






'''simple docstring'''





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


SCREAMING_SNAKE_CASE 							=						None
SCREAMING_SNAKE_CASE 							=						'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE 							=						[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]
@dataclass
class 							A_    :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			bool =					True
 _SCREAMING_SNAKE_CASE							:			Optional[str] =					None
 # Automatically constructed
 _SCREAMING_SNAKE_CASE							:			ClassVar[str] =					"PIL.Image.Image"
 _SCREAMING_SNAKE_CASE							:			ClassVar[Any] =					pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
 _SCREAMING_SNAKE_CASE							:			str =					field(default="Image" ,   init=__lowercase ,   repr=__lowercase			)


 def __call__(	self) ->		int:

     """simple docstring"""



     return self.pa_type


 def  snake_case__				(	self							,  _A) ->		dict:

     """simple docstring"""



     if config.PIL_AVAILABLE:
         import PIL.Image
     else:
         raise ImportError('''To support encoding images, please install \'Pillow\'.''')

     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      Union[str, Any]			  =				np.array(_A)

     if isinstance(_A							,  _A):
         return {"path": value, "bytes": None}
     elif isinstance(_A							,  _A):
         return {"path": None, "bytes": value}
     elif isinstance(_A							,  np.ndarray):
         # convert the image array to PNG/TIFF bytes
         return encode_np_array(_A)
     elif isinstance(_A							,  PIL.Image.Image):
         # convert the PIL image to bytes (default format is PNG/TIFF)
         return encode_pil_image(_A)
     elif value.get('''path''') is not None and os.path.isfile(value['''path''']):
         # we set "bytes": None to not duplicate the data if they're already available locally
         return {"bytes": None, "path": value.get('''path''')}
     elif value.get('''bytes''') is not None or value.get('''path''') is not None:
         # store the image bytes, and path is used to infer the image format using the file extension
         return {"bytes": value.get('''bytes'''), "path": value.get('''path''')}
     else:
         raise ValueError(
             f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''')


 def  snake_case__				(	self							,  _A							,  _A=None) ->		"PIL.Image.Image":

     """simple docstring"""



     if not self.decode:
         raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''')

     if config.PIL_AVAILABLE:
         import PIL.Image
     else:
         raise ImportError('''To support decoding images, please install \'Pillow\'.''')

     if token_per_repo_id is None:
         _UpperCAmelCase				:      List[Any]			  =				{}

     _UpperCAmelCase				:      List[str]			  =				value['''path'''], value['''bytes''']
     if bytes_ is None:
         if path is None:
             raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''')
         else:
             if is_local_path(_A):
                 _UpperCAmelCase				:      str			  =				PIL.Image.open(_A)
             else:
                 _UpperCAmelCase				:      Any			  =				path.split('''::''')[-1]
                 try:
                     _UpperCAmelCase				:      Optional[Any]			  =				string_to_dict(_A							,  config.HUB_DATASETS_URL)['''repo_id''']
                     _UpperCAmelCase				:      Any			  =				token_per_repo_id.get(_A)
                 except ValueError:
                     _UpperCAmelCase				:      int			  =				None
                 with xopen(_A							,  '''rb'''							,  use_auth_token=_A) as f:
                     _UpperCAmelCase				:      Optional[Any]			  =				BytesIO(f.read())
                 _UpperCAmelCase				:      Any			  =				PIL.Image.open(bytes_)
     else:
         _UpperCAmelCase				:      Union[str, Any]			  =				PIL.Image.open(BytesIO(bytes_))
     image.load()  # to avoid "Too many open files" errors
     return image


 def  snake_case__				(	self) ->		Union["FeatureType", Dict[str, "FeatureType"]]:

     """simple docstring"""



     from .features import Value

     return (
         self
         if self.decode
         else {
             "bytes": Value('''binary'''),
             "path": Value('''string'''),
         }
     )


 def  snake_case__				(	self							,  _A) ->		pa.StructArray:

     """simple docstring"""



     if pa.types.is_string(storage.type):
         _UpperCAmelCase				:      Tuple			  =				pa.array([None] * len(_A)							,  type=pa.binary())
         _UpperCAmelCase				:      Dict			  =				pa.StructArray.from_arrays([bytes_array, storage]							,  ['''bytes''', '''path''']							,  mask=storage.is_null())
     elif pa.types.is_binary(storage.type):
         _UpperCAmelCase				:      Any			  =				pa.array([None] * len(_A)							,  type=pa.string())
         _UpperCAmelCase				:      Union[str, Any]			  =				pa.StructArray.from_arrays([storage, path_array]							,  ['''bytes''', '''path''']							,  mask=storage.is_null())
     elif pa.types.is_struct(storage.type):
         if storage.type.get_field_index('''bytes''') >= 0:
             _UpperCAmelCase				:      List[str]			  =				storage.field('''bytes''')
         else:
             _UpperCAmelCase				:      List[Any]			  =				pa.array([None] * len(_A)							,  type=pa.binary())
         if storage.type.get_field_index('''path''') >= 0:
             _UpperCAmelCase				:      Optional[int]			  =				storage.field('''path''')
         else:
             _UpperCAmelCase				:      Union[str, Any]			  =				pa.array([None] * len(_A)							,  type=pa.string())
         _UpperCAmelCase				:      List[str]			  =				pa.StructArray.from_arrays([bytes_array, path_array]							,  ['''bytes''', '''path''']							,  mask=storage.is_null())
     elif pa.types.is_list(storage.type):
         _UpperCAmelCase				:      Optional[int]			  =				pa.array(
             [encode_np_array(np.array(_A))['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]							,  type=pa.binary()							,  )
         _UpperCAmelCase				:      Tuple			  =				pa.array([None] * len(_A)							,  type=pa.string())
         _UpperCAmelCase				:      Optional[Any]			  =				pa.StructArray.from_arrays(
             [bytes_array, path_array]							,  ['''bytes''', '''path''']							,  mask=bytes_array.is_null())
     return array_cast(_A							,  self.pa_type)


