






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




 def __call__( self		:							Optional[Any] )		->  Union[str, Any]:
       return self.pa_type




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

       if isinstance(__lowercase   , __lowercase ):
             SCREAMING_SNAKE_CASE__       :			str													=np.array(__lowercase )

       if isinstance(__lowercase   , __lowercase ):
             return {"path": value, "bytes": None}
       elif isinstance(__lowercase   , __lowercase ):
             return {"path": None, "bytes": value}
       elif isinstance(__lowercase   , np.ndarray ):
             # convert the image array to PNG/TIFF bytes
             return encode_np_array(__lowercase )
       elif isinstance(__lowercase   , PIL.Image.Image ):
             # convert the PIL image to bytes (default format is PNG/TIFF)
             return encode_pil_image(__lowercase )
       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        __magic_name__		( self		:							int   , __lowercase		:							dict   , __lowercase		:							List[str]=None )		->  "PIL.Image.Image":
       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:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													={}

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




 def        __magic_name__		( self		:							List[Any] )		->  Union["FeatureType", Dict[str, "FeatureType"]]:
       from .features import Value

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




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




 def        __magic_name__		( self		:							int   , __lowercase		:							pa.StructArray )		->  pa.StructArray:

       @no_op_if_value_is_null
       def path_to_bytes(__lowercase		:							List[Any] ):
             with xopen(__lowercase   , '''rb''' ) as f:
                   SCREAMING_SNAKE_CASE__       :			Tuple													=f.read()
             return bytes_

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





def 	_a(					):




      '''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()
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=list(set(PIL.Image.OPEN.keys()							) & set(PIL.Image.SAVE.keys()							)							)
      return _IMAGE_COMPRESSION_FORMATS





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




      '''simple docstring'''






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





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




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  np.ndarray							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__       :			Any													=array.dtype
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
      SCREAMING_SNAKE_CASE__       :			Dict													=dtype.kind
      SCREAMING_SNAKE_CASE__       :			str													=dtype.itemsize

      SCREAMING_SNAKE_CASE__       :			Dict													=None

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







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







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

a_					=	{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""biogpt"""




 def __init__( self		:							str   , __lowercase		:							Union[str, Any]=4_23_84   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							Any=24   , __lowercase		:							Any=16   , __lowercase		:							Optional[Any]=40_96   , __lowercase		:							Any="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							List[Any]=0.1   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							List[Any]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Any=0.0   , __lowercase		:							int=0.0   , __lowercase		:							str=1   , __lowercase		:							int=0   , __lowercase		:							Optional[int]=2   , **__lowercase		:							Dict   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			int													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Any													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			int													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=scale_embedding
       SCREAMING_SNAKE_CASE__       :			str													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=layerdrop
       SCREAMING_SNAKE_CASE__       :			Dict													=activation_dropout
       super().__init__(pad_token_id=__lowercase   , bos_token_id=__lowercase   , eos_token_id=__lowercase   , **__lowercase )