



import numpy
class 				_snake_case			:




    def __init__(						self					:						Union[str, Any],					__lowercase					:						Dict,					__lowercase					:						int						):
       lowercase__					  =				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.
       lowercase__					  =				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.
       lowercase__					  =				numpy.random.rand(
           4,					3						)

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

       # Real output values provided.
       lowercase__					  =				output_array

       # Predicted output values by the neural network.
       # Predicted_output array initially consists of zeroes.
       lowercase__					  =				numpy.zeros(output_array.shape						)




    def 							A__      (						self					:						List[Any]						):
       lowercase__					  =				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.
       lowercase__					  =				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.
       lowercase__					  =				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 							A__      (						self					:						Union[str, Any]						):
       lowercase__					  =				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						),					)
       lowercase__					  =				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						),					)
       lowercase__					  =				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 							A__      (						self					:						Any,					__lowercase					:						List[Any],					__lowercase					:						Optional[int],					__lowercase					:						Optional[Any]						):
       for iteration in range(1,					iterations + 1						):
          lowercase__					  =				self.feedforward()
          self.back_propagation()
          if give_loss:
             lowercase__					  =				numpy.mean(numpy.square(output - self.feedforward()						)						)
             print(F'''Iteration {iteration} Loss: {loss}'''						)




    def 							A__      (						self					:						Optional[int],					__lowercase					:						List[Any]						):
       lowercase__					  =				input_arr

       lowercase__					  =				sigmoid(
           numpy.dot(self.array,					self.input_layer_and_first_hidden_layer_weights						)						)

       lowercase__					  =				sigmoid(
           numpy.dot(
               self.layer_between_input_and_first_hidden_layer,					self.first_hidden_layer_and_second_hidden_layer_weights,					)						)

       lowercase__					  =				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 (     SCREAMING_SNAKE_CASE_  ):
   return 1 / (1 + numpy.exp(-value  ))



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   return (value) * (1 - (value))



def __lowerCAmelCase (     ):
   lowercase__					  =				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.
   lowercase__					  =				numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,  dtype=numpy.floataa  )

   # Calling neural network class.
   lowercase__					  =				TwoHiddenLayerNeuralNetwork(
       input_array=UpperCAmelCase__						,  output_array=UpperCAmelCase__  )

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

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


if __name__ == "__main__":
       example()









import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowercase_								=     """<<<<<<< This should probably be modified because it mentions: """

lowercase_								=     """=======
>>>>>>>
"""

lowercase_								=     [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase_								=     [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r"""tfds\.core""", r"""datasets"""),
    (r"""tf\.io\.gfile\.GFile""", r"""open"""),
    (r"""tf\.([\w\d]+)""", r"""datasets.Value('\1')"""),
    (r"""tfds\.features\.Text\(\)""", r"""datasets.Value('string')"""),
    (r"""tfds\.features\.Text\(""", r"""datasets.Value('string'),"""),
    (r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
    (r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
    (r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (r"""tfds\.""", r"""datasets."""),
    (r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
    (r"""self\.builder_config""", r"""self.config"""),
]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			return ConvertCommand(args.tfds_path						,  args.datasets_directory  )




class 				_snake_case			(					lowercase__):




				@staticmethod
				def 							A__      (						__lowercase					:						ArgumentParser						):
							lowercase__					  =				parser.add_parser(
							    "convert",					help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",					)
							train_parser.add_argument(
							    "--tfds_path",					type=__lowercase,					required=__lowercase,					help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",					)
							train_parser.add_argument(
							    "--datasets_directory",					type=__lowercase,					required=__lowercase,					help="Path to the HuggingFace Datasets folder."						)
							train_parser.set_defaults(func=__lowercase						)




				def __init__(						self					:						Tuple,					__lowercase					:						str,					__lowercase					:						str,					*__lowercase					:						Tuple						):
							lowercase__					  =				get_logger("datasets-cli/converting"						)

							lowercase__					  =				tfds_path
							lowercase__					  =				datasets_directory