 def  snake_case__				(	self							,  _A) ->		pa.StructArray:

     """simple docstring"""



     @no_op_if_value_is_null
     def path_to_bytes(_A):
         with xopen(_A							,  '''rb''') as f:
             _UpperCAmelCase				:      Optional[int]			  =				f.read()
         return bytes_

     _UpperCAmelCase				:      Dict			  =				pa.array(
         [
             (path_to_bytes(x['''path''']) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
             for x in storage.to_pylist()
         ]							,  type=pa.binary()							,  )
     _UpperCAmelCase				:      Tuple			  =				pa.array(
         [os.path.basename(_A) if path is not None else None for path in storage.field('''path''').to_pylist()]							,  type=pa.string()							,  )
     _UpperCAmelCase				:      Dict			  =				pa.StructArray.from_arrays([bytes_array, path_array]							,  ['''bytes''', '''path''']							,  mask=bytes_array.is_null())
     return array_cast(_A							,  self.pa_type)



def      _lowerCamelCase	(						)      ->					List[str]:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

    global _IMAGE_COMPRESSION_FORMATS
    if _IMAGE_COMPRESSION_FORMATS is None:
        PIL.Image.init()
        _UpperCAmelCase				:      Optional[Any]			  =				list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
    return _IMAGE_COMPRESSION_FORMATS

def      _lowerCamelCase	(						__A    :							"PIL.Image.Image" )      ->					bytes:
    _UpperCAmelCase				:      Optional[Any]			  =				BytesIO()
    if image.format in list_image_compression_formats():
        _UpperCAmelCase				:      Any			  =				image.format
    else:
        _UpperCAmelCase				:      List[str]			  =				'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
    image.save(__A		,		format=__A )
    return buffer.getvalue()

def      _lowerCamelCase	(						__A    :							"PIL.Image.Image" )      ->					dict:
    if hasattr(__A		,		'''filename''' ) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(__A )}

def      _lowerCamelCase	(						__A    :							np.ndarray )      ->					dict:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

    _UpperCAmelCase				:      Union[str, Any]			  =				array.dtype
    _UpperCAmelCase				:      str			  =				dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
    _UpperCAmelCase				:      Optional[Any]			  =				dtype.kind
    _UpperCAmelCase				:      Union[str, Any]			  =				dtype.itemsize

    _UpperCAmelCase				:      Union[str, Any]			  =				None

    # Multi-channel array case (only np.dtype("|u1") is allowed)
    if array.shape[2:]:
        _UpperCAmelCase				:      Union[str, Any]			  =				np.dtype('''|u1''' )
        if dtype_kind not in ["u", "i"]:
            raise TypeError(
                f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
        if dtype is not dest_dtype:
            warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
    # Exact match
    elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
        _UpperCAmelCase				:      int			  =				dtype
    else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
        while dtype_itemsize >= 1:
            _UpperCAmelCase				:      int			  =				dtype_byteorder + dtype_kind + str(__A )
            _UpperCAmelCase				:      List[Any]			  =				np.dtype(__A )
            if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
                break
            else:
                dtype_itemsize //= 2

    if dest_dtype is None:
        raise TypeError(
            f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
    _UpperCAmelCase				:      Any			  =				PIL.Image.fromarray(array.astype(__A ) )
    return {"path": None, "bytes": image_to_bytes(__A )}


def      _lowerCamelCase	(						__A    :							Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] )      ->					List[dict]:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

    if objs:
        _UpperCAmelCase				:      List[str]			  =				first_non_null_value(__A )
        if isinstance(__A		,		__A ):
            return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
        if isinstance(__A		,		np.ndarray ):
            _UpperCAmelCase				:      Any			  =				no_op_if_value_is_null(__A )
            return [obj_to_image_dict_func(__A ) for obj in objs]
        elif isinstance(__A		,		PIL.Image.Image ):
            _UpperCAmelCase				:      List[str]			  =				no_op_if_value_is_null(__A )
            return [obj_to_image_dict_func(__A ) for obj in objs]
        else:
            return objs
    else:
        return objs












def      _lowerCamelCase	(						__A    :							int )      ->					bool:
    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      Tuple			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )
    if number < 0:
        return False
    _UpperCAmelCase				:      List[str]			  =				number * number
    while number > 0:
        if number % 10 != number_square % 10:
            return False
        number //= 10
        number_square //= 10
    return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()




