'''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


a=  None
a=  '''<''' 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
a=  [
    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"""






     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						None
     # Automatically constructed
     SCREAMING_SNAKE_CASE__		   =						"PIL.Image.Image"
     SCREAMING_SNAKE_CASE__		   =						pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}						)
     SCREAMING_SNAKE_CASE__		   =						field(default='''Image'''			,							init=_lowerCamelCase			,							repr=_lowerCamelCase						)







     def __call__(		self    ):
         return self.pa_type







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

         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[Any]		    =  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 lowerCAmelCase     (		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:
             __UpperCamelCase	:	List[str]		    =  {}

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







     def lowerCAmelCase     (		self    ):
         from .features import Value

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         if pa.types.is_string(storage.type    ):
             __UpperCamelCase	:	Any		    =  pa.array([None] * len(_lowerCamelCase    )     ,			type=pa.binary()    )
             __UpperCamelCase	:	Union[str, Any]		    =  pa.StructArray.from_arrays([bytes_array, storage]     ,			['bytes', 'path']     ,			mask=storage.is_null()    )
         elif pa.types.is_binary(storage.type    ):
             __UpperCamelCase	:	List[str]		    =  pa.array([None] * len(_lowerCamelCase    )     ,			type=pa.string()    )
             __UpperCamelCase	:	List[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	:	Union[str, Any]		    =  storage.field('bytes'    )
             else:
                 __UpperCamelCase	:	Any		    =  pa.array([None] * len(_lowerCamelCase    )     ,			type=pa.binary()    )
             if storage.type.get_field_index('path'    ) >= 0:
                 __UpperCamelCase	:	Union[str, Any]		    =  storage.field('path'    )
             else:
                 __UpperCamelCase	:	Optional[int]		    =  pa.array([None] * len(_lowerCamelCase    )     ,			type=pa.string()    )
             __UpperCamelCase	:	List[str]		    =  pa.StructArray.from_arrays([bytes_array, path_array]     ,			['bytes', 'path']     ,			mask=storage.is_null()    )
         elif pa.types.is_list(storage.type    ):
             __UpperCamelCase	:	List[str]		    =  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()     ,			)
             __UpperCamelCase	:	Tuple		    =  pa.array([None] * len(_lowerCamelCase    )     ,			type=pa.string()    )
             __UpperCamelCase	:	str		    =  pa.StructArray.from_arrays(
                 [bytes_array, path_array]     ,			['bytes', 'path']     ,			mask=bytes_array.is_null()    )
         return array_cast(_lowerCamelCase     ,			self.pa_type    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):

         @no_op_if_value_is_null
         def path_to_bytes(_lowerCamelCase    ):
             with xopen(_lowerCamelCase     ,			'rb'    ) as f:
                 __UpperCamelCase	:	Tuple		    =  f.read()
             return bytes_

         __UpperCamelCase	:	Union[str, Any]		    =  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	:	Dict		    =  pa.array(
             [os.path.basename(_lowerCamelCase    ) if path is not None else None for path in storage.field('path'    ).to_pylist()]     ,			type=pa.string()     ,			)
         __UpperCamelCase	:	List[str]		    =  pa.StructArray.from_arrays([bytes_array, path_array]     ,			['bytes', 'path']     ,			mask=bytes_array.is_null()    )
         return array_cast(_lowerCamelCase     ,			self.pa_type    )







def  _UpperCamelCase (     ):




    """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	:	int		    =  list(set(PIL.Image.OPEN.keys()			) & set(PIL.Image.SAVE.keys()			)			)
    return _IMAGE_COMPRESSION_FORMATS







def  _UpperCamelCase (     _a	:	"PIL.Image.Image"			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  BytesIO()
    if image.format in list_image_compression_formats():
        __UpperCamelCase	:	Optional[Any]		    =  image.format
    else:
        __UpperCamelCase	:	List[str]		    =  'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
    image.save(_a				,     format=_a			)
    return buffer.getvalue()







def  _UpperCamelCase (     _a	:	"PIL.Image.Image"			):




    """simple docstring"""



    if hasattr(_a				,     'filename'			) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(_a			)}







def  _UpperCamelCase (     _a	:	np.ndarray			):




    """simple docstring"""



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

    __UpperCamelCase	:	List[str]		    =  array.dtype
    __UpperCamelCase	:	Union[str, Any]		    =  dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
    __UpperCamelCase	:	Any		    =  dtype.kind
    __UpperCamelCase	:	int		    =  dtype.itemsize

    __UpperCamelCase	:	Dict		    =  None

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







def  _UpperCamelCase (     _a	:	Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]			):




    """simple docstring"""



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

    if objs:
        __UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  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			):
            __UpperCamelCase	:	int		    =  no_op_if_value_is_null(_a			)
            return [obj_to_image_dict_func(_a			) for obj in objs]
        elif isinstance(_a				,     PIL.Image.Image			):
            __UpperCamelCase	:	int		    =  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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



