'''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, logging


SCREAMING_SNAKE_CASE_:	int    =logging.get_logger(__name__)




class 			__A (					UpperCamelCase__      ):
    a__			:		Union[str, Any]			  =    ["""pixel_values"""]



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



    def    _lowercase		(self       :       Dict  ,     __a       :       np.ndarray  ,     __a       :       Dict[str, int]  ,     __a       :       PILImageResampling = PILImageResampling.BICUBIC  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       List[Any]  ,     ):
         UpperCAmelCase_			       =  get_size_dict(__a  ,     default_to_square=__a					)
         if "shortest_edge" not in size:
              raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
         UpperCAmelCase_			       =  get_resize_output_image_size(__a  ,     size=size["shortest_edge"]  ,     default_to_square=__a					)
         return resize(__a  ,     size=__a  ,     resample=__a  ,     data_format=__a  ,     **__a					)



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       np.ndarray  ,     __a       :       Dict[str, int]  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       Optional[Any]  ,     ):
         UpperCAmelCase_			       =  get_size_dict(__a					)
         return center_crop(__a  ,     size=(size["height"], size["width"])  ,     data_format=__a  ,     **__a					)



    def    _lowercase		(self       :       int  ,     __a       :       np.ndarray  ,     __a       :       float  ,     __a       :       Optional[Union[str, ChannelDimension]] = None  ,     **__a       :       List[str]					):
         return rescale(__a  ,     scale=__a  ,     data_format=__a  ,     **__a					)



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






    def    _lowercase		(self       :       str  ,     __a       :       ImageInput  ,     __a       :       Optional[bool] = None  ,     __a       :       Dict[str, int] = None  ,     __a       :       PILImageResampling = None  ,     __a       :       bool = None  ,     __a       :       Dict[str, int] = None  ,     __a       :       Optional[bool] = None  ,     __a       :       Optional[float] = None  ,     __a       :       Optional[bool] = None  ,     __a       :       Optional[Union[float, List[float]]] = None  ,     __a       :       Optional[Union[float, List[float]]] = None  ,     __a       :       Optional[Union[str, TensorType]] = None  ,     __a       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,     **__a       :       List[str]  ,     ):
         UpperCAmelCase_			       =  do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_			       =  size if size is not None else self.size
         UpperCAmelCase_			       =  get_size_dict(__a  ,     default_to_square=__a					)
         UpperCAmelCase_			       =  resample if resample is not None else self.resample
         UpperCAmelCase_			       =  do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_			       =  crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_			       =  get_size_dict(__a					)
         UpperCAmelCase_			       =  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_			       =  do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_			       =  image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_			       =  image_std if image_std is not None else self.image_std

         UpperCAmelCase_			       =  make_list_of_images(__a					)

         if not valid_images(__a					):
              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.
         UpperCAmelCase_			       =  [to_numpy_array(__a					) for image in images]

         if do_resize:
              UpperCAmelCase_			       =  [self.resize(image=__a  ,     size=__a  ,     resample=__a					) for image in images]

         if do_center_crop:
              UpperCAmelCase_			       =  [self.center_crop(image=__a  ,     size=__a					) for image in images]

         if do_rescale:
              UpperCAmelCase_			       =  [self.rescale(image=__a  ,     scale=__a					) for image in images]

         if do_normalize:
              UpperCAmelCase_			       =  [self.normalize(image=__a  ,     mean=__a  ,     std=__a					) for image in images]

         UpperCAmelCase_			       =  [to_channel_dimension_format(__a  ,     __a					) for image in images]

         UpperCAmelCase_			       =  {"pixel_values": images}
         return BatchFeature(data=__a  ,     tensor_type=__a					)






"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_lowercase							=				get_logger(__name__)

class 					__a     ( enum.Enum       ):



							'''simple docstring'''







							_lowerCamelCase					:			Tuple          =   """all_checks"""
							_lowerCamelCase					:			Optional[int]          =   """basic_checks"""
							_lowerCamelCase					:			str          =   """no_checks"""





class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__=None      )						->Union[str, Any]:
			if expected_checksums is None:
						logger.info("Unable to verify checksums."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedDownloadedFile(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__lowercase       =       " for " + verification_name if verification_name is not None else ""
			if len(__magic_name__      ) > 0:
						raise NonMatchingChecksumError(
						    F'''Checksums didn\'t match{for_verification_name}:\n'''
						    F'''{bad_urls}\n'''
						    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"      )
			logger.info("All the checksums matched successfully" + for_verification_name      )

class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Any:
			if expected_splits is None:
						logger.info("Unable to verify splits sizes."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [
			    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(__magic_name__      ) > 0:
						raise NonMatchingSplitsSizesError(str(__magic_name__      )      )
			logger.info("All the splits matched successfully."      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__ = True      )						->dict:
			if record_checksum:
						__lowercase       =       shaaaa()
						with open(__magic_name__			,   "rb"      ) as f:
									for chunk in iter(lambda: f.read(1 << 2_0      )			,   b""      ):
												m.update(__magic_name__      )
									__lowercase       =       m.hexdigest()
			else:
						__lowercase       =       None
			return {"num_bytes": os.path.getsize(__magic_name__      ), "checksum": checksum}







def      lowerCAmelCase__			(		__magic_name__      )						->List[str]:
			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False





