
import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
					from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
					from tensorflow.keras.optimizers import Adam

class       a		(   tf.keras.optimizers.schedules.LearningRateSchedule					):







							def __init__(						self  :  Dict      ,				snake_case__  :  float      ,				snake_case__  :  Callable      ,				snake_case__  :  int      ,				snake_case__  :  float = 1.0      ,				snake_case__  :  str = None      ,				):
								"""simple docstring"""







								super().__init__()
								__lowerCAmelCase         =							initial_learning_rate
								__lowerCAmelCase         =							warmup_steps
								__lowerCAmelCase         =							power
								__lowerCAmelCase         =							decay_schedule_fn
								__lowerCAmelCase         =							name







							def __call__(						self  :  int      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								with tf.name_scope(self.name or "WarmUp" ) as name:
									# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
									# learning rate will be `global_step/num_warmup_steps * init_lr`.
									__lowerCAmelCase         =							tf.cast(snake_case__      ,				tf.floataa )
									__lowerCAmelCase         =							tf.cast(self.warmup_steps      ,				tf.floataa )
									__lowerCAmelCase         =							global_step_float / warmup_steps_float
									__lowerCAmelCase         =							self.initial_learning_rate * tf.math.pow(snake_case__      ,				self.power )
									return tf.cond(
									    global_step_float < warmup_steps_float      ,				lambda: warmup_learning_rate      ,				lambda: self.decay_schedule_fn(step - self.warmup_steps )      ,				name=snake_case__      ,				)







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return {
								    "initial_learning_rate": self.initial_learning_rate,
								    "decay_schedule_fn": self.decay_schedule_fn,
								    "warmup_steps": self.warmup_steps,
								    "power": self.power,
								    "name": self.name,
								}






def 		_UpperCAmelCase   (	UpperCamelCase:     float			,							UpperCamelCase:     int			,							UpperCamelCase:     int			,							UpperCamelCase:     float = 0.0			,							UpperCamelCase:     float = 0.9			,							UpperCamelCase:     float = 0.999			,							UpperCamelCase:     float = 1e-8			,							UpperCamelCase:     Optional[float] = None			,							UpperCamelCase:     Optional[float] = None			,							UpperCamelCase:     float = 0.0			,							UpperCamelCase:     float = 1.0			,							UpperCamelCase:     Optional[List[str]] = None			,							):






	"""simple docstring"""


	__lowerCAmelCase         =							tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=UpperCamelCase			,							decay_steps=num_train_steps - num_warmup_steps			,							end_learning_rate=init_lr * min_lr_ratio			,							power=UpperCamelCase			,							)
	if num_warmup_steps:
		__lowerCAmelCase         =							WarmUp(
		    initial_learning_rate=UpperCamelCase			,							decay_schedule_fn=UpperCamelCase			,							warmup_steps=UpperCamelCase			,							)
	if weight_decay_rate > 0.0:
		__lowerCAmelCase         =							AdamWeightDecay(
		    learning_rate=UpperCamelCase			,							weight_decay_rate=UpperCamelCase			,							beta_a=UpperCamelCase			,							beta_a=UpperCamelCase			,							epsilon=UpperCamelCase			,							clipnorm=UpperCamelCase			,							global_clipnorm=UpperCamelCase			,							exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]			,							include_in_weight_decay=UpperCamelCase			,							)
	else:
		__lowerCAmelCase         =							tf.keras.optimizers.Adam(
		    learning_rate=UpperCamelCase			,							beta_a=UpperCamelCase			,							beta_a=UpperCamelCase			,							epsilon=UpperCamelCase			,							clipnorm=UpperCamelCase			,							global_clipnorm=UpperCamelCase			,							)
	# We return the optimizer and the LR scheduler in order to better track the
	# evolution of the LR independently of the optimizer.
	return optimizer, lr_schedule

class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  int      ,				snake_case__  :  Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1      ,				snake_case__  :  float = 0.9      ,				snake_case__  :  float = 0.9_9_9      ,				snake_case__  :  float = 1E-7      ,				snake_case__  :  bool = False      ,				snake_case__  :  float = 0.0      ,				snake_case__  :  Optional[List[str]] = None      ,				snake_case__  :  Optional[List[str]] = None      ,				snake_case__  :  str = "AdamWeightDecay"      ,				**snake_case__  :  Tuple      ,				):
								"""simple docstring"""







								super().__init__(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ )
								__lowerCAmelCase         =							weight_decay_rate
								__lowerCAmelCase         =							include_in_weight_decay
								__lowerCAmelCase         =							exclude_from_weight_decay







							@classmethod
							def       UpperCAmelCase__					(						cls  :  Dict      ,				snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							{"WarmUp": WarmUp}
								return super(snake_case__      ,				cls ).from_config(snake_case__      ,				custom_objects=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







								super(snake_case__      ,				self )._prepare_local(snake_case__      ,				snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							tf.constant(
								    self.weight_decay_rate      ,				name="adam_weight_decay_rate" )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self._do_use_weight_decay(var.name )
								if do_decay:
									return var.assign_sub(
									    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]      ,				use_locking=self._use_locking      ,				)
								return tf.no_op()







