


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


UpperCAmelCase_   :				Dict						      = None
UpperCAmelCase_   :				Union[str, Any]						      = '<' 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
UpperCAmelCase_   :				str						      = [
    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 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  int       =		True
      snake_case__       :  str       =		None
      # Automatically constructed
      snake_case__       :  Union[str, Any]       =		'''PIL.Image.Image'''
      snake_case__       :  int       =		pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}    )
      snake_case__       :  List[str]       =		field(default='''Image'''		,					init=__lowerCamelCase		,					repr=__lowerCamelCase    )
      def __call__( self       : Union[str, Any]  )	->    Dict:
             return self.pa_type
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]  )	->    str:
             if config.PIL_AVAILABLE:
                    import PIL.Image
             else:
                    raise ImportError('To support encoding images, please install \'Pillow\'.'  )

             if isinstance(UpperCAmelCase_  ,							UpperCAmelCase_  ):
                    a_      :	int							=						np.array(UpperCAmelCase_  )

             if isinstance(UpperCAmelCase_  ,							UpperCAmelCase_  ):
                    return {"path": value, "bytes": None}
             elif isinstance(UpperCAmelCase_  ,							UpperCAmelCase_  ):
                    return {"path": None, "bytes": value}
             elif isinstance(UpperCAmelCase_  ,							np.ndarray  ):
                    # convert the image array to PNG/TIFF bytes
                    return encode_np_array(UpperCAmelCase_  )
             elif isinstance(UpperCAmelCase_  ,							PIL.Image.Image  ):
                    # convert the PIL image to bytes (default format is PNG/TIFF)
                    return encode_pil_image(UpperCAmelCase_  )
             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  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : dict  ,							SCREAMING_SNAKE_CASE__       : int=None  )	->    Any:
             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:
                    a_      :	int							=						{}

             a_      :	int							=						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(UpperCAmelCase_  ):
                                  a_      :	int							=						PIL.Image.open(UpperCAmelCase_  )
                           else:
                                  a_      :	str							=						path.split('::'  )[-1]
                                  try:
                                         a_      :	Union[str, Any]							=						string_to_dict(UpperCAmelCase_  ,							config.HUB_DATASETS_URL  )['repo_id']
                                         a_      :	Optional[Any]							=						token_per_repo_id.get(UpperCAmelCase_  )
                                  except ValueError:
                                         a_      :	Any							=						None
                                  with xopen(UpperCAmelCase_  ,							'rb'  ,							use_auth_token=UpperCAmelCase_  ) as f:
                                         a_      :	Optional[int]							=						BytesIO(f.read()  )
                                  a_      :	Tuple							=						PIL.Image.open(bytes_  )
             else:
                    a_      :	List[Any]							=						PIL.Image.open(BytesIO(bytes_  )  )
             image.load()  # to avoid "Too many open files" errors
             return image
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             from .features import Value

             return (
                 self
                 if self.decode
                 else {
                     "bytes": Value('binary'  ),
                     "path": Value('string'  ),
                 }
             )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[pa.StringArray, pa.StructArray, pa.ListArray]  )	->    int:
             if pa.types.is_string(storage.type  ):
                    a_      :	Dict							=						pa.array([None] * len(UpperCAmelCase_  )  ,							type=pa.binary()  )
                    a_      :	Union[str, Any]							=						pa.StructArray.from_arrays([bytes_array, storage]  ,							['bytes', 'path']  ,							mask=storage.is_null()  )
             elif pa.types.is_binary(storage.type  ):
                    a_      :	Tuple							=						pa.array([None] * len(UpperCAmelCase_  )  ,							type=pa.string()  )
                    a_      :	Optional[int]							=						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:
                           a_      :	Tuple							=						storage.field('bytes'  )
                    else:
                           a_      :	Tuple							=						pa.array([None] * len(UpperCAmelCase_  )  ,							type=pa.binary()  )
                    if storage.type.get_field_index('path'  ) >= 0:
                           a_      :	int							=						storage.field('path'  )
                    else:
                           a_      :	List[str]							=						pa.array([None] * len(UpperCAmelCase_  )  ,							type=pa.string()  )
                    a_      :	Dict							=						pa.StructArray.from_arrays([bytes_array, path_array]  ,							['bytes', 'path']  ,							mask=storage.is_null()  )
             elif pa.types.is_list(storage.type  ):
                    a_      :	Optional[Any]							=						pa.array(
                        [encode_np_array(np.array(UpperCAmelCase_  )  )['bytes'] if arr is not None else None for arr in storage.to_pylist()]  ,							type=pa.binary()  ,							)
                    a_      :	Optional[Any]							=						pa.array([None] * len(UpperCAmelCase_  )  ,							type=pa.string()  )
                    a_      :	Optional[int]							=						pa.StructArray.from_arrays(
                        [bytes_array, path_array]  ,							['bytes', 'path']  ,							mask=bytes_array.is_null()  )
             return array_cast(UpperCAmelCase_  ,							self.pa_type  )

      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : pa.StructArray  )	->    List[Any]:

             @no_op_if_value_is_null
             def path_to_bytes(SCREAMING_SNAKE_CASE__       : int  ):
                    with xopen(UpperCAmelCase_  ,							'rb'  ) as f:
                           a_      :	List[str]							=						f.read()
                    return bytes_

             a_      :	Optional[Any]							=						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()  ,							)
             a_      :	List[Any]							=						pa.array(
                 [os.path.basename(UpperCAmelCase_  ) if path is not None else None for path in storage.field('path'  ).to_pylist()]  ,							type=pa.string()  ,							)
             a_      :	str							=						pa.StructArray.from_arrays([bytes_array, path_array]  ,							['bytes', 'path']  ,							mask=bytes_array.is_null()  )
             return array_cast(UpperCAmelCase_  ,							self.pa_type  )







def  SCREAMING_SNAKE_CASE_		(    )			->	List[str]:

       """simple docstring"""

       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()
              a_      :	Tuple							=						list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
       return _IMAGE_COMPRESSION_FORMATS







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	bytes:

       """simple docstring"""

       a_      :	List[Any]							=						BytesIO()
       if image.format in list_image_compression_formats():
              a_      :	Dict							=						image.format
       else:
              a_      :	List[str]							=						'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
       image.save(lowerCamelCase_						,					format=lowerCamelCase_       )
       return buffer.getvalue()







def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	dict:

       """simple docstring"""

       if hasattr(lowerCamelCase_						,					'filename'       ) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(lowerCamelCase_       )}







def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	dict:

       """simple docstring"""

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

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

       a_      :	List[str]							=						None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              a_      :	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:
              a_      :	Union[str, Any]							=						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:
                     a_      :	List[str]							=						dtype_byteorder + dtype_kind + str(lowerCamelCase_       )
                     a_      :	Any							=						np.dtype(lowerCamelCase_       )
                     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}"""       )
       a_      :	Optional[int]							=						PIL.Image.fromarray(array.astype(lowerCamelCase_       )       )
       return {"path": None, "bytes": image_to_bytes(lowerCamelCase_       )}







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]       )			->	List[dict]:

       """simple docstring"""

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

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






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


