'''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


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      lowerCAmelCase						:
     _SCREAMING_SNAKE_CASE     :     Dict		=True
     _SCREAMING_SNAKE_CASE     :     int		=None
     # Automatically constructed
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""PIL.Image.Image"""
     _SCREAMING_SNAKE_CASE     :     Any		=pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}			)
     _SCREAMING_SNAKE_CASE     :     List[str]		=field(default="""Image"""			,		init=lowercase_			,		repr=lowercase_			)



     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=	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 							UpperCamelCase				(    )		->						Optional[Any]:




       '''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=	list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
       return _IMAGE_COMPRESSION_FORMATS

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       _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(lowerCamelCase_       ,					format=lowerCamelCase_   )
       return buffer.getvalue()

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''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 							UpperCamelCase				(    lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       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(lowerCamelCase_   )
                     _A=	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=	PIL.Image.fromarray(array.astype(lowerCamelCase_   )   )
       return {"path": None, "bytes": image_to_bytes(lowerCamelCase_   )}





def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






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

       if objs:
              _A=	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=	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=	no_op_if_value_is_null(lowerCamelCase_   )
                     return [obj_to_image_dict_func(lowerCamelCase_   ) for obj in objs]
              else:
                     return objs
       else:
              return objs
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )