


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml

class     a		(yaml.SafeLoader				):




							"""simple docstring"""


							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  Any    )     ->   Dict:
														__snake_case		:      Optional[int]										=       [self.constructed_objects[key_node] for key_node, _ in node.value]
														__snake_case		:      List[Any]										=       [tuple(lowerCamelCase    ) if isinstance(lowerCamelCase   ,     lowerCamelCase    ) else key for key in keys]
														__snake_case		:      int										=       Counter(lowerCamelCase    )
														__snake_case		:      List[str]										=       [key for key in counter if counter[key] > 1]
														if duplicate_keys:
																					raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'    )






							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Optional[int]=False    )     ->   int:
														__snake_case		:      str										=       super().construct_mapping(lowerCamelCase   ,     deep=lowerCamelCase    )
														self._check_no_duplicates_on_constructed_node(lowerCamelCase    )
														return mapping







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[str]										=       list(readme_content.splitlines()     )
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														__snake_case		:      Dict										=       full_content[1:].index("---"     ) + 1
														__snake_case		:      Union[str, Any]										=       "\n".join(full_content[1:sep_idx]     )
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

							return None, "\n".join(__lowerCamelCase     )

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  {"train_eval_index"}  # train-eval-index in the YAML metadata

							@classmethod
							def    __snake_case (							cls	:  Optional[Any]   ,     lowerCamelCase	:  Path    )     ->   "DatasetMetadata":
														with open(lowerCamelCase   ,     encoding="utf-8"    ) as readme_file:
																					__snake_case			,      __snake_case		:      Optional[int]										=       _split_yaml_from_readme(readme_file.read()    )
														if yaml_string is not None:
																					return cls.from_yaml_string(lowerCamelCase    )
														else:
																					return cls()

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Path    )     ->   int:
														if path.exists():
																					with open(lowerCamelCase   ,     encoding="utf-8"    ) as readme_file:
																												__snake_case		:      Any										=       readme_file.read()
														else:
																					__snake_case		:      Tuple										=       None
														__snake_case		:      Optional[Any]										=       self._to_readme(lowerCamelCase    )
														with open(lowerCamelCase   ,     "w"   ,     encoding="utf-8"    ) as readme_file:
																					readme_file.write(lowerCamelCase    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Optional[str] = None    )     ->   str:
														if readme_content is not None:
																					__snake_case			,      __snake_case		:      List[str]										=       _split_yaml_from_readme(lowerCamelCase    )
																					__snake_case		:      Optional[int]										=       "---\n" + self.to_yaml_string() + "---\n" + content
														else:
																					__snake_case		:      Optional[int]										=       "---\n" + self.to_yaml_string() + "---\n"
														return full_content

							@classmethod
							def    __snake_case (							cls	:  Optional[int]   ,     lowerCamelCase	:  str    )     ->   "DatasetMetadata":
														__snake_case		:      int										=       yaml.load(lowerCamelCase   ,     Loader=_NoDuplicateSafeLoader    ) or {}

														# Convert the YAML keys to DatasetMetadata fields
														__snake_case		:      Optional[Any]										=       {
														    (key.replace("-"   ,     "_"    ) if key.replace("-"   ,     "_"    ) in cls._FIELDS_WITH_DASHES else key): value
														    for key, value in metadata_dict.items()
														}
														return cls(**lowerCamelCase    )






							def    __snake_case (							self	:  Dict    )     ->   str:
														return yaml.safe_dump(
														    {
														        (key.replace("_"   ,     "-"    ) if key in self._FIELDS_WITH_DASHES else key): value
														        for key, value in self.items()
														    }   ,     sort_keys=lowerCamelCase   ,     allow_unicode=lowerCamelCase   ,     encoding="utf-8"   ,     ).decode("utf-8"    )
_snake_case					:		List[Any]      =    {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
			from argparse import ArgumentParser

			_snake_case					:		Dict      =    ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
			ap.add_argument("readme_filepath")
			_snake_case					:		int      =    ap.parse_args()

			_snake_case					:		Union[str, Any]      =    Path(args.readme_filepath)
			_snake_case					:		Optional[int]      =    DatasetMetadata.from_readme(readme_filepath)
			print(dataset_metadata)
			dataset_metadata.to_readme(readme_filepath)





import numpy

class     a		:




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  numpy.ndarray    )     ->   None:
														__snake_case		:      Any										=       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.
														__snake_case		:      int										=       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.
														__snake_case		:      Optional[int]										=       numpy.random.rand(
														    4   ,     3    )

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

														# Real output values provided.
														__snake_case		:      Optional[Any]										=       output_array

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

							def    __snake_case (							self	:  List[Any]    )     ->   numpy.ndarray:
														__snake_case		:      List[str]										=       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.
														__snake_case		:      str										=       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.
														__snake_case		:      str										=       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	:  Union[str, Any]    )     ->   None:
														__snake_case		:      Optional[int]										=       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    )   ,     )
														__snake_case		:      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    )   ,     )
														__snake_case		:      Optional[Any]										=       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	:  List[str]   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  bool    )     ->   None:
														for iteration in range(1   ,     iterations + 1    ):
																					__snake_case		:      Any										=       self.feedforward()
																					self.back_propagation()
																					if give_loss:
																												__snake_case		:      str										=       numpy.mean(numpy.square(output - self.feedforward()    )    )
																												print(F'Iteration {iteration} Loss: {loss}'    )






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

														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														__snake_case		:      List[Any]										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														__snake_case		:      Any										=       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_					( __lowerCamelCase     ):
							return 1 / (1 + numpy.exp(-value     ))







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return (value) * (1 - (value))







def     lowerCAmelCase_					( ):
							__snake_case		:      Dict										=       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.
							__snake_case		:      Union[str, Any]										=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,     dtype=numpy.floataa     )

							# Calling neural network class.
							__snake_case		:      int										=       TwoHiddenLayerNeuralNetwork(
							    input_array=__lowerCamelCase							,     output_array=__lowerCamelCase     )

							# Calling training function.
							# Set give_loss to True if you want to see loss in every iteration.
							neural_network.train(output=__lowerCamelCase							,     iterations=1_0							,     give_loss=__lowerCamelCase     )

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


if __name__ == "__main__":
			example()

