





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

SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[List[ImageInput]]:


      '''simple docstring'''


      if isinstance(_SCREAMING_SNAKE_CASE				,		(list, tuple)			) and isinstance(videos[0]				,		(list, tuple)			) and is_valid_image(videos[0][0]			):
            return videos

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

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

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



class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			Optional[int]				=    ["pixel_values"]






     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					PILImageResampling = PILImageResampling.BILINEAR		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Union[int, float] = 1 / 255		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,**lowerCamelCase__		:					List[str]		,)  ->	None:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		size if size is not None else {"""shortest_edge""": 256}
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,default_to_square=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,param_name="""crop_size""" )

           SCREAMING_SNAKE_CASE	=		do_resize
           SCREAMING_SNAKE_CASE	=		size
           SCREAMING_SNAKE_CASE	=		do_center_crop
           SCREAMING_SNAKE_CASE	=		crop_size
           SCREAMING_SNAKE_CASE	=		resample
           SCREAMING_SNAKE_CASE	=		do_rescale
           SCREAMING_SNAKE_CASE	=		rescale_factor
           SCREAMING_SNAKE_CASE	=		offset
           SCREAMING_SNAKE_CASE	=		do_normalize
           SCREAMING_SNAKE_CASE	=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           SCREAMING_SNAKE_CASE	=		image_std if image_std is not None else IMAGENET_STANDARD_STD






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Dict[str, int]		,lowerCamelCase__		:					PILImageResampling = PILImageResampling.BILINEAR		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					Any		,)  ->	np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,default_to_square=lowerCamelCase__ )
           if "shortest_edge" in size:
                 SCREAMING_SNAKE_CASE	=		get_resize_output_image_size(lowerCamelCase__		,size["""shortest_edge"""]		,default_to_square=lowerCamelCase__ )
           elif "height" in size and "width" in size:
                 SCREAMING_SNAKE_CASE	=		(size["""height"""], size["""width"""])
           else:
                 raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
           return resize(lowerCamelCase__		,size=lowerCamelCase__		,resample=lowerCamelCase__		,data_format=lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Dict[str, int]		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					int		,)  ->	np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__ )
           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(lowerCamelCase__		,size=(size["""height"""], size["""width"""])		,data_format=lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Union[int, float]		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					int		,)  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		image.astype(np.floataa )
           if offset:
                 SCREAMING_SNAKE_CASE	=		image - (scale / 2)
           return rescale(lowerCamelCase__		,scale=lowerCamelCase__		,data_format=lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					np.ndarray		,lowerCamelCase__		:					Union[float, List[float]]		,lowerCamelCase__		:					Union[float, List[float]]		,lowerCamelCase__		:					Optional[Union[str, ChannelDimension]] = None		,**lowerCamelCase__		:					List[Any]		,)  ->	np.ndarray:




           '''simple docstring'''


           return normalize(lowerCamelCase__		,mean=lowerCamelCase__		,std=lowerCamelCase__		,data_format=lowerCamelCase__		,**lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					ImageInput		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					PILImageResampling = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					float = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[ChannelDimension] = ChannelDimension.FIRST		,)  ->	np.ndarray:




           '''simple docstring'''


           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.
           SCREAMING_SNAKE_CASE	=		to_numpy_array(lowerCamelCase__ )

           if do_resize:
                 SCREAMING_SNAKE_CASE	=		self.resize(image=lowerCamelCase__		,size=lowerCamelCase__		,resample=lowerCamelCase__ )

           if do_center_crop:
                 SCREAMING_SNAKE_CASE	=		self.center_crop(lowerCamelCase__		,size=lowerCamelCase__ )

           if do_rescale:
                 SCREAMING_SNAKE_CASE	=		self.rescale(image=lowerCamelCase__		,scale=lowerCamelCase__		,offset=lowerCamelCase__ )

           if do_normalize:
                 SCREAMING_SNAKE_CASE	=		self.normalize(image=lowerCamelCase__		,mean=lowerCamelCase__		,std=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		to_channel_dimension_format(lowerCamelCase__		,lowerCamelCase__ )
           return image






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					ImageInput		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					PILImageResampling = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Dict[str, int] = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					float = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[float, List[float]]] = None		,lowerCamelCase__		:					Optional[Union[str, TensorType]] = None		,lowerCamelCase__		:					ChannelDimension = ChannelDimension.FIRST		,**lowerCamelCase__		:					Optional[int]		,)  ->	PIL.Image.Image:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		do_resize if do_resize is not None else self.do_resize
           SCREAMING_SNAKE_CASE	=		resample if resample is not None else self.resample
           SCREAMING_SNAKE_CASE	=		do_center_crop if do_center_crop is not None else self.do_center_crop
           SCREAMING_SNAKE_CASE	=		do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE	=		rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE	=		offset if offset is not None else self.offset
           SCREAMING_SNAKE_CASE	=		do_normalize if do_normalize is not None else self.do_normalize
           SCREAMING_SNAKE_CASE	=		image_mean if image_mean is not None else self.image_mean
           SCREAMING_SNAKE_CASE	=		image_std if image_std is not None else self.image_std

           SCREAMING_SNAKE_CASE	=		size if size is not None else self.size
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,default_to_square=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		crop_size if crop_size is not None else self.crop_size
           SCREAMING_SNAKE_CASE	=		get_size_dict(lowerCamelCase__		,param_name="""crop_size""" )

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

           SCREAMING_SNAKE_CASE	=		make_batched(lowerCamelCase__ )

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

           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": videos}
           return BatchFeature(data=lowerCamelCase__		,tensor_type=lowerCamelCase__ )









import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      for line in lines:
            SCREAMING_SNAKE_CASE	=		re.sub(r"""#.*"""				,		""""""				,		_SCREAMING_SNAKE_CASE			)  # remove comments
            if line:
                  filtered_lines.append(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		"""\n""".join(_SCREAMING_SNAKE_CASE			)

      # Make a hash from all this code
      SCREAMING_SNAKE_CASE	=		full_str.encode("""utf-8"""			)
      return shaaaa(_SCREAMING_SNAKE_CASE			).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_		     = {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_		     = {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_		     = {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_		     = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
      _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


