



"""simple docstring"""

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, is_vision_available, logging


if is_vision_available():
     import PIL


lowercase__    :  Optional[Any]							    =	logging.get_logger(__name__)




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			Union[str, Any]							=		["""pixel_values"""]

 def __init__(     self		:       int  ,     lowercase_		:       bool = True  ,     lowercase_		:       Dict[str, int] = None  ,     lowercase_		:       float = None  ,     lowercase_		:       PILImageResampling = PILImageResampling.BILINEAR  ,     lowercase_		:       bool = True  ,     lowercase_		:       Union[int, float] = 1 / 255  ,     lowercase_		:       bool = True  ,     lowercase_		:       Optional[Union[float, List[float]]] = None  ,     lowercase_		:       Optional[Union[float, List[float]]] = None  ,     **lowercase_		:       str  ,     ):
     super().__init__(**lowercase_				)
     snake_case_ :     Optional[int]				=	size if size is not None else {'''shortest_edge''': 384}
     snake_case_ :     Dict				=	get_size_dict(lowercase_  ,     default_to_square=lowercase_				)

     snake_case_ :     List[Any]				=	do_resize
     snake_case_ :     str				=	size
     # Default value set here for backwards compatibility where the value in config is None
     snake_case_ :     Optional[Any]				=	crop_pct if crop_pct is not None else 224 / 256
     snake_case_ :     Optional[int]				=	resample
     snake_case_ :     Any				=	do_rescale
     snake_case_ :     Any				=	rescale_factor
     snake_case_ :     Optional[Any]				=	do_normalize
     snake_case_ :     Tuple				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     snake_case_ :     Tuple				=	image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Dict[str, int]  ,     lowercase_		:       float  ,     lowercase_		:       PILImageResampling = PILImageResampling.BICUBIC  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       Dict  ,     ):
     snake_case_ :     int				=	get_size_dict(lowercase_  ,     default_to_square=lowercase_				)
     if "shortest_edge" not in size:
         raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"				)
     snake_case_ :     Any				=	size['''shortest_edge''']

     if shortest_edge < 384:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         snake_case_ :     int				=	int(shortest_edge / crop_pct				)
         snake_case_ :     Tuple				=	get_resize_output_image_size(lowercase_  ,     size=lowercase_  ,     default_to_square=lowercase_				)
         snake_case_ :     str				=	resize(image=lowercase_  ,     size=lowercase_  ,     resample=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=lowercase_  ,     size=(shortest_edge, shortest_edge)  ,     data_format=lowercase_  ,     **lowercase_				)
     else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             lowercase_  ,     size=(shortest_edge, shortest_edge)  ,     resample=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Union[int, float]  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       Any  ,     ):
     return rescale(lowercase_  ,     scale=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       np.ndarray  ,     lowercase_		:       Union[float, List[float]]  ,     lowercase_		:       Union[float, List[float]]  ,     lowercase_		:       Optional[Union[str, ChannelDimension]] = None  ,     **lowercase_		:       Union[str, Any]  ,     ):
     return normalize(lowercase_  ,     mean=lowercase_  ,     std=lowercase_  ,     data_format=lowercase_  ,     **lowercase_				)






 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       ImageInput  ,     lowercase_		:       bool = None  ,     lowercase_		:       Dict[str, int] = None  ,     lowercase_		:       float = None  ,     lowercase_		:       PILImageResampling = None  ,     lowercase_		:       bool = None  ,     lowercase_		:       float = None  ,     lowercase_		:       bool = None  ,     lowercase_		:       Optional[Union[float, List[float]]] = None  ,     lowercase_		:       Optional[Union[float, List[float]]] = None  ,     lowercase_		:       Optional[Union[str, TensorType]] = None  ,     lowercase_		:       ChannelDimension = ChannelDimension.FIRST  ,     **lowercase_		:       Optional[int]  ,     ):
     snake_case_ :     Dict				=	do_resize if do_resize is not None else self.do_resize
     snake_case_ :     Tuple				=	crop_pct if crop_pct is not None else self.crop_pct
     snake_case_ :     Any				=	resample if resample is not None else self.resample
     snake_case_ :     List[Any]				=	do_rescale if do_rescale is not None else self.do_rescale
     snake_case_ :     Optional[int]				=	rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_ :     List[str]				=	do_normalize if do_normalize is not None else self.do_normalize
     snake_case_ :     Optional[int]				=	image_mean if image_mean is not None else self.image_mean
     snake_case_ :     List[Any]				=	image_std if image_std is not None else self.image_std

     snake_case_ :     Optional[Any]				=	size if size is not None else self.size
     snake_case_ :     List[str]				=	get_size_dict(lowercase_  ,     default_to_square=lowercase_				)

     snake_case_ :     List[Any]				=	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 or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
         raise ValueError('''crop_pct must be specified if size < 384.'''				)

     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.
     snake_case_ :     str				=	[to_numpy_array(lowercase_				) for image in images]

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

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

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

     snake_case_ :     Optional[int]				=	[to_channel_dimension_format(lowercase_  ,     lowercase_				) for image in images]

     snake_case_ :     Optional[Any]				=	{'''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,
)





