"""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__:Optional[List[str]]			=	None
SCREAMING_SNAKE_CASE__:List[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
SCREAMING_SNAKE_CASE__:Dict			=	[
    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 				snake_case__     :
 _snake_case   :			bool           =		True
 _snake_case   :			Optional[str]           =		None
 # Automatically constructed
 _snake_case   :			ClassVar[str]           =		"PIL.Image.Image"
 _snake_case   :			ClassVar[Any]           =		pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}   )
 _snake_case   :			str           =		field(default="""Image""",		init=snake_case_,		repr=snake_case_   )


 def __call__(				self		):
        return self.pa_type


 def 		a__ (				self  ,						lowerCamelCase		):
        if config.PIL_AVAILABLE:
               import PIL.Image
        else:
               raise ImportError("To support encoding images, please install 'Pillow'."		)

        if isinstance(lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  np.array(lowerCamelCase		)

        if isinstance(lowerCamelCase  ,						lowerCamelCase		):
               return {"path": value, "bytes": None}
        elif isinstance(lowerCamelCase  ,						lowerCamelCase		):
               return {"path": None, "bytes": value}
        elif isinstance(lowerCamelCase  ,						np.ndarray		):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(lowerCamelCase		)
        elif isinstance(lowerCamelCase  ,						PIL.Image.Image		):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(lowerCamelCase		)
        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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None		):
        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     				=  {}

        __a				, __a     				=  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(lowerCamelCase		):
                             __a     				=  PIL.Image.open(lowerCamelCase		)
                      else:
                             __a     				=  path.split("::"		)[-1]
                             try:
                                    __a     				=  string_to_dict(lowerCamelCase  ,						config.HUB_DATASETS_URL		)["repo_id"]
                                    __a     				=  token_per_repo_id.get(lowerCamelCase		)
                             except ValueError:
                                    __a     				=  None
                             with xopen(lowerCamelCase  ,						"rb"  ,						use_auth_token=lowerCamelCase		) as f:
                                    __a     				=  BytesIO(f.read()		)
                             __a     				=  PIL.Image.open(bytes_		)
        else:
               __a     				=  PIL.Image.open(BytesIO(bytes_		)		)
        image.load()  # to avoid "Too many open files" errors
        return image


 def 		a__ (				self		):
        from .features import Value

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


 def 		a__ (				self  ,						lowerCamelCase		):
        if pa.types.is_string(storage.type		):
               __a     				=  pa.array([None] * len(lowerCamelCase		)  ,						type=pa.binary()		)
               __a     				=  pa.StructArray.from_arrays([bytes_array, storage]  ,						["bytes", "path"]  ,						mask=storage.is_null()		)
        elif pa.types.is_binary(storage.type		):
               __a     				=  pa.array([None] * len(lowerCamelCase		)  ,						type=pa.string()		)
               __a     				=  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     				=  storage.field("bytes"		)
               else:
                      __a     				=  pa.array([None] * len(lowerCamelCase		)  ,						type=pa.binary()		)
               if storage.type.get_field_index("path"		) >= 0:
                      __a     				=  storage.field("path"		)
               else:
                      __a     				=  pa.array([None] * len(lowerCamelCase		)  ,						type=pa.string()		)
               __a     				=  pa.StructArray.from_arrays([bytes_array, path_array]  ,						["bytes", "path"]  ,						mask=storage.is_null()		)
        elif pa.types.is_list(storage.type		):
               __a     				=  pa.array(
                   [encode_np_array(np.array(lowerCamelCase		)		)["bytes"] if arr is not None else None for arr in storage.to_pylist()]  ,						type=pa.binary()  ,						)
               __a     				=  pa.array([None] * len(lowerCamelCase		)  ,						type=pa.string()		)
               __a     				=  pa.StructArray.from_arrays(
                   [bytes_array, path_array]  ,						["bytes", "path"]  ,						mask=bytes_array.is_null()		)
        return array_cast(lowerCamelCase  ,						self.pa_type		)






 def 		a__ (				self  ,						lowerCamelCase		):

        @no_op_if_value_is_null
        def path_to_bytes(lowerCamelCase		):
               with xopen(lowerCamelCase  ,						"rb"		) as f:
                      __a     				=  f.read()
               return bytes_

