

'''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


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


   def __call__(			self  ):





      """simple docstring"""





      return self.pa_type


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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

      if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
         _lowercase      :				List[Any]      					=       np.array(_UpperCamelCase  )

      if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
         return {"path": value, "bytes": None}
      elif isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
         return {"path": None, "bytes": value}
      elif isinstance(_UpperCamelCase      ,    np.ndarray  ):
         # convert the image array to PNG/TIFF bytes
         return encode_np_array(_UpperCamelCase  )
      elif isinstance(_UpperCamelCase      ,    PIL.Image.Image  ):
         # convert the PIL image to bytes (default format is PNG/TIFF)
         return encode_pil_image(_UpperCamelCase  )
      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      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """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:
         _lowercase      :				List[Any]      					=       {}

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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      from .features import Value

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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

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







def  _A		(     )      ->     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()
      _lowercase      :				List[str]      					=       list(set(PIL.Image.OPEN.keys()					) & set(PIL.Image.SAVE.keys()					)					)
   return _IMAGE_COMPRESSION_FORMATS







def  _A		(     snake_case					)      ->     bytes:
   _lowercase      :				Any      					=       BytesIO()
   if image.format in list_image_compression_formats():
      _lowercase      :				Tuple      					=       image.format
   else:
      _lowercase      :				Union[str, Any]      					=       "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
   image.save(snake_case		,							format=snake_case					)
   return buffer.getvalue()







def  _A		(     snake_case					)      ->     dict:
   if hasattr(snake_case		,							"filename"					) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(snake_case					)}







def  _A		(     snake_case					)      ->     dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."					)

   _lowercase      :				Optional[int]      					=       array.dtype
   _lowercase      :				Tuple      					=       dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
   _lowercase      :				int      					=       dtype.kind
   _lowercase      :				Dict      					=       dtype.itemsize

   _lowercase      :				Union[str, Any]      					=       None

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







def  _A		(     snake_case					)      ->     List[dict]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."					)

   if objs:
      _lowercase      :				List[Any]      					=       first_non_null_value(snake_case					)
      if isinstance(snake_case		,							snake_case					):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(snake_case		,							np.ndarray					):
         _lowercase      :				Union[str, Any]      					=       no_op_if_value_is_null(snake_case					)
         return [obj_to_image_dict_func(snake_case					) for obj in objs]
      elif isinstance(snake_case		,							PIL.Image.Image					):
         _lowercase      :				Union[str, Any]      					=       no_op_if_value_is_null(snake_case					)
         return [obj_to_image_dict_func(snake_case					) for obj in objs]
      else:
         return objs
   else:
      return objs









'''simple docstring'''
import logging

from transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






