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  _lowerCamelCase					(   a_				: int):
			return EnvironmentCommand()





def  _lowerCamelCase					(   a_				: List[str]):
			return EnvironmentCommand(args.accelerate_config_file)


class   _lowerCAmelCase		(     __UpperCAmelCase				):







							@staticmethod
							def 				snake_case  (				__snake_case     :    ArgumentParser		):
										lowerCamelCase						:Union[str, Any]						  =     parser.add_parser('''env'''		)
										download_parser.set_defaults(func=lowerCAmelCase_		)
										download_parser.add_argument(
										    '''--accelerate-config_file'''	,					default=lowerCAmelCase_	,					help='''The accelerate config file to use for the default values in the launching script.'''	,					)
										download_parser.set_defaults(func=lowerCAmelCase_		)







							def __init__(				self     :    Any	,					__snake_case     :    Optional[Any]	,					*__snake_case     :    Optional[Any]		):
										lowerCamelCase						:List[Any]						  =     accelerate_config_file







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Tuple						  =     '''not installed'''
										if is_safetensors_available():
													import safetensors

													lowerCamelCase						:Optional[Any]						  =     safetensors.__version__
										elif importlib.util.find_spec('''safetensors'''		) is not None:
													import safetensors

													lowerCamelCase						:Any						  =     F"{safetensors.__version__} but is ignored because of PyTorch version too old."

										lowerCamelCase						:Tuple						  =     '''not installed'''
										lowerCamelCase						:int						  =     '''not found'''
										if is_accelerate_available():
													import accelerate
													from accelerate.commands.config import default_config_file, load_config_from_file

													lowerCamelCase						:str						  =     accelerate.__version__
													# Get the default from the config file.
													if self._accelerate_config_file is not None or os.path.isfile(lowerCAmelCase_		):
																lowerCamelCase						:int						  =     load_config_from_file(self._accelerate_config_file		).to_dict()

													lowerCamelCase						:Optional[int]						  =     (
													    '''\n'''.join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]		)
													    if isinstance(lowerCAmelCase_	,					lowerCAmelCase_		)
													    else F"\t{accelerate_config}"
													)

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

													lowerCamelCase						:int						  =     torch.__version__
													lowerCamelCase						:int						  =     torch.cuda.is_available()

										lowerCamelCase						:List[str]						  =     '''not installed'''
										lowerCamelCase						:Any						  =     '''NA'''
										if is_tf_available():
													import tensorflow as tf

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

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

													lowerCamelCase						:Dict						  =     flax.__version__
													lowerCamelCase						:Tuple						  =     jax.__version__
													lowerCamelCase						:Any						  =     jaxlib.__version__
													lowerCamelCase						:Dict						  =     jax.lib.xla_bridge.get_backend().platform

										lowerCamelCase						:Any						  =     {
										    '''`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(lowerCAmelCase_		)		)

										return info







							@staticmethod
							def 				snake_case  (				__snake_case     :    Union[str, Any]		):
										return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]		) + "\n"




import numpy
class   _lowerCAmelCase		:







							def __init__(				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:Dict						  =     input_array

										# Random initial weights are assigned where first argument is the
										# number of nodes in previous layer and second argument is the
										# number of nodes in the next layer.

										# Random initial weights are assigned.
										# self.input_array.shape[1] is used to represent number of nodes in input layer.
										# First hidden layer consists of 4 nodes.
										lowerCamelCase						:Dict						  =     numpy.random.rand(
										    self.input_array.shape[1]	,					4		)

										# Random initial values for the first hidden layer.
										# First hidden layer has 4 nodes.
										# Second hidden layer has 3 nodes.
										lowerCamelCase						:Dict						  =     numpy.random.rand(
										    4	,					3		)

										# Random initial values for the second hidden layer.
										# Second hidden layer has 3 nodes.
										# Output layer has 1 node.
										lowerCamelCase						:Any						  =     numpy.random.rand(3	,					1		)

										# Real output values provided.
										lowerCamelCase						:Union[str, Any]						  =     output_array

										# Predicted output values by the neural network.
										# Predicted_output array initially consists of zeroes.
										lowerCamelCase						:List[str]						  =     numpy.zeros(output_array.shape		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Any						  =     sigmoid(
										    numpy.dot(self.input_array	,					self.input_layer_and_first_hidden_layer_weights		)		)

										# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
										# connecting the first hidden set of nodes with the second hidden set of nodes.
										lowerCamelCase						:Any						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_input_and_first_hidden_layer	,					self.first_hidden_layer_and_second_hidden_layer_weights	,					)		)

										# layer_between_second_hidden_layer_and_output is the layer connecting
										# second hidden layer with the output node.
										lowerCamelCase						:Dict						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_first_hidden_layer_and_second_hidden_layer	,					self.second_hidden_layer_and_output_layer_weights	,					)		)

										return self.layer_between_second_hidden_layer_and_output







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     numpy.dot(
										    self.layer_between_first_hidden_layer_and_second_hidden_layer.T	,					2
										    * (self.output_array - self.predicted_output)
										    * sigmoid_derivative(self.predicted_output		)	,					)
										lowerCamelCase						:Dict						  =     numpy.dot(
										    self.layer_between_input_and_first_hidden_layer.T	,					numpy.dot(
										        2
										        * (self.output_array - self.predicted_output)
										        * sigmoid_derivative(self.predicted_output		)	,					self.second_hidden_layer_and_output_layer_weights.T	,					)
										    * sigmoid_derivative(
										        self.layer_between_first_hidden_layer_and_second_hidden_layer		)	,					)
										lowerCamelCase						:int						  =     numpy.dot(
										    self.input_array.T	,					numpy.dot(
										        numpy.dot(
										            2
										            * (self.output_array - self.predicted_output)
										            * sigmoid_derivative(self.predicted_output		)	,					self.second_hidden_layer_and_output_layer_weights.T	,					)
										        * sigmoid_derivative(
										            self.layer_between_first_hidden_layer_and_second_hidden_layer		)	,					self.first_hidden_layer_and_second_hidden_layer_weights.T	,					)
										    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer		)	,					)

										self.input_layer_and_first_hidden_layer_weights += (
										    updated_input_layer_and_first_hidden_layer_weights
										)
										self.first_hidden_layer_and_second_hidden_layer_weights += (
										    updated_first_hidden_layer_and_second_hidden_layer_weights
										)
										self.second_hidden_layer_and_output_layer_weights += (
										    updated_second_hidden_layer_and_output_layer_weights
										)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    int	,					__snake_case     :    bool		):
										for iteration in range(1	,					iterations + 1		):
													lowerCamelCase						:Union[str, Any]						  =     self.feedforward()
													self.back_propagation()
													if give_loss:
																lowerCamelCase						:Tuple						  =     numpy.mean(numpy.square(output - self.feedforward()		)		)
																print(F"Iteration {iteration} Loss: {loss}"		)







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:int						  =     input_arr

										lowerCamelCase						:Union[str, Any]						  =     sigmoid(
										    numpy.dot(self.array	,					self.input_layer_and_first_hidden_layer_weights		)		)

										lowerCamelCase						:Optional[Any]						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_input_and_first_hidden_layer	,					self.first_hidden_layer_and_second_hidden_layer_weights	,					)		)

										lowerCamelCase						:Optional[int]						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_first_hidden_layer_and_second_hidden_layer	,					self.second_hidden_layer_and_output_layer_weights	,					)		)

										return int(self.layer_between_second_hidden_layer_and_output > 0.6		)





def  _lowerCamelCase					(   a_				: numpy.ndarray):
			return 1 / (1 + numpy.exp(-value))





def  _lowerCamelCase					(   a_				: numpy.ndarray):
			return (value) * (1 - (value))





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     numpy.array(
			    (
			        [0, 0, 0],
			        [0, 0, 1],
			        [0, 1, 0],
			        [0, 1, 1],
			        [1, 0, 0],
			        [1, 0, 1],
			        [1, 1, 0],
			        [1, 1, 1],
			    )						,			dtype=numpy.floataa						,			)

			# True output values for the given input values.
			lowerCamelCase						:int						  =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,			dtype=numpy.floataa)

			# Calling neural network class.
			lowerCamelCase						:List[Any]						  =     TwoHiddenLayerNeuralNetwork(
			    input_array=a_						,			output_array=a_)

			# Calling training function.
			# Set give_loss to True if you want to see loss in every iteration.
			neural_network.train(output=a_						,			iterations=10						,			give_loss=a_)

			return neural_network.predict(numpy.array(([1, 1, 1])						,			dtype=numpy.floataa))


if __name__ == "__main__":
			example()