        __a     				=  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     				=  pa.array(
            [os.path.basename(lowerCamelCase		) if path is not None else None for path in storage.field("path"		).to_pylist()]  ,						type=pa.string()  ,						)
        __a     				=  pa.StructArray.from_arrays([bytes_array, path_array]  ,						["bytes", "path"]  ,						mask=bytes_array.is_null()		)
        return array_cast(lowerCamelCase  ,						self.pa_type		)



def 					_lowerCamelCase(					):
       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     				=  list(set(PIL.Image.OPEN.keys()							) & set(PIL.Image.SAVE.keys()							)							)
       return _IMAGE_COMPRESSION_FORMATS



def 					_lowerCamelCase(					a							):
       __a     				=  BytesIO()
       if image.format in list_image_compression_formats():
              __a     				=  image.format
       else:
              __a     				=  "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
       image.save(a ,  format=a							)
       return buffer.getvalue()



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

       __a     				=  array.dtype
       __a     				=  dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
       __a     				=  dtype.kind
       __a     				=  dtype.itemsize

       __a     				=  None

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




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

       if objs:
              __a				, __a     				=  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							):
                     __a     				=  no_op_if_value_is_null(a							)
                     return [obj_to_image_dict_func(a							) for obj in objs]
              elif isinstance(a ,  PIL.Image.Image							):
                     __a     				=  no_op_if_value_is_null(a							)
                     return [obj_to_image_dict_func(a							) for obj in objs]
              else:
                     return objs
       else:
              return objs

"""simple docstring"""



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     __a     				=  mf_knapsack(i - 1 ,  a ,  a ,  a							)
              else:
                     __a     				=  max(
                         mf_knapsack(i - 1 ,  a ,  a ,  a							) ,  mf_knapsack(i - 1 ,  a ,  a ,  j - wt[i - 1]							) + val[i - 1] ,  )
              __a     				=  val
       return f[i][j]



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  [[0] * (w + 1) for _ in range(n + 1							)]

       for i in range(1 ,  n + 1							):
              for w_ in range(1 ,  w + 1							):
                     if wt[i - 1] <= w_:
                            __a     				=  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,  dp[i - 1][w_]							)
                     else:
                            __a     				=  dp[i - 1][w_]

       return dp[n][w_], dp



def 					_lowerCamelCase(					a ,  a ,  a							):
       if not (isinstance(a ,  (list, tuple)							) and isinstance(a ,  (list, tuple)							)):
              raise ValueError(
                  "Both the weights and values vectors must be either lists or tuples"							)

       __a     				=  len(a							)
       if num_items != len(a							):
              __a     				=  (
                  "The number of weights must be the same as the number of values.\n"
                  F"But got {num_items} weights and {len(a							)} values"
              )
              raise ValueError(a							)
       for i in range(a							):
              if not isinstance(wt[i] ,  a							):
                     __a     				=  (
                         "All weights must be integers but got weight of "
                         F"type {type(wt[i]							)} at index {i}"
                     )
                     raise TypeError(a							)

       __a				, __a     				=  knapsack(a ,  a ,  a ,  a							)
       __a     				=  set()
       _construct_solution(a ,  a ,  a ,  a ,  a							)

       return optimal_val, example_optional_set



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a							):
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(a ,  a ,  i - 1 ,  a ,  a							)
              else:
                     optimal_set.add(a							)
                     _construct_solution(a ,  a ,  i - 1 ,  j - wt[i - 1] ,  a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Tuple			=	[3, 2, 4, 4]
   SCREAMING_SNAKE_CASE__:List[str]			=	[4, 3, 2, 3]
   SCREAMING_SNAKE_CASE__:List[str]			=	4
   SCREAMING_SNAKE_CASE__:List[str]			=	6
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[int]			=	knapsack(w, wt, val, n)
   print(optimal_solution)
   print(mf_knapsack(n, wt, val, w))  # switched the n and w

   # testing the dynamic programming problem with example
   # the optimal subset for the above example are items 3 and 4
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[Any]			=	knapsack_with_example_solution(w, wt, val)
   assert optimal_solution == 8
   assert optimal_subset == {3, 4}
   print("""optimal_value = """, optimal_solution)
   print("""An optimal subset corresponding to the optimal value""", optimal_subset)
