





import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__lowercase       :Any       =  "http://www.mocksite.com/file1.txt"
__lowercase       :Dict       =  "\"text\": [\"foo\", \"foo\"]"
__lowercase       :Optional[Any]       =  "6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"



class    _a     :






							"""simple docstring"""

							snake_case_							 =						2_00
							snake_case_							 =						{"Content-Length": "100"}
							snake_case_							 =						{}

							def      A_  (      self				:     str							,	**a				:     Union[str, Any]    )	->str:
													return [bytes(a							,	"utf-8"    )]







def       UpperCAmelCase							(     *_lowerCamelCase      :  Dict			,   **_lowerCamelCase      :  int     ):





						'''simple docstring'''


						return MockResponse()







@pytest.mark.parametrize("urls_type"			,   [str, list, dict]     )
def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Any			,   _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


						import requests

						monkeypatch.setattr(_lowerCamelCase			,   "request"			,   _lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				str   =					URL
						if issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					url
						elif issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Any   =					[url]
						elif issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{"train": url}
						SCREAMING_SNAKE_CASE__     :				Tuple   =					"dummy"
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"downloads"
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					tmp_path
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					DownloadConfig(
						    cache_dir=os.path.join(_lowerCamelCase			,   _lowerCamelCase     )			,   use_etag=_lowerCamelCase			,   )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					DownloadManager(dataset_name=_lowerCamelCase			,   download_config=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					dl_manager.download(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					urls
						for downloaded_paths in [downloaded_paths]:
												if isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																		SCREAMING_SNAKE_CASE__     :				str   =					[downloaded_paths]
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					[urls]
												elif isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																		assert "train" in downloaded_paths.keys()
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					downloaded_paths.values()
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					urls.values()
												assert downloaded_paths
												for downloaded_path, input_url in zip(_lowerCamelCase			,   _lowerCamelCase     ):
																		assert downloaded_path == dl_manager.downloaded_paths[input_url]
																		SCREAMING_SNAKE_CASE__     :				Dict   =					Path(_lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					downloaded_path.parts
																		assert parts[-1] == HASH
																		assert parts[-2] == cache_subdir
																		assert downloaded_path.exists()
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					downloaded_path.read_text()
																		assert content == CONTENT
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					downloaded_path.with_suffix(".json"     )
																		assert metadata_downloaded_path.exists()
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					json.loads(metadata_downloaded_path.read_text()     )
																		assert metadata_content == {"url": URL, "etag": None}







@pytest.mark.parametrize("paths_type"			,   [str, list, dict]     )
def       UpperCAmelCase							(     _lowerCamelCase      :  Any			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					str(_lowerCamelCase     )
						if issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					filename
						elif issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				int   =					[filename]
						elif issubclass(_lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				List[str]   =					{"train": filename}
						SCREAMING_SNAKE_CASE__     :				Tuple   =					"dummy"
						SCREAMING_SNAKE_CASE__     :				int   =					xz_file.parent
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					"extracted"
						SCREAMING_SNAKE_CASE__     :				Dict   =					DownloadConfig(
						    cache_dir=_lowerCamelCase			,   use_etag=_lowerCamelCase			,   )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					DownloadManager(dataset_name=_lowerCamelCase			,   download_config=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					dl_manager.extract(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					paths
						for extracted_paths in [extracted_paths]:
												if isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																		SCREAMING_SNAKE_CASE__     :				Any   =					[extracted_paths]
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[paths]
												elif isinstance(_lowerCamelCase			,   _lowerCamelCase     ):
																		assert "train" in extracted_paths.keys()
																		SCREAMING_SNAKE_CASE__     :				str   =					extracted_paths.values()
																		SCREAMING_SNAKE_CASE__     :				Any   =					paths.values()
												assert extracted_paths
												for extracted_path, input_path in zip(_lowerCamelCase			,   _lowerCamelCase     ):
																		assert extracted_path == dl_manager.extracted_paths[input_path]
																		SCREAMING_SNAKE_CASE__     :				Any   =					Path(_lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				Any   =					extracted_path.parts
																		assert parts[-1] == hash_url_to_filename(_lowerCamelCase			,   etag=_lowerCamelCase     )
																		assert parts[-2] == extracted_subdir
																		assert extracted_path.exists()
																		SCREAMING_SNAKE_CASE__     :				int   =					extracted_path.read_text()
																		SCREAMING_SNAKE_CASE__     :				str   =					text_file.read_text()
																		assert extracted_file_content == expected_file_content







