"""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


__SCREAMING_SNAKE_CASE    	=None
__SCREAMING_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
__SCREAMING_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        UpperCamelCase		:
   lowercase							=  True
   lowercase							=  None
   # Automatically constructed
   lowercase							=  "PIL.Image.Image"
   lowercase							=  pa.struct({'bytes': pa.binary(), 'path': pa.string()}  )
   lowercase							=  field(default='Image'						,      init=lowercase_						,      repr=lowercase_  )
   def __call__(			self			)	->      List[Any]:


        '''simple docstring'''


        return self.pa_type
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      dict:


        '''simple docstring'''


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

        if isinstance(__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				Optional[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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=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:
             lowercase_	:				List[str]													=	{}

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


        '''simple docstring'''


        from .features import Value

        return (
            self
            if self.decode
            else {
                "bytes": Value('binary'			),
                "path": Value('string'			),
            }
        )
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      pa.StructArray:


        '''simple docstring'''


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







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      pa.StructArray:


        '''simple docstring'''


        @no_op_if_value_is_null
        def path_to_bytes(__UpperCamelCase			):
             with xopen(__UpperCamelCase     ,'rb'			) as f:
                  lowercase_	:				str													=	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_	:				Any													=	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_	:				Dict													=	pa.StructArray.from_arrays([bytes_array, path_array]     ,['bytes', 'path']     ,mask=bytes_array.is_null()			)
        return array_cast(__UpperCamelCase     ,self.pa_type			)






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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     "PIL.Image.Image"      ):
     lowercase_	:				int													=	BytesIO()
     if image.format in list_image_compression_formats():
          lowercase_	:				Tuple													=	image.format
     else:
          lowercase_	:				List[Any]													=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
     image.save(__SCREAMING_SNAKE_CASE     ,			format=__SCREAMING_SNAKE_CASE      )
     return buffer.getvalue()



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     "PIL.Image.Image"      ):
     if hasattr(__SCREAMING_SNAKE_CASE     ,			'filename'      ) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE      )}



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

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

     lowercase_	:				int													=	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[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:
               lowercase_	:				Tuple													=	dtype_byteorder + dtype_kind + str(__SCREAMING_SNAKE_CASE      )
               lowercase_	:				List[Any]													=	np.dtype(__SCREAMING_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(__SCREAMING_SNAKE_CASE      )      )
     return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE      )}







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

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


"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int | float], int | float]     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int = 1_00     ,			):
     lowercase_	:				Dict													=	x_start
     lowercase_	:				Optional[Any]													=	fnc(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	0.0

     for _ in range(__SCREAMING_SNAKE_CASE      ):
          # Approximates curve as a sequence of linear lines and sums their length
          lowercase_	:				Union[str, Any]													=	(x_end - x_start) / steps + xa
          lowercase_	:				str													=	fnc(__SCREAMING_SNAKE_CASE      )
          length += math.hypot(xa - xa     ,			fxa - fxa      )

          # Increment step
          lowercase_	:				List[Any]													=	xa
          lowercase_	:				Optional[Any]													=	fxa

     return length


if __name__ == "__main__":



       def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
            return math.sin(10 * x      )

       print("f(x) = sin(10 * x)")
       print("The length of the curve from x = -10 to x = 10 is:")
       __SCREAMING_SNAKE_CASE    	=10
       while i <= 10_0000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10

