
import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE							:int										=    'examples/'
SCREAMING_SNAKE_CASE							:str										=    {
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
SCREAMING_SNAKE_CASE							:int										=    {
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
SCREAMING_SNAKE_CASE							:List[str]										=    'README.md'


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.read()
				__A      ,							__A             =			REPLACE_PATTERNS[pattern]
				__A             =			replace.replace("VERSION"		,       a_						)
				__A             =			re_pattern.sub(a_		,       a_						)
				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.write(a_						)


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""simple docstring"""



				for folder, directories, fnames in os.walk(a_						):
								# Removing some of the folders with non-actively maintained examples from the walk
								if "research_projects" in directories:
												directories.remove("research_projects"						)
								if "legacy" in directories:
												directories.remove("legacy"						)
								for fname in fnames:
												if fname.endswith(".py"						):
																update_version_in_file(os.path.join(a_		,       a_						)		,       a_		,       pattern="examples"						)


def 						UpperCAmelCase      (     a_		,       a_=False						)		->     str:







				"""simple docstring"""



				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(a_		,       a_		,       a_						)
				if not patch:
								update_version_in_examples(a_						)


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			"🤗 Transformers currently provides the following architectures"
				__A             =			"1. Want to contribute a new model?"
				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.readlines()

				# Find the start of the list.
				__A             =			0
				while not lines[start_index].startswith(_start_prompt						):
								start_index += 1
				start_index += 1

				__A             =			start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt						):
								if lines[index].startswith("1."						):
												__A             =			lines[index].replace(
												    "https://huggingface.co/docs/diffusers/main/model_doc"		,       "https://huggingface.co/docs/diffusers/model_doc"		,       )
								index += 1

				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.writelines(a_						)


def 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				with open(REPLACE_FILES["init"]		,       "r"						) as f:
								__A             =			f.read()
				__A             =			REPLACE_PATTERNS["init"][0].search(a_						).groups()[0]
				return packaging.version.parse(a_						)


def 						UpperCAmelCase      (     a_=False						)		->     str:







				"""simple docstring"""



				__A             =			get_version()
				if patch and default_version.is_devrelease:
								raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"						)
				if default_version.is_devrelease:
								__A             =			default_version.base_version
				elif patch:
								__A             =			F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
				else:
								__A             =			F'''{default_version.major}.{default_version.minor + 1}.0'''

				# Now let's ask nicely if that's the right one.
				__A             =			input(F'''Which version are you releasing? [{default_version}]'''						)
				if len(a_						) == 0:
								__A             =			default_version

				print(F'''Updating version to {version}.'''						)
				global_version_update(a_		,       patch=a_						)


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			get_version()
				__A             =			F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
				__A             =			current_version.base_version

				# Check with the user we got that right.
				__A             =			input(F'''Which version are we developing now? [{dev_version}]'''						)
				if len(a_						) == 0:
								__A             =			dev_version

				print(F'''Updating version to {version}.'''						)
				global_version_update(a_						)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    argparse.ArgumentParser()
					parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
					parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print('Nothing to do after a patch :-)')
					else:
										post_release_work()


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			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.
					__A             =			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.
					__A             =			numpy.random.rand(
					    4							,3			)

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

					# Real output values provided.
					__A             =			output_array

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






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			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.
					__A             =			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.
					__A             =			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     UpperCamelCase_  (	self :			str			):
					__A             =			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			)							,)
					__A             =			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			)							,)
					__A             =			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     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					for iteration in range(1							,iterations + 1			):
									__A             =			self.feedforward()
									self.back_propagation()
									if give_loss:
													__A             =			numpy.mean(numpy.square(output - self.feedforward()			)			)
													print(f'''Iteration {iteration} Loss: {loss}'''			)







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

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

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

					__A             =			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      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			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.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			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=1_0		,       give_loss=a_						)

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


if __name__ == "__main__":
					example()
