowercase_   ,	scale=lowercase_   ,	data_format=lowercase_   ,	**lowercase_  )





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase   ,	_lowercase   ,	_lowercase = None   ,	**_lowercase   ,	)			->      List[str]:
									return normalize(lowercase_   ,	mean=lowercase_   ,	std=lowercase_   ,	data_format=lowercase_   ,	**lowercase_  )





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = None   ,	_lowercase = ChannelDimension.FIRST   ,	**_lowercase   ,	)			->      Any:
									a_		:		Optional[int]      				=      do_resize if do_resize is not None else self.do_resize
									a_		:		int      				=      size if size is not None else self.size
									a_		:		Dict      				=      get_size_dict(lowercase_   ,	default_to_square=lowercase_  )
									a_		:		str      				=      resample if resample is not None else self.resample
									a_		:		Any      				=      do_center_crop if do_center_crop is not None else self.do_center_crop
									a_		:		Union[str, Any]      				=      crop_size if crop_size is not None else self.crop_size
									a_		:		Union[str, Any]      				=      get_size_dict(lowercase_  )
									a_		:		Union[str, Any]      				=      do_rescale if do_rescale is not None else self.do_rescale
									a_		:		Tuple      				=      rescale_factor if rescale_factor is not None else self.rescale_factor
									a_		:		List[Any]      				=      do_normalize if do_normalize is not None else self.do_normalize
									a_		:		Any      				=      image_mean if image_mean is not None else self.image_mean
									a_		:		List[Any]      				=      image_std if image_std is not None else self.image_std

									a_		:		str      				=      make_list_of_images(lowercase_  )

									if not valid_images(lowercase_  ):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""  )

									if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

									if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

									if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

									# All transformations expect numpy arrays.
									a_		:		List[Any]      				=      [to_numpy_array(lowercase_  ) for image in images]

									if do_resize:
												a_		:		Optional[Any]      				=      [self.resize(image=lowercase_   ,	size=lowercase_   ,	resample=lowercase_  ) for image in images]

									if do_center_crop:
												a_		:		List[str]      				=      [self.center_crop(image=lowercase_   ,	size=lowercase_  ) for image in images]

									if do_rescale:
												a_		:		int      				=      [self.rescale(image=lowercase_   ,	scale=lowercase_  ) for image in images]

									if do_normalize:
												a_		:		Dict      				=      [self.normalize(image=lowercase_   ,	mean=lowercase_   ,	std=lowercase_  ) for image in images]

									a_		:		Tuple      				=      [to_channel_dimension_format(lowercase_   ,	lowercase_  ) for image in images]

									a_		:		int      				=      {'''pixel_values''': images}
									return BatchFeature(data=lowercase_   ,	tensor_type=lowercase_  )







"""simple docstring"""

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,
)





