



"""simple docstring"""
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					get_logger(__name__)

# Datasets
__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
__SCREAMING_SNAKE_CASE     :  Tuple										=					'''https://cdn-datasets.huggingface.co/datasets/datasets'''
__SCREAMING_SNAKE_CASE     :  str										=					'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''

# Metrics
__SCREAMING_SNAKE_CASE     :  Any										=					'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
__SCREAMING_SNAKE_CASE     :  Any										=					'''https://cdn-datasets.huggingface.co/datasets/metric'''
__SCREAMING_SNAKE_CASE     :  Any										=					'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''

# Hub
__SCREAMING_SNAKE_CASE     :  int										=					os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
__SCREAMING_SNAKE_CASE     :  Optional[int]										=					HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
__SCREAMING_SNAKE_CASE     :  List[str]										=					'''main'''

__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
			import importlib_metadata
else:
			import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
__SCREAMING_SNAKE_CASE     :  Optional[int]										=					{'''1''', '''ON''', '''YES''', '''TRUE'''}
__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
__SCREAMING_SNAKE_CASE     :  Tuple										=					version.parse(importlib_metadata.version('''dill'''))
__SCREAMING_SNAKE_CASE     :  List[Any]										=					version.parse(importlib_metadata.version('''pandas'''))
__SCREAMING_SNAKE_CASE     :  List[str]										=					version.parse(importlib_metadata.version('''pyarrow'''))

__SCREAMING_SNAKE_CASE     :  List[Any]										=					os.environ.get('''USE_TF''', '''AUTO''').upper()
__SCREAMING_SNAKE_CASE     :  Optional[int]										=					os.environ.get('''USE_TORCH''', '''AUTO''').upper()
__SCREAMING_SNAKE_CASE     :  Dict										=					os.environ.get('''USE_JAX''', '''AUTO''').upper()

__SCREAMING_SNAKE_CASE     :  Optional[int]										=					'''N/A'''
__SCREAMING_SNAKE_CASE     :  Optional[int]										=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
			__SCREAMING_SNAKE_CASE     :  Dict										=					importlib.util.find_spec('''torch''') is not None
			if TORCH_AVAILABLE:
						try:
									__SCREAMING_SNAKE_CASE     :  Optional[int]										=					version.parse(importlib_metadata.version('''torch'''))
									logger.info(F"""PyTorch version {TORCH_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
									pass
else:
			logger.info('''Disabling PyTorch because USE_TF is set''')

__SCREAMING_SNAKE_CASE     :  Tuple										=					'''N/A'''
__SCREAMING_SNAKE_CASE     :  Any										=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
			__SCREAMING_SNAKE_CASE     :  Tuple										=					importlib.util.find_spec('''tensorflow''') is not None
			if TF_AVAILABLE:
						# For the metadata, we have to look for both tensorflow and tensorflow-cpu
						for package in [
						    "tensorflow",
						    "tensorflow-cpu",
						    "tensorflow-gpu",
						    "tf-nightly",
						    "tf-nightly-cpu",
						    "tf-nightly-gpu",
						    "intel-tensorflow",
						    "tensorflow-rocm",
						    "tensorflow-macos",
						]:
									try:
												__SCREAMING_SNAKE_CASE     :  str										=					version.parse(importlib_metadata.version(package))
									except importlib_metadata.PackageNotFoundError:
												continue
									else:
												break
						else:
									__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					False
			if TF_AVAILABLE:
						if TF_VERSION.major < 2:
									logger.info(F"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
									__SCREAMING_SNAKE_CASE     :  str										=					False
						else:
									logger.info(F"""TensorFlow version {TF_VERSION} available.""")
else:
			logger.info('''Disabling Tensorflow because USE_TORCH is set''')


__SCREAMING_SNAKE_CASE     :  Tuple										=					'''N/A'''
__SCREAMING_SNAKE_CASE     :  List[Any]										=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
			if JAX_AVAILABLE:
						try:
									__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					version.parse(importlib_metadata.version('''jax'''))
									logger.info(F"""JAX version {JAX_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
									pass
else:
			logger.info('''Disabling JAX because USE_JAX is set to False''')


