
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


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

						def __call__(			self				)					->      Any:


								'''simple docstring'''

								return self.pa_type

						def  A_				(			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						    =	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  A_				(			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						    =	{}

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

						def  A_				(			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  A_				(			self       ,							a				)					->      pa.StructArray:


								'''simple docstring'''

								if pa.types.is_string(storage.type				):
										_UpperCamelCase						    =	pa.array([None] * len(a				)       ,							type=pa.binary()				)
										_UpperCamelCase						    =	pa.StructArray.from_arrays([bytes_array, storage]       ,							["""bytes""", """path"""]       ,							mask=storage.is_null()				)
								elif pa.types.is_binary(storage.type				):
										_UpperCamelCase						    =	pa.array([None] * len(a				)       ,							type=pa.string()				)
										_UpperCamelCase						    =	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						    =	storage.field("""bytes"""				)
										else:
												_UpperCamelCase						    =	pa.array([None] * len(a				)       ,							type=pa.binary()				)
										if storage.type.get_field_index("""path"""				) >= 0:
												_UpperCamelCase						    =	storage.field("""path"""				)
										else:
												_UpperCamelCase						    =	pa.array([None] * len(a				)       ,							type=pa.string()				)
										_UpperCamelCase						    =	pa.StructArray.from_arrays([bytes_array, path_array]       ,							["""bytes""", """path"""]       ,							mask=storage.is_null()				)
								elif pa.types.is_list(storage.type				):
										_UpperCamelCase						    =	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						    =	pa.array([None] * len(a				)       ,							type=pa.string()				)
										_UpperCamelCase						    =	pa.StructArray.from_arrays(
										    [bytes_array, path_array]       ,							["""bytes""", """path"""]       ,							mask=bytes_array.is_null()				)
								return array_cast(a       ,							self.pa_type				)







						def  A_				(			self       ,							a				)					->      pa.StructArray:


								'''simple docstring'''

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

								_UpperCamelCase						    =	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						    =	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						    =	pa.StructArray.from_arrays([bytes_array, path_array]       ,							["""bytes""", """path"""]       ,							mask=bytes_array.is_null()				)
								return array_cast(a       ,							self.pa_type				)




def 							__A()		->				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()
				_UpperCamelCase						    =	list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
		return _IMAGE_COMPRESSION_FORMATS


def 							__A(lowerCAmelCase )		->				bytes:

		"""simple docstring"""






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


def 							__A(lowerCAmelCase )		->				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 							__A(lowerCAmelCase )		->				dict:

		"""simple docstring"""






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

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

		_UpperCamelCase						    =	None

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







def 							__A(lowerCAmelCase )		->				List[dict]:

		"""simple docstring"""






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

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





def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float]:

		"""simple docstring"""






		if not len(lowerCAmelCase ) == len(lowerCAmelCase ) == 3:
				raise ValueError("""Please enter a valid equation.""" )
		if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
				raise ValueError("""Both a & b of two equations can't be zero.""" )

		# Extract the coefficients
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa

		# Calculate the determinants of the matrices
		_UpperCamelCase						    =	aa * ba - aa * ba
		_UpperCamelCase						    =	ca * ba - ca * ba
		_UpperCamelCase						    =	aa * ca - aa * ca

		# Check if the system of linear equations has a solution (using Cramer's rule)
		if determinant == 0:
				if determinant_x == determinant_y == 0:
						raise ValueError("""Infinite solutions. (Consistent system)""" )
				else:
						raise ValueError("""No solution. (Inconsistent system)""" )
		else:
				if determinant_x == determinant_y == 0:
						# Trivial solution (Inconsistent system)
						return (0.0, 0.0)
				else:
						_UpperCamelCase						    =	determinant_x / determinant
						_UpperCamelCase						    =	determinant_y / determinant
						# Non-Trivial Solution (Consistent system)
						return (x, y)