				def 							A__      (						self					:						Any						):
							if os.path.isdir(self._tfds_path						):
										lowercase__					  =				os.path.abspath(self._tfds_path						)
							elif os.path.isfile(self._tfds_path						):
										lowercase__					  =				os.path.dirname(self._tfds_path						)
							else:
										raise ValueError("--tfds_path is neither a directory nor a file. Please check path."						)

							lowercase__					  =				os.path.abspath(self._datasets_directory						)

							self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''						)

							lowercase__					  =				[]
							lowercase__					  =				[]
							lowercase__					  =				{}

							if os.path.isdir(self._tfds_path						):
										lowercase__					  =				os.listdir(__lowercase						)
							else:
										lowercase__					  =				[os.path.basename(self._tfds_path						)]

							for f_name in file_names:
										self._logger.info(F'''Looking at file {f_name}'''						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
										lowercase__					  =				os.path.join(__lowercase,					__lowercase						)

										if not os.path.isfile(__lowercase						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
													self._logger.info("Skipping file"						)
													continue

										with open(__lowercase,					encoding="utf-8"						) as f:
													lowercase__					  =				f.readlines()

										lowercase__					  =				[]
										lowercase__					  =				False
										lowercase__					  =				False
										lowercase__					  =				[]
										for line in lines:
													lowercase__					  =				line

													# Convert imports
													if "import tensorflow.compat.v2 as tf" in out_line:
																continue
													elif "@tfds.core" in out_line:
																continue
													elif "builder=self" in out_line:
																continue
													elif "import tensorflow_datasets.public_api as tfds" in out_line:
																lowercase__					  =				"import datasets\n"
													elif "import tensorflow" in out_line:
																# order is important here
																lowercase__					  =				""
																continue
													elif "from absl import logging" in out_line:
																lowercase__					  =				"from datasets import logging\n"
													elif "getLogger" in out_line:
																lowercase__					  =				out_line.replace("getLogger",					"get_logger"						)
													elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																lowercase__					  =				True
																lowercase__					  =				list(filter(lambda __lowercase						: e in out_line,					__lowercase						)						)
																out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowercase						) + "\n"						)
																out_lines.append(__lowercase						)
																out_lines.append(__lowercase						)
																continue
													else:
																for pattern, replacement in TO_CONVERT:
																			lowercase__					  =				re.sub(__lowercase,					__lowercase,					__lowercase						)

                # Take care of saving utilities (to later move them together with main script)
													if "tensorflow_datasets" in out_line:
																lowercase__					  =				re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)",					__lowercase						)
																tfds_imports.extend(imp.strip() for imp in match.group(1						).split(","						)						)
																lowercase__					  =				"from . import " + match.group(1						)

													# Check we have not forget anything
													if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																raise ValueError(F'''Error converting {out_line.strip()}'''						)

													if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																lowercase__					  =				True
													out_lines.append(__lowercase						)

										if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__					  =				f_name.replace(".py",					""						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													lowercase__					  =				os.path.join(__lowercase,					__lowercase						)
													os.makedirs(__lowercase,					exist_ok=__lowercase						)
													self._logger.info(F'''Adding directory {output_dir}'''						)
													imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}						)
										else:
													# Utilities will be moved at the end
													utils_files.append(__lowercase						)

										if needs_manual_update:
													with_manual_update.append(__lowercase						)

										with open(__lowercase,					"w",					encoding="utf-8"						) as f:
													f.writelines(__lowercase						)
										self._logger.info(F'''Converted in {output_file}'''						)

							for utils_file in utils_files:
										try:
													lowercase__					  =				os.path.basename(__lowercase						)
													lowercase__					  =				imports_to_builder_map[f_name.replace(".py",					""						)]
													self._logger.info(F'''Moving {dest_folder} to {utils_file}'''						)
													shutil.copy(__lowercase,					__lowercase						)
										except KeyError:
													self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''						)

							if with_manual_update:
										for file_path in with_manual_update:
													self._logger.warning(
													    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''						)




