


"""simple docstring"""






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 				A_       ( snake_case__		)					->  List[Any]:
			return EnvironmentCommand()





def 				A_       ( snake_case__		)					->  Any:
			return EnvironmentCommand(args.accelerate_config_file		)






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		@staticmethod
		def   _UpperCamelCase( SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	parser.add_parser('''env'''   )
					download_parser.set_defaults(func=SCREAMING_SNAKE_CASE__   )
					download_parser.add_argument(
					    '''--accelerate-config_file'''					,				default=SCREAMING_SNAKE_CASE__					,				help='''The accelerate config file to use for the default values in the launching script.'''					,				)
					download_parser.set_defaults(func=SCREAMING_SNAKE_CASE__   )
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				*SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	accelerate_config_file
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	'''not installed'''
					if is_safetensors_available():
								import safetensors

								_UpperCamelCase					:Dict					     =	safetensors.__version__
					elif importlib.util.find_spec('''safetensors'''   ) is not None:
								import safetensors

								_UpperCamelCase					:Dict					     =	f"{safetensors.__version__} but is ignored because of PyTorch version too old."

					_UpperCamelCase					:List[Any]					     =	'''not installed'''
					_UpperCamelCase					:str					     =	'''not found'''
					if is_accelerate_available():
								import accelerate
								from accelerate.commands.config import default_config_file, load_config_from_file

								_UpperCamelCase					:Union[str, Any]					     =	accelerate.__version__
								# Get the default from the config file.
								if self._accelerate_config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:List[str]					     =	load_config_from_file(self._accelerate_config_file   ).to_dict()

								_UpperCamelCase					:Optional[Any]					     =	(
								    '''\n'''.join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()]   )
								    if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								    else f"\t{accelerate_config}"
								)

					_UpperCamelCase					:Optional[int]					     =	'''not installed'''
					_UpperCamelCase					:List[str]					     =	'''NA'''
					if is_torch_available():
								import torch

								_UpperCamelCase					:Optional[Any]					     =	torch.__version__
								_UpperCamelCase					:List[str]					     =	torch.cuda.is_available()

					_UpperCamelCase					:str					     =	'''not installed'''
					_UpperCamelCase					:int					     =	'''NA'''
					if is_tf_available():
								import tensorflow as tf

								_UpperCamelCase					:int					     =	tf.__version__
								try:
											# deprecated in v2.1
											_UpperCamelCase					:Tuple					     =	tf.test.is_gpu_available()
								except AttributeError:
											# returns list of devices, convert to bool
											_UpperCamelCase					:List[Any]					     =	bool(tf.config.list_physical_devices('''GPU'''   )   )

					_UpperCamelCase					:List[Any]					     =	'''not installed'''
					_UpperCamelCase					:List[Any]					     =	'''not installed'''
					_UpperCamelCase					:List[str]					     =	'''not installed'''
					_UpperCamelCase					:Optional[int]					     =	'''NA'''
					if is_flax_available():
								import flax
								import jax
								import jaxlib

								_UpperCamelCase					:Tuple					     =	flax.__version__
								_UpperCamelCase					:str					     =	jax.__version__
								_UpperCamelCase					:List[Any]					     =	jaxlib.__version__
								_UpperCamelCase					:Any					     =	jax.lib.xla_bridge.get_backend().platform

					_UpperCamelCase					:List[str]					     =	{
					    '''`transformers` version''': version,
					    '''Platform''': platform.platform(),
					    '''Python version''': platform.python_version(),
					    '''Huggingface_hub version''': huggingface_hub.__version__,
					    '''Safetensors version''': f"{safetensors_version}",
					    '''Accelerate version''': f"{accelerate_version}",
					    '''Accelerate config''': f"{accelerate_config_str}",
					    '''PyTorch version (GPU?)''': f"{pt_version} ({pt_cuda_available})",
					    '''Tensorflow version (GPU?)''': f"{tf_version} ({tf_cuda_available})",
					    '''Flax version (CPU?/GPU?/TPU?)''': f"{flax_version} ({jax_backend})",
					    '''Jax version''': f"{jax_version}",
					    '''JaxLib version''': f"{jaxlib_version}",
					    '''Using GPU in script?''': '''<fill in>''',
					    '''Using distributed or parallel set-up in script?''': '''<fill in>''',
					}

					print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''   )
					print(self.format_dict(SCREAMING_SNAKE_CASE__   )   )

					return info





		@staticmethod
		def   _UpperCamelCase( SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"





"""simple docstring"""






# 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 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	cost_function(snake_case__       ,					snake_case__		)
						if iterations % 1_00 == 0:
									print(f"loss: {j} \t"		)  # printing the loss after every 100 iterations
			return theta


# In[68]:

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

					UpperCamelCase__				:Tuple			   =	0.1
					UpperCamelCase__				:List[str]			   =	logistic_reg(alpha, x, y, max_iterations=70_000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def 				A_       ( snake_case__		)					->  Optional[Any]:
								return sigmoid_function(
								    np.dot(snake_case__       ,					snake_case__		)		)  # 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""")
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Union[str, Any]			   =	(x[:, 0].min(), x[:, 0].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	(x[:, 1].min(), x[:, 1].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					UpperCamelCase__				:List[Any]			   =	np.c_[xxa.ravel(), xxa.ravel()]
					UpperCamelCase__				:List[Any]			   =	predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()




					plt.show()

