





import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "linear"
							lowerCamelCase_							     =       "cosine"
							lowerCamelCase_							     =       "cosine_with_restarts"
							lowerCamelCase_							     =       "polynomial"
							lowerCamelCase_							     =       "constant"
							lowerCamelCase_							     =       "constant_with_warmup"
							lowerCamelCase_							     =       "piecewise_constant"






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optimizer       , UpperCamelCase__:				int = -1     ):
		return LambdaLR(UpperCamelCase__       , lambda UpperCamelCase__     : 1       , last_epoch=UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optimizer       , UpperCamelCase__:				int       , UpperCamelCase__:				int = -1     ):

		def lr_lambda(UpperCamelCase__:				int     ):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase__     ) / float(max(1.0       , UpperCamelCase__     )     )
				return 1.0

		return LambdaLR(UpperCamelCase__       , UpperCamelCase__       , last_epoch=UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optimizer       , UpperCamelCase__:				str       , UpperCamelCase__:				int = -1     ):
		SCREAMING_SNAKE_CASE__								=   {}
		SCREAMING_SNAKE_CASE__								=   step_rules.split(""","""     )
		for rule_str in rule_list[:-1]:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   rule_str.split(""":"""     )
				SCREAMING_SNAKE_CASE__								=   int(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   float(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   value
		SCREAMING_SNAKE_CASE__								=   float(rule_list[-1]     )

		def create_rules_function(UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Any     ):
				def rule_func(UpperCamelCase__:				int     ) -> float:
						SCREAMING_SNAKE_CASE__								=   sorted(rules_dict.keys()     )
						for i, sorted_step in enumerate(UpperCamelCase__     ):
								if steps < sorted_step:
										return rules_dict[sorted_steps[i]]
						return last_lr_multiple

				return rule_func

		SCREAMING_SNAKE_CASE__								=   create_rules_function(UpperCamelCase__       , UpperCamelCase__     )

		return LambdaLR(UpperCamelCase__       , UpperCamelCase__       , last_epoch=UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				str       , UpperCamelCase__:				Dict       , UpperCamelCase__:				List[str]=-1     ):

		def lr_lambda(UpperCamelCase__:				int     ):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase__     ) / float(max(1       , UpperCamelCase__     )     )
				return max(
				    0.0       , float(num_training_steps - current_step     ) / float(max(1       , num_training_steps - num_warmup_steps     )     )     )

		return LambdaLR(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optimizer       , UpperCamelCase__:				int       , UpperCamelCase__:				int       , UpperCamelCase__:				float = 0.5       , UpperCamelCase__:				int = -1     ):

		def lr_lambda(UpperCamelCase__:				Any     ):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase__     ) / float(max(1       , UpperCamelCase__     )     )
				SCREAMING_SNAKE_CASE__								=   float(current_step - num_warmup_steps     ) / float(max(1       , num_training_steps - num_warmup_steps     )     )
				return max(0.0       , 0.5 * (1.0 + math.cos(math.pi * float(UpperCamelCase__     ) * 2.0 * progress     ))     )

		return LambdaLR(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optimizer       , UpperCamelCase__:				int       , UpperCamelCase__:				int       , UpperCamelCase__:				int = 1       , UpperCamelCase__:				int = -1     ):

		def lr_lambda(UpperCamelCase__:				Optional[Any]     ):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase__     ) / float(max(1       , UpperCamelCase__     )     )
				SCREAMING_SNAKE_CASE__								=   float(current_step - num_warmup_steps     ) / float(max(1       , num_training_steps - num_warmup_steps     )     )
				if progress >= 1.0:
						return 0.0
				return max(0.0       , 0.5 * (1.0 + math.cos(math.pi * ((float(UpperCamelCase__     ) * progress) % 1.0)     ))     )

		return LambdaLR(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Any       , UpperCamelCase__:				int=1e-7       , UpperCamelCase__:				int=1.0       , UpperCamelCase__:				Optional[int]=-1     ):
		SCREAMING_SNAKE_CASE__								=   optimizer.defaults["""lr"""]
		if not (lr_init > lr_end):
				raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''     )

		def lr_lambda(UpperCamelCase__:				int     ):
				if current_step < num_warmup_steps:
						return float(UpperCamelCase__     ) / float(max(1       , UpperCamelCase__     )     )
				elif current_step > num_training_steps:
						return lr_end / lr_init  # as LambdaLR multiplies by lr_init
				else:
						SCREAMING_SNAKE_CASE__								=   lr_init - lr_end
						SCREAMING_SNAKE_CASE__								=   num_training_steps - num_warmup_steps
						SCREAMING_SNAKE_CASE__								=   1 - (current_step - num_warmup_steps) / decay_steps
						SCREAMING_SNAKE_CASE__								=   lr_range * pct_remaining**power + lr_end
						return decay / lr_init  # as LambdaLR multiplies by lr_init

		return LambdaLR(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )


_lowerCamelCase 					=      {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, SchedulerType]       , UpperCamelCase__:				Optimizer       , UpperCamelCase__:				Optional[str] = None       , UpperCamelCase__:				Optional[int] = None       , UpperCamelCase__:				Optional[int] = None       , UpperCamelCase__:				int = 1       , UpperCamelCase__:				float = 1.0       , UpperCamelCase__:				int = -1       , ):
		SCREAMING_SNAKE_CASE__								=   SchedulerType(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   TYPE_TO_SCHEDULER_FUNCTION[name]
		if name == SchedulerType.CONSTANT:
				return schedule_func(UpperCamelCase__       , last_epoch=UpperCamelCase__     )

		if name == SchedulerType.PIECEWISE_CONSTANT:
				return schedule_func(UpperCamelCase__       , step_rules=UpperCamelCase__       , last_epoch=UpperCamelCase__     )

		# All other schedulers require `num_warmup_steps`
		if num_warmup_steps is None:
				raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.'''     )

		if name == SchedulerType.CONSTANT_WITH_WARMUP:
				return schedule_func(UpperCamelCase__       , num_warmup_steps=UpperCamelCase__       , last_epoch=UpperCamelCase__     )

		# All other schedulers require `num_training_steps`
		if num_training_steps is None:
				raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.'''     )

		if name == SchedulerType.COSINE_WITH_RESTARTS:
				return schedule_func(
				    UpperCamelCase__       , num_warmup_steps=UpperCamelCase__       , num_training_steps=UpperCamelCase__       , num_cycles=UpperCamelCase__       , last_epoch=UpperCamelCase__       , )

		if name == SchedulerType.POLYNOMIAL:
				return schedule_func(
				    UpperCamelCase__       , num_warmup_steps=UpperCamelCase__       , num_training_steps=UpperCamelCase__       , power=UpperCamelCase__       , last_epoch=UpperCamelCase__       , )

		return schedule_func(
		    UpperCamelCase__       , num_warmup_steps=UpperCamelCase__       , num_training_steps=UpperCamelCase__       , last_epoch=UpperCamelCase__     )



import qiskit


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						qiskit.Aer.get_backend("aer_simulator" )

		# Create a Quantum Circuit acting on the q register
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						qiskit.QuantumCircuit(a     , a )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0 )
		circuit.x(1 )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]     , [0, 1] )

		# Execute the circuit on the qasm simulator
		SCREAMING_SNAKE_CASE_				:Any				     =						qiskit.execute(a     , a     , shots=1000 )

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(a )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			single_qubit_measure(2, 2)
		print(F'''Total count for various states are: {counts}''')