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Optional[int]=None      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							list(zip(*snake_case__ ) )
								return super(snake_case__      ,				self ).apply_gradients(zip(snake_case__      ,				snake_case__ )      ,				name=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  List[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  int ):
								"""simple docstring"""







								if apply_state is None:
									return self._decayed_lr_t[var_dtype], {}

								__lowerCAmelCase         =							apply_state or {}
								__lowerCAmelCase         =							apply_state.get((var_device, var_dtype) )
								if coefficients is None:
									__lowerCAmelCase         =							self._fallback_apply_state(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							coefficients

								return coefficients["lr_t"], {"apply_state": apply_state}







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Optional[Any]=None ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							self._get_lr(var.device      ,				var.dtype.base_dtype      ,				snake_case__ )
								__lowerCAmelCase         =							self._decay_weights_op(snake_case__      ,				snake_case__      ,				snake_case__ )
								with tf.control_dependencies([decay] ):
									return super(snake_case__      ,				self )._resource_apply_dense(snake_case__      ,				snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Dict      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  List[Any]=None ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							self._get_lr(var.device      ,				var.dtype.base_dtype      ,				snake_case__ )
								__lowerCAmelCase         =							self._decay_weights_op(snake_case__      ,				snake_case__      ,				snake_case__ )
								with tf.control_dependencies([decay] ):
									return super(snake_case__      ,				self )._resource_apply_sparse(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							super().get_config()
								config.update({"weight_decay_rate": self.weight_decay_rate} )
								return config







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								if self.weight_decay_rate == 0:
									return False

								if self._include_in_weight_decay:
									for r in self._include_in_weight_decay:
										if re.search(snake_case__      ,				snake_case__ ) is not None:
											return True

								if self._exclude_from_weight_decay:
									for r in self._exclude_from_weight_decay:
										if re.search(snake_case__      ,				snake_case__ ) is not None:
											return False
								return True

class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								__lowerCAmelCase         =							None







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								if self._accum_steps is None:
									__lowerCAmelCase         =							tf.Variable(
									    tf.constant(0      ,				dtype=tf.intaa )      ,				trainable=snake_case__      ,				synchronization=tf.VariableSynchronization.ON_READ      ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,				)

								return self._accum_steps.value()







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								if not self._gradients:
									raise ValueError("The accumulator should be called first to initialize the gradients" )
								return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]







							def __call__(						self  :  Tuple      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								if not self._gradients:
									__lowerCAmelCase         =							self.step  # Create the step variable.
									self._gradients.extend(
									    [
									        tf.Variable(
									            tf.zeros_like(snake_case__ )      ,				trainable=snake_case__      ,				synchronization=tf.VariableSynchronization.ON_READ      ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,				)
									        if gradient is not None
									        else gradient
									        for gradient in gradients
									    ] )
								if len(snake_case__ ) != len(self._gradients ):
									raise ValueError(F"Expected {len(self._gradients )} gradients, but got {len(snake_case__ )}" )

								for accum_gradient, gradient in zip(self._gradients      ,				snake_case__ ):
									if accum_gradient is not None and gradient is not None:
										accum_gradient.assign_add(snake_case__ )

								self._accum_steps.assign_add(1 )







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								if not self._gradients:
									return
								self._accum_steps.assign(0 )
								for gradient in self._gradients:
									if gradient is not None:
										gradient.assign(tf.zeros_like(snake_case__ ) )








import math
import sys






def 		_UpperCAmelCase   (	UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							""
	try:
		with open(UpperCamelCase			,							"rb"  ) as binary_file:
			__lowerCAmelCase         =							binary_file.read()
		for dat in data:
			__lowerCAmelCase         =							F"{dat:08b}"
			result += curr_byte
		return result
	except OSError:
		print("File not accessible"  )
		sys.exit()






def 		_UpperCAmelCase   (	UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							{"0": "0", "1": "1"}
	__lowerCAmelCase   ,						__lowerCAmelCase         =							"", ""
	__lowerCAmelCase         =							len(UpperCamelCase  )

	for i in range(len(UpperCamelCase  )  ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		__lowerCAmelCase         =							lexicon[curr_string]
		result += last_match_id
		__lowerCAmelCase         =							last_match_id + "0"

		if math.loga(UpperCamelCase  ).is_integer():
			__lowerCAmelCase         =							{}
			for curr_key in list(UpperCamelCase  ):
				__lowerCAmelCase         =							lexicon.pop(UpperCamelCase  )
			__lowerCAmelCase         =							new_lex

		__lowerCAmelCase         =							last_match_id + "1"
		index += 1
		__lowerCAmelCase         =							""
	return result






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							8
	try:
		with open(UpperCamelCase			,							"wb"  ) as opened_file:
			__lowerCAmelCase         =							[
			    to_write[i : i + byte_length]
			    for i in range(0			,							len(UpperCamelCase  )			,							UpperCamelCase  )
			]

			if len(result_byte_array[-1]  ) % byte_length == 0:
				result_byte_array.append("10000000"  )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]  ) - 1
				)

			for elem in result_byte_array[:-1]:
				opened_file.write(int(UpperCamelCase			,							2  ).to_bytes(1			,							byteorder="big"  )  )
	except OSError:
		print("File not accessible"  )
		sys.exit()






def 		_UpperCAmelCase   (	UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							0
	for letter in data_bits:
		if letter == "1":
			break
		counter += 1

	__lowerCAmelCase         =							data_bits[counter:]
	__lowerCAmelCase         =							data_bits[counter + 1 :]
	return data_bits






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							read_file_binary(UpperCamelCase  )
	__lowerCAmelCase         =							remove_prefix(UpperCamelCase  )
	__lowerCAmelCase         =							decompress_data(UpperCamelCase  )
	write_file_binary(UpperCamelCase			,							UpperCamelCase  )


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])