__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					os.environ.get('''USE_BEAM''', '''AUTO''').upper()
__SCREAMING_SNAKE_CASE     :  Any										=					'''N/A'''
__SCREAMING_SNAKE_CASE     :  List[Any]										=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
			try:
						__SCREAMING_SNAKE_CASE     :  Any										=					version.parse(importlib_metadata.version('''apache_beam'''))
						__SCREAMING_SNAKE_CASE     :  Dict										=					True
						logger.info(F"""Apache Beam version {BEAM_VERSION} available.""")
			except importlib_metadata.PackageNotFoundError:
						pass
else:
			logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					importlib.util.find_spec('''PIL''') is not None
__SCREAMING_SNAKE_CASE     :  List[str]										=					importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
__SCREAMING_SNAKE_CASE     :  List[Any]										=					importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
__SCREAMING_SNAKE_CASE     :  str										=					importlib.util.find_spec('''rarfile''') is not None
__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					importlib.util.find_spec('''zstandard''') is not None
__SCREAMING_SNAKE_CASE     :  Tuple										=					importlib.util.find_spec('''lz4''') is not None
__SCREAMING_SNAKE_CASE     :  Dict										=					importlib.util.find_spec('''py7zr''') is not None


# Cache location
__SCREAMING_SNAKE_CASE     :  List[Any]										=					'''~/.cache'''
__SCREAMING_SNAKE_CASE     :  Dict										=					os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
__SCREAMING_SNAKE_CASE     :  Optional[int]										=					os.path.join(XDG_CACHE_HOME, '''huggingface''')
__SCREAMING_SNAKE_CASE     :  Dict										=					os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

__SCREAMING_SNAKE_CASE     :  int										=					os.path.join(HF_CACHE_HOME, '''datasets''')
__SCREAMING_SNAKE_CASE     :  Dict										=					Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

__SCREAMING_SNAKE_CASE     :  Optional[int]										=					os.path.join(HF_CACHE_HOME, '''metrics''')
__SCREAMING_SNAKE_CASE     :  Tuple										=					Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					os.path.join(HF_CACHE_HOME, '''modules''')
__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

__SCREAMING_SNAKE_CASE     :  Dict										=					'''downloads'''
__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
__SCREAMING_SNAKE_CASE     :  Any										=					Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					'''extracted'''
__SCREAMING_SNAKE_CASE     :  Any										=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
__SCREAMING_SNAKE_CASE     :  List[str]										=					Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
__SCREAMING_SNAKE_CASE     :  Dict										=					(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
__SCREAMING_SNAKE_CASE     :  str										=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
__SCREAMING_SNAKE_CASE     :  int										=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
__SCREAMING_SNAKE_CASE     :  Tuple										=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
__SCREAMING_SNAKE_CASE     :  Tuple										=					'''500MB'''

# Parquet configuration
__SCREAMING_SNAKE_CASE     :  List[Any]										=					1_0_0
__SCREAMING_SNAKE_CASE     :  List[Any]										=					1_0_0
__SCREAMING_SNAKE_CASE     :  str										=					1_0_0

# Offline mode
__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
__SCREAMING_SNAKE_CASE     :  List[str]										=					0  # Disabled
__SCREAMING_SNAKE_CASE     :  List[str]										=					float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
__SCREAMING_SNAKE_CASE     :  int										=					'''dataset.arrow'''
__SCREAMING_SNAKE_CASE     :  str										=					'''indices.arrow'''
__SCREAMING_SNAKE_CASE     :  Tuple										=					'''state.json'''
__SCREAMING_SNAKE_CASE     :  Optional[int]										=					'''dataset_info.json'''
__SCREAMING_SNAKE_CASE     :  Dict										=					'''dataset_infos.json'''
__SCREAMING_SNAKE_CASE     :  List[str]										=					'''LICENSE'''
__SCREAMING_SNAKE_CASE     :  List[str]										=					'''metric_info.json'''
__SCREAMING_SNAKE_CASE     :  Tuple										=					'''dataset_dict.json'''

__SCREAMING_SNAKE_CASE     :  List[Any]										=					'''datasets_modules'''

__SCREAMING_SNAKE_CASE     :  Any										=					2_5_5

# Streaming
__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					2_0
__SCREAMING_SNAKE_CASE     :  Tuple										=					5

# Datasets without script
__SCREAMING_SNAKE_CASE     :  Tuple										=					2_0_0
__SCREAMING_SNAKE_CASE     :  List[Any]										=					1_0
__SCREAMING_SNAKE_CASE     :  List[Any]										=					2_0_0

# Progress bars
__SCREAMING_SNAKE_CASE     :  Tuple										=					0.05  # 20 progress updates per sec





"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				return 1 / (1 + np.exp(-1 * x							))


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = units
									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = activation
									if learning_rate is None:
													_lowerCamelCase					     = 0.3
									_lowerCamelCase					     = learning_rate
									_lowerCamelCase					     = is_input_layer

