import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class __snake_case       (		_snake_case	):




			def __init__(					self    :       List[Any] ,			_lowercase    :       NestedDataStructureLike[PathLike] ,			_lowercase    :       Optional[NamedSplit] = None ,			_lowercase    :       Optional[Features] = None ,			_lowercase    :       str = None ,			_lowercase    :       bool = False ,			_lowercase    :       bool = False ,			_lowercase    :       Optional[str] = None ,			_lowercase    :       Optional[int] = None ,			**_lowercase    :       Optional[int] ,			):



					"""simple docstring"""






					super().__init__(
					    _lowercase ,			split=_lowercase ,			features=_lowercase ,			cache_dir=_lowercase ,			keep_in_memory=_lowercase ,			streaming=_lowercase ,			num_proc=_lowercase ,			**_lowercase ,			)
					SCREAMING_SNAKE_CASE__    							=   field
					SCREAMING_SNAKE_CASE__    							=   path_or_paths if isinstance(_lowercase ,			_lowercase   ) else {self.split: path_or_paths}
					SCREAMING_SNAKE_CASE__    							=   Json(
					    cache_dir=_lowercase ,			data_files=_lowercase ,			features=_lowercase ,			field=_lowercase ,			**_lowercase ,			)




			def 			__a		(					self    :       Union[str, Any]   ):



					"""simple docstring"""






					if self.streaming:
							SCREAMING_SNAKE_CASE__    							=   self.builder.as_streaming_dataset(split=self.split   )
					# Build regular (map-style) dataset
					else:
							SCREAMING_SNAKE_CASE__    							=   None
							SCREAMING_SNAKE_CASE__    							=   None
							SCREAMING_SNAKE_CASE__    							=   None
							SCREAMING_SNAKE_CASE__    							=   None

							self.builder.download_and_prepare(
							    download_config=_lowercase ,			download_mode=_lowercase ,			verification_mode=_lowercase ,			base_path=_lowercase ,			num_proc=self.num_proc ,			)
							SCREAMING_SNAKE_CASE__    							=   self.builder.as_dataset(
							    split=self.split ,			verification_mode=_lowercase ,			in_memory=self.keep_in_memory   )
					return dataset







class __snake_case       :




			def __init__(					self    :       Tuple ,			_lowercase    :       Dataset ,			_lowercase    :       Union[PathLike, BinaryIO] ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Optional[int] = None ,			**_lowercase    :       str ,			):



					"""simple docstring"""






					if num_proc is not None and num_proc <= 0:
							raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""   )

					SCREAMING_SNAKE_CASE__    							=   dataset
					SCREAMING_SNAKE_CASE__    							=   path_or_buf
					SCREAMING_SNAKE_CASE__    							=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					SCREAMING_SNAKE_CASE__    							=   num_proc
					SCREAMING_SNAKE_CASE__    							=   """utf-8"""
					SCREAMING_SNAKE_CASE__    							=   to_json_kwargs




			def 			__a		(					self    :       str   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""path_or_buf""" ,			_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""orient""" ,			"""records"""   )
					SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""lines""" ,			True if orient == """records""" else False   )
					SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""index""" ,			False if orient in ["""split""", """table"""] else True   )
					SCREAMING_SNAKE_CASE__    							=   self.to_json_kwargs.pop("""compression""" ,			_lowercase   )

					if compression not in [None, "infer", "gzip", "bz2", "xz"]:
							raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""   )

					if isinstance(self.path_or_buf ,			(str, bytes, os.PathLike)   ):
							with fsspec.open(self.path_or_buf ,			"""wb""" ,			compression=_lowercase   ) as buffer:
									SCREAMING_SNAKE_CASE__    							=   self._write(file_obj=_lowercase ,			orient=_lowercase ,			lines=_lowercase ,			index=_lowercase ,			**self.to_json_kwargs   )
					else:
							if compression:
									raise NotImplementedError(
									    f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
									    """ was passed. Please provide a local path instead."""   )
							SCREAMING_SNAKE_CASE__    							=   self._write(
							    file_obj=self.path_or_buf ,			orient=_lowercase ,			lines=_lowercase ,			index=_lowercase ,			**self.to_json_kwargs   )
					return written




			def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   args

					SCREAMING_SNAKE_CASE__    							=   query_table(
					    table=self.dataset.data ,			key=slice(_lowercase ,			offset + self.batch_size   ) ,			indices=self.dataset._indices ,			)
					SCREAMING_SNAKE_CASE__    							=   batch.to_pandas().to_json(
					    path_or_buf=_lowercase ,			orient=_lowercase ,			lines=_lowercase ,			index=_lowercase ,			**_lowercase   )
					if not json_str.endswith("""\n"""   ):
							json_str += "\n"
					return json_str.encode(self.encoding   )




			def 			__a		(					self    :       Dict ,			_lowercase    :       BinaryIO ,			_lowercase    :       Optional[int] ,			_lowercase    :       Any ,			_lowercase    :       Union[str, Any] ,			**_lowercase    :       Any ,			):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   0

					if self.num_proc is None or self.num_proc == 1:
							for offset in logging.tqdm(
							    range(0 ,			len(self.dataset   ) ,			self.batch_size   ) ,			unit="""ba""" ,			disable=not logging.is_progress_bar_enabled() ,			desc="""Creating json from Arrow format""" ,			):
									SCREAMING_SNAKE_CASE__    							=   self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
									written += file_obj.write(_lowercase   )
					else:
							SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   len(self.dataset   ), self.batch_size
							with multiprocessing.Pool(self.num_proc   ) as pool:
									for json_str in logging.tqdm(
									    pool.imap(
									        self._batch_json ,			[(offset, orient, lines, index, to_json_kwargs) for offset in range(0 ,			_lowercase ,			_lowercase   )] ,			) ,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,			unit="""ba""" ,			disable=not logging.is_progress_bar_enabled() ,			desc="""Creating json from Arrow format""" ,			):
											written += file_obj.write(_lowercase   )

					return written

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
   __lowerCamelCase    :				List[Any]							     =	datasets.load_iris()
   __lowerCamelCase    :				List[Any]							     =	iris.data[:, :2]
   __lowerCamelCase    :				Dict							     =	(iris.target != 0) * 1

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(10, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
