
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   UpperCAmelCase		:
   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=A_       ,repr=A_ )



   def __call__(self		:					Any )		->	List[str]:


          '''simple docstring'''


          return self.pa_type



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] )		->	dict:


          '''simple docstring'''


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

          if isinstance(snake_case__    ,						snake_case__ ):
                 snake_case       :   Optional[Any]							=      np.array(snake_case__ )

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

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



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


          '''simple docstring'''


          from .features import Value

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					Union[pa.StringArray, pa.StructArray, pa.ListArray] )		->	pa.StructArray:


          '''simple docstring'''


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



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


          '''simple docstring'''


          @no_op_if_value_is_null
          def path_to_bytes(snake_case__		:					List[str] ):
                 with xopen(snake_case__    ,						"rb" ) as f:
                        snake_case       :   int							=      f.read()
                 return bytes_

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



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


def 			UpperCamelCase					(    __lowerCamelCase     : "PIL.Image.Image"    ):
       snake_case       :   Dict							=      BytesIO()
       if image.format in list_image_compression_formats():
              snake_case       :   Optional[Any]							=      image.format
       else:
              snake_case       :   List[Any]							=      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
       image.save(__lowerCamelCase				,					format=__lowerCamelCase    )
       return buffer.getvalue()


def 			UpperCamelCase					(    __lowerCamelCase     : "PIL.Image.Image"    ):
       if hasattr(__lowerCamelCase				,					"filename"    ) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}


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

       snake_case       :   List[str]							=      array.dtype
       snake_case       :   Dict							=      dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
       snake_case       :   Dict							=      dtype.kind
       snake_case       :   int							=      dtype.itemsize

       snake_case       :   Tuple							=      None

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




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

       if objs:
              snake_case				,					snake_case       :   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    ):
                     snake_case       :   Any							=      no_op_if_value_is_null(__lowerCamelCase    )
                     return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
              elif isinstance(__lowerCamelCase				,					PIL.Image.Image    ):
                     snake_case       :   Optional[int]							=      no_op_if_value_is_null(__lowerCamelCase    )
                     return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
              else:
                     return objs
       else:
              return objs







__lowerCamelCase							=       {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_5818,
}


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : float    ):
       if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
              snake_case       :   List[Any]							=      (
                  f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                  f"""Valid values are: {', '.join(__lowerCamelCase    )}"""
              )
              raise ValueError(__lowerCamelCase    )
       return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





