




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 		_lowerCAmelCase       (							UpperCamelCase__	):
			"""simple docstring"""
			lowerCAmelCase__           ='''linear'''
			lowerCAmelCase__           ='''cosine'''
			lowerCAmelCase__           ='''cosine_with_restarts'''
			lowerCAmelCase__           ='''polynomial'''
			lowerCAmelCase__           ='''constant'''
			lowerCAmelCase__           ='''constant_with_warmup'''
			lowerCAmelCase__           ='''piecewise_constant'''







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optimizer						,						SCREAMING_SNAKE_CASE						:		int = -1 ):






	"""simple docstring"""
	return LambdaLR(UpperCamelCase__						,						lambda SCREAMING_SNAKE_CASE : 1						,						last_epoch=UpperCamelCase__ )







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optimizer						,						SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		int = -1 ):






	"""simple docstring"""

	def lr_lambda(SCREAMING_SNAKE_CASE						:		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 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optimizer						,						SCREAMING_SNAKE_CASE						:		str						,						SCREAMING_SNAKE_CASE						:		int = -1 ):






	"""simple docstring"""
	snake_case__						:     Tuple		={}
	snake_case__						:     int		=step_rules.split(''',''' )
	for rule_str in rule_list[:-1]:
		snake_case__,    snake_case__						:     List[str]		=rule_str.split(''':''' )
		snake_case__						:     List[str]		=int(UpperCamelCase__ )
		snake_case__						:     str		=float(UpperCamelCase__ )
		snake_case__						:     Union[str, Any]		=value
	snake_case__						:     List[Any]		=float(rule_list[-1] )

	def create_rules_function(SCREAMING_SNAKE_CASE						:		Optional[int]						,						SCREAMING_SNAKE_CASE						:		Any ):
		def rule_func(SCREAMING_SNAKE_CASE						:		int ) -> float:
			snake_case__						:     Union[str, Any]		=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

	snake_case__						:     Optional[Any]		=create_rules_function(UpperCamelCase__						,						UpperCamelCase__ )

	return LambdaLR(UpperCamelCase__						,						UpperCamelCase__						,						last_epoch=UpperCamelCase__ )







def 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Dict						,						SCREAMING_SNAKE_CASE						:		str						,						SCREAMING_SNAKE_CASE						:		Dict						,						SCREAMING_SNAKE_CASE						:		List[str]=-1 ):






	"""simple docstring"""

	def lr_lambda(SCREAMING_SNAKE_CASE						:		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 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optimizer						,						SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		float = 0.5						,						SCREAMING_SNAKE_CASE						:		int = -1 ):






	"""simple docstring"""

	def lr_lambda(SCREAMING_SNAKE_CASE						:		Any ):
		if current_step < num_warmup_steps:
			return float(UpperCamelCase__ ) / float(max(1						,						UpperCamelCase__ ) )
		snake_case__						:     str		=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 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Optimizer						,						SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		int						,						SCREAMING_SNAKE_CASE						:		int = 1						,						SCREAMING_SNAKE_CASE						:		int = -1 ):






	"""simple docstring"""

	def lr_lambda(SCREAMING_SNAKE_CASE						:		Optional[Any] ):
		if current_step < num_warmup_steps:
			return float(UpperCamelCase__ ) / float(max(1						,						UpperCamelCase__ ) )
		snake_case__						:     int		=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 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Union[str, Any]						,						SCREAMING_SNAKE_CASE						:		Optional[int]						,						SCREAMING_SNAKE_CASE						:		Any						,						SCREAMING_SNAKE_CASE						:		int=1E-7						,						SCREAMING_SNAKE_CASE						:		int=1.0						,						SCREAMING_SNAKE_CASE						:		Optional[int]=-1 ):






	"""simple docstring"""
	snake_case__						:     Any		=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(SCREAMING_SNAKE_CASE						:		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:
			snake_case__						:     int		=lr_init - lr_end
			snake_case__						:     Any		=num_training_steps - num_warmup_steps
			snake_case__						:     Optional[int]		=1 - (current_step - num_warmup_steps) / decay_steps
			snake_case__						:     int		=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 	lowercase_		(     SCREAMING_SNAKE_CASE						:		Union[str, SchedulerType]						,						SCREAMING_SNAKE_CASE						:		Optimizer						,						SCREAMING_SNAKE_CASE						:		Optional[str] = None						,						SCREAMING_SNAKE_CASE						:		Optional[int] = None						,						SCREAMING_SNAKE_CASE						:		Optional[int] = None						,						SCREAMING_SNAKE_CASE						:		int = 1						,						SCREAMING_SNAKE_CASE						:		float = 1.0						,						SCREAMING_SNAKE_CASE						:		int = -1						,						):






	"""simple docstring"""
	snake_case__						:     int		=SchedulerType(UpperCamelCase__ )
	snake_case__						:     Optional[Any]		=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__ )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCamelCase 					=      {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase 					=      ['SpeechEncoderDecoderModel']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase 					=      ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_lowerCamelCase 					=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)