





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_  							=				None
UpperCAmelCase_  							=				'<' 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_  							=				[
    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 					lowercase__ :

   '''simple docstring'''


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




   def __call__(					self							)				->   Optional[Any]:






       """simple docstring"""
       return self.pa_type




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   dict:






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

       if isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       str      =   np.array(__magic_name__							)

       if isinstance(__magic_name__,	__magic_name__							):
           return {"path": value, "bytes": None}
       elif isinstance(__magic_name__,	__magic_name__							):
           return {"path": None, "bytes": value}
       elif isinstance(__magic_name__,	np.ndarray							):
           # convert the image array to PNG/TIFF bytes
           return encode_np_array(__magic_name__							)
       elif isinstance(__magic_name__,	PIL.Image.Image							):
           # convert the PIL image to bytes (default format is PNG/TIFF)
           return encode_pil_image(__magic_name__							)
       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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=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__							:       Union[str, Any]      =   {}

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




   def    UpperCamelCase__						(					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    UpperCamelCase__						(					self,	__magic_name__							)				->   pa.StructArray:






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




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   pa.StructArray:






       """simple docstring"""

       @no_op_if_value_is_null
       def path_to_bytes(__magic_name__							):
           with xopen(__magic_name__,	'''rb'''							) as f:
               UpperCamelCase__							:       Optional[Any]      =   f.read()
           return bytes_

       UpperCamelCase__							:       Tuple      =   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__							:       List[str]      =   pa.array(
           [os.path.basename(__magic_name__							) if path is not None else None for path in storage.field('''path'''							).to_pylist()],	type=pa.string(),	)
       UpperCamelCase__							:       Any      =   pa.StructArray.from_arrays([bytes_array, path_array],	['''bytes''', '''path'''],	mask=bytes_array.is_null()							)
       return array_cast(__magic_name__,	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__							:       Tuple      =   list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
    return _IMAGE_COMPRESSION_FORMATS




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




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




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

    UpperCamelCase__							:       Tuple      =   array.dtype
    UpperCamelCase__							:       List[Any]      =   dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
    UpperCamelCase__							:       Tuple      =   dtype.kind
    UpperCamelCase__							:       List[str]      =   dtype.itemsize

    UpperCamelCase__							:       int      =   None

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





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








def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       int      =   set()
    # edges = list of graph's edges
    UpperCamelCase__							:       str      =   get_edges(__UpperCAmelCase   )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   edges.pop()
        chosen_vertices.add(__UpperCAmelCase   )
        chosen_vertices.add(__UpperCAmelCase   )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(__UpperCAmelCase   )
    return chosen_vertices




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       Optional[int]      =   set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)   )
    return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")

