

'''simple docstring'''




def 		UpperCamelCase			(					a						,		a					) ->      int:






     '''simple docstring'''
     return int((input_a, input_a).count(1					) != 0					)




def 		UpperCamelCase			(					) ->      None:






     '''simple docstring'''
     assert or_gate(0						,		0					) == 0
     assert or_gate(0						,		1					) == 1
     assert or_gate(1						,		0					) == 1
     assert or_gate(1						,		1					) == 1


if __name__ == "__main__":
      print(or_gate(0, 1))
      print(or_gate(1, 0))
      print(or_gate(0, 0))
      print(or_gate(1, 1))












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


_lowerCamelCase      :						Optional[List[str]]							=  None
_lowerCamelCase      :						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
_lowerCamelCase      :						Union[str, Any]							=  [
    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 							UpperCamelCase_       :

    '''simple docstring'''






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


    def __call__(     self :      List[str])		->List[str]:





           '''simple docstring'''

           return self.pa_type


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"])		->dict:





           '''simple docstring'''

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

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  A__ 					=       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 :      Optional[int]   ,					UpperCAmelCase__ :      dict   ,					UpperCAmelCase__ :      str=None)		->"PIL.Image.Image":





           '''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__ 					=       {}

           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(UpperCAmelCase__):
                                A__ 					=       PIL.Image.open(UpperCAmelCase__)
                         else:
                                A__ 					=       path.split('''::''')[-1]
                                try:
                                       A__ 					=       string_to_dict(UpperCAmelCase__   ,					config.HUB_DATASETS_URL)['''repo_id''']
                                       A__ 					=       token_per_repo_id.get(UpperCAmelCase__)
                                except ValueError:
                                       A__ 					=       None
                                with xopen(UpperCAmelCase__   ,					'''rb'''   ,					use_auth_token=UpperCAmelCase__) 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 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Union["FeatureType", Dict[str, "FeatureType"]]:





           '''simple docstring'''

           from .features import Value

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Union[pa.StringArray, pa.StructArray, pa.ListArray])		->pa.StructArray:





           '''simple docstring'''

           if pa.types.is_string(storage.type):
                  A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					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(UpperCAmelCase__)   ,					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(UpperCAmelCase__)   ,					type=pa.binary())
                  if storage.type.get_field_index('''path''') >= 0:
                         A__ 					=       storage.field('''path''')
                  else:
                         A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					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(UpperCAmelCase__))['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]   ,					type=pa.binary()   ,					)
                  A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					type=pa.string())
                  A__ 					=       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 :      List[Any]   ,					UpperCAmelCase__ :      pa.StructArray)		->pa.StructArray:





           '''simple docstring'''

           @no_op_if_value_is_null
           def path_to_bytes(UpperCAmelCase__ :      Dict):
                  with xopen(UpperCAmelCase__   ,					'''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(UpperCAmelCase__) 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(UpperCAmelCase__   ,					self.pa_type)







def SCREAMING_SNAKE_CASE							(			)  ->			List[str]:



       """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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			bytes:



       """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(lowercase_					,							format=lowercase_		)
       return buffer.getvalue()







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			dict:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			dict:



       """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(lowercase_		)
                     A__ 					=       np.dtype(lowercase_		)
                     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(lowercase_		)		)
       return {"path": None, "bytes": image_to_bytes(lowercase_		)}







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[dict]:



       """simple docstring"""

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

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