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				(self.units, back_units)      )      )
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				self.units      )      ).T
									if self.activation is None:
													_lowerCamelCase					     = sigmoid

					def 	snake_case__					(			self      ):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
													_lowerCamelCase					     = np.dot(self.output ,				(1 - self.output).T      )
													_lowerCamelCase					     = np.diag(np.diag(lowerCamelCase__      )      )
									else:
													_lowerCamelCase					     = 1
									return gradient_activation

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = xdata
									if self.is_input_layer:
													# input layer
													_lowerCamelCase					     = xdata
													_lowerCamelCase					     = xdata
													return xdata
									else:
													_lowerCamelCase					     = np.dot(self.weight ,				self.xdata      ) - self.bias
													_lowerCamelCase					     = self.activation(self.wx_plus_b      )
													return self.output





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.cal_gradient()  # i * i 维
									_lowerCamelCase					     = np.asmatrix(np.dot(gradient.T ,				lowerCamelCase__      )      )

									_lowerCamelCase					     = np.asmatrix(self.xdata      )
									_lowerCamelCase					     = -1
									_lowerCamelCase					     = self.weight

									_lowerCamelCase					     = np.dot(gradient.T ,				self._gradient_weight.T      )
									_lowerCamelCase					     = gradient * self._gradient_bias
									_lowerCamelCase					     = np.dot(lowerCamelCase__ ,				self._gradient_x      ).T
									# upgrade: the Negative gradient direction
									_lowerCamelCase					     = self.weight - self.learn_rate * self.gradient_weight
									_lowerCamelCase					     = self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self      ):
									_lowerCamelCase					     = []
									_lowerCamelCase					     = []
									_lowerCamelCase					     = plt.figure()
									_lowerCamelCase					     = self.fig_loss.add_subplot(1 ,				1 ,				1      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									self.layers.append(lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													if i < 1:
																	_lowerCamelCase					     = True
													else:
																	layer.initializer(self.layers[i - 1].units      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													print(F"""------- layer {i} -------"""      )
													print('''weight.shape ''' ,				np.shape(layer.weight      )      )
													print('''bias.shape ''' ,				np.shape(layer.bias      )      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = train_round
									_lowerCamelCase					     = accuracy

									self.ax_loss.hlines(self.accuracy ,				0 ,				self.train_round * 1.1      )

									_lowerCamelCase					     = np.shape(lowerCamelCase__      )
									for _ in range(lowerCamelCase__      ):
													_lowerCamelCase					     = 0
													for row in range(x_shape[0]      ):
																	_lowerCamelCase					     = np.asmatrix(xdata[row, :]      ).T
																	_lowerCamelCase					     = np.asmatrix(ydata[row, :]      ).T

																	# forward propagation
																	for layer in self.layers:
																					_lowerCamelCase					     = layer.forward_propagation(_xdata      )

																	_lowerCamelCase     ,   _lowerCamelCase					     = self.cal_loss(_ydata ,				_xdata      )
																	_lowerCamelCase					     = all_loss + loss

																	# back propagation: the input_layer does not upgrade
																	for layer in self.layers[:0:-1]:
																					_lowerCamelCase					     = layer.back_propagation(lowerCamelCase__      )

													_lowerCamelCase					     = all_loss / x_shape[0]
													self.train_mse.append(lowerCamelCase__      )

													self.plot_loss()

													if mse < self.accuracy:
																	print('''----达到精度----'''      )
																	return mse
									return None

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.sum(np.power((ydata - ydata_) ,				2      )      )
									_lowerCamelCase					     = 2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient





					def 	snake_case__					(			self      ):
									if self.ax_loss.lines:
													self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
									self.ax_loss.plot(self.train_mse ,				'''r-'''      )
									plt.ion()
									plt.xlabel('''step'''      )
									plt.ylabel('''loss'''      )
									plt.show()
									plt.pause(0.1      )



def 				lowerCAmelCase_(      )		->				Optional[Any]:
				_lowerCamelCase					     = np.random.randn(10  ,      10							)
				_lowerCamelCase					     = np.asarray(
				    [
				        [0.8, 0.4],
				        [0.4, 0.3],
				        [0.3_4, 0.4_5],
				        [0.6_7, 0.3_2],
				        [0.8_8, 0.6_7],
				        [0.7_8, 0.7_7],
				        [0.5_5, 0.6_6],
				        [0.5_5, 0.4_3],
				        [0.5_4, 0.1],
				        [0.1, 0.5],
				    ]							)
				_lowerCamelCase					     = BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(lowercase_							)							)
				model.build()
				model.summary()
				model.train(xdata=lowercase_  ,      ydata=lowercase_  ,      train_round=1_00  ,      accuracy=0.0_1							)


if __name__ == "__main__":
			example()