def       UpperCAmelCase							(     _lowerCamelCase      :  Any			,   _lowerCamelCase      :  List[Any]     ):





						'''simple docstring'''


						assert path.endswith(".jsonl"     )
						for num_items, line in enumerate(_lowerCamelCase			,   start=1     ):
												SCREAMING_SNAKE_CASE__     :				List[str]   =					json.loads(line.decode("utf-8"     )     )
												assert item.keys() == {"col_1", "col_2", "col_3"}
						assert num_items == 4







@pytest.mark.parametrize("archive_jsonl"			,   ["tar_jsonl_path", "zip_jsonl_path"]     )
def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					request.getfixturevalue(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					DownloadManager()
						for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase     )			,   start=1     ):
												_test_jsonl(_lowerCamelCase			,   _lowerCamelCase     )
						assert num_jsonl == 2







@pytest.mark.parametrize("archive_nested_jsonl"			,   ["tar_nested_jsonl_path", "zip_nested_jsonl_path"]     )
def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					request.getfixturevalue(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					DownloadManager()
						for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase     )			,   start=1     ):
												for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCamelCase     )			,   start=1     ):
																		_test_jsonl(_lowerCamelCase			,   _lowerCamelCase     )
						assert num_tar == 1
						assert num_jsonl == 2







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					DownloadManager()
						for num_file, file in enumerate(dl_manager.iter_files(_lowerCamelCase     )			,   start=1     ):
												assert os.path.basename(_lowerCamelCase     ) == ("test.txt" if num_file == 1 else "train.txt")
						assert num_file == 2






import numpy



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     numpy.ndarray							,	a				:     numpy.ndarray    )	->None:
													SCREAMING_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.
													SCREAMING_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.
													SCREAMING_SNAKE_CASE__     :				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.
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					numpy.random.rand(3							,	1    )

													# Real output values provided.
													SCREAMING_SNAKE_CASE__     :				str   =					output_array

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

							def      A_  (      self				:     Union[str, Any]    )	->numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[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.
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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.
													SCREAMING_SNAKE_CASE__     :				int   =					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				:     int    )	->None:
													SCREAMING_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    )							,	)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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    )							,	)
													SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     int							,	a				:     numpy.ndarray							,	a				:     int							,	a				:     bool    )	->None:
													for iteration in range(1							,	iterations + 1    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.feedforward()
																			self.back_propagation()
																			if give_loss:
																									SCREAMING_SNAKE_CASE__     :				int   =					numpy.mean(numpy.square(output - self.feedforward()    )    )
																									print(f"""Iteration {iteration} Loss: {loss}"""    )






							def      A_  (      self				:     Tuple							,	a				:     numpy.ndarray    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_arr

													SCREAMING_SNAKE_CASE__     :				Dict   =					sigmoid(
													    numpy.dot(self.array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													SCREAMING_SNAKE_CASE__     :				Any   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													SCREAMING_SNAKE_CASE__     :				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       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return 1 / (1 + numpy.exp(-value     ))







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return (value) * (1 - (value))







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					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.
						SCREAMING_SNAKE_CASE__     :				Any   =					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,   dtype=numpy.floataa     )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					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=10			,   give_loss=_lowerCamelCase     )

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


if __name__ == "__main__":
				example()