



"""simple docstring"""






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

from .. import config
from .logging import get_logger


__snake_case				:  Optional[int] =       get_logger(__name__)



class      A__  (    enum.Enum				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      "all_checks"
	SCREAMING_SNAKE_CASE        =      "basic_checks"
	SCREAMING_SNAKE_CASE        =      "no_checks"






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=None      )		->  Optional[int]:
			if expected_checksums is None:
						logger.info("Unable to verify checksums."      )
						return
			if len(set(__lowercase      ) - set(__lowercase      )      ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(__lowercase      ) - set(__lowercase      )      )      )
			if len(set(__lowercase      ) - set(__lowercase      )      ) > 0:
						raise UnexpectedDownloadedFile(str(set(__lowercase      ) - set(__lowercase      )      )      )
			__lowerCAmelCase :    List[str]				=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__lowerCAmelCase :    List[str]				=			''' for ''' + verification_name if verification_name is not None else ''''''
			if len(__lowercase      ) > 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__  (    __snake_case				):




	'''simple docstring'''





	pass






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






class      A__  (    __snake_case				):




	'''simple docstring'''





	pass






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Union[str, Any]:
			if expected_splits is None:
						logger.info("Unable to verify splits sizes."      )
						return
			if len(set(__lowercase      ) - set(__lowercase      )      ) > 0:
						raise ExpectedMoreSplits(str(set(__lowercase      ) - set(__lowercase      )      )      )
			if len(set(__lowercase      ) - set(__lowercase      )      ) > 0:
						raise UnexpectedSplits(str(set(__lowercase      ) - set(__lowercase      )      )      )
			__lowerCAmelCase :    Union[str, Any]				=			[
			    {'''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(__lowercase      ) > 0:
						raise NonMatchingSplitsSizesError(str(__lowercase      )      )
			logger.info("All the splits matched successfully."      )


def      _lowercase							(    __snake_case   ,__snake_case = True      )		->  dict:
			if record_checksum:
						__lowerCAmelCase :    str				=			shaaaa()
						with open(__lowercase   ,"rb"      ) as f:
									for chunk in iter(lambda: f.read(1 << 20      )   ,B""      ):
												m.update(__lowercase      )
									__lowerCAmelCase :    str				=			m.hexdigest()
			else:
						__lowerCAmelCase :    Optional[int]				=			None
			return {"num_bytes": os.path.getsize(__lowercase      ), "checksum": checksum}





def      _lowercase							(    __snake_case      )		->  Tuple:
			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

_UpperCamelCase			:	Union[str, Any]								=logging.get_logger(__name__)



def  a__ (__lowercase							:str   )		-> List[List[ImageInput]]:
				if isinstance(__lowercase					,  (list, tuple)   ) and isinstance(videos[0]					,  (list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

				elif isinstance(__lowercase					,  (list, tuple)   ) and is_valid_image(videos[0]   ):
								return [videos]

				elif is_valid_image(__lowercase   ):
								return [[videos]]

				raise ValueError(f"""Could not make batched video from {videos}"""   )






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BILINEAR       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = True       ,A__ = None       ,A__ = None       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				Tuple   =      size if size is not None else {'''shortest_edge''': 256}
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				str   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				str   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						_A	:				Tuple   =      do_resize
						_A	:				Optional[Any]   =      size
						_A	:				Optional[Any]   =      do_center_crop
						_A	:				List[str]   =      crop_size
						_A	:				Dict   =      resample
						_A	:				Tuple   =      do_rescale
						_A	:				int   =      rescale_factor
						_A	:				List[str]   =      offset
						_A	:				List[Any]   =      do_normalize
						_A	:				List[Any]   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A	:				List[str]   =      image_std if image_std is not None else IMAGENET_STANDARD_STD







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BILINEAR       ,A__ = None       ,**A__       ,):
						_A	:				str   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" in size:
										_A	:				Optional[Any]   =      get_resize_output_image_size(A__       ,size['''shortest_edge''']       ,default_to_square=A__		)
						elif "height" in size and "width" in size:
										_A	:				str   =      (size['''height'''], size['''width'''])
						else:
										raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Optional[int]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				Any   =      image.astype(np.floataa		)
						if offset:
										_A	:				List[str]   =      image - (scale / 2)
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,):
						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_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.'''		)

						if offset and not do_rescale:
										raise ValueError('''For offset, do_rescale must also be set to True.'''		)

						# All transformations expect numpy arrays.
						_A	:				Dict   =      to_numpy_array(A__		)

						if do_resize:
										_A	:				Any   =      self.resize(image=A__       ,size=A__       ,resample=A__		)

						if do_center_crop:
										_A	:				str   =      self.center_crop(A__       ,size=A__		)

						if do_rescale:
										_A	:				Optional[int]   =      self.rescale(image=A__       ,scale=A__       ,offset=A__		)

						if do_normalize:
										_A	:				Dict   =      self.normalize(image=A__       ,mean=A__       ,std=A__		)

						_A	:				Union[str, Any]   =      to_channel_dimension_format(A__       ,A__		)
						return image







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				Optional[int]   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      resample if resample is not None else self.resample
						_A	:				Union[str, Any]   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Tuple   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				List[str]   =      offset if offset is not None else self.offset
						_A	:				Optional[int]   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Optional[Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				Dict   =      image_std if image_std is not None else self.image_std

						_A	:				str   =      size if size is not None else self.size
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				Optional[int]   =      get_size_dict(A__       ,param_name='''crop_size'''		)

						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.'''		)

						_A	:				List[str]   =      make_batched(A__		)

						_A	:				Tuple   =      [
						    [
						        self._preprocess_image(
						            image=A__       ,do_resize=A__       ,size=A__       ,resample=A__       ,do_center_crop=A__       ,crop_size=A__       ,do_rescale=A__       ,rescale_factor=A__       ,offset=A__       ,do_normalize=A__       ,image_mean=A__       ,image_std=A__       ,data_format=A__       ,)
						        for img in video
						    ]
						    for video in videos
						]

						_A	:				Optional[int]   =      {'''pixel_values''': videos}
						return BatchFeature(data=A__       ,tensor_type=A__		)



