





'''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


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


     def __call__(						self):

          '''simple docstring'''






          return self.pa_type


     def 							SCREAMING_SNAKE_CASE  (						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):
               __A						: Dict      =       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 							SCREAMING_SNAKE_CASE  (						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:
               __A						: Any      =       {}

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          from .features import Value

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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






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

          __A						: Optional[int]      =       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						: int      =       pa.array(
              [os.path.basename(_UpperCAmelCase) if path is not None else None for path in storage.field('path').to_pylist()]						,	type=pa.string()						,	)
          __A						: List[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			(  )     ->     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()
          __A						: str      =       list(set(PIL.Image.OPEN.keys()			) & set(PIL.Image.SAVE.keys()			)			)
     return _IMAGE_COMPRESSION_FORMATS






def        _lowerCAmelCase			(  __snake_case  : "PIL.Image.Image"			)     ->     bytes:
     __A						: Any      =       BytesIO()
     if image.format in list_image_compression_formats():
          __A						: Any      =       image.format
     else:
          __A						: Dict      =       'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
     image.save(__snake_case ,							format=__snake_case			)
     return buffer.getvalue()






def        _lowerCAmelCase			(  __snake_case  : "PIL.Image.Image"			)     ->     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        _lowerCAmelCase			(  __snake_case  : np.ndarray			)     ->     dict:
     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'			)

     __A						: int      =       array.dtype
     __A						: Tuple      =       dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
     __A						: Optional[int]      =       dtype.kind
     __A						: List[str]      =       dtype.itemsize

     __A						: Dict      =       None

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







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

     if objs:
          __A ,__A						: Optional[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			):
               __A						: Optional[int]      =       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			):
               __A						: Optional[int]      =       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'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])