


'''simple docstring'''

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      :Union[str, Any]    	=			logging.get_logger(__name__)
class _lowerCamelCase       (						UpperCamelCase_							):



		'''simple docstring'''







		A_	:			List[str]          =				'linear'
		A_	:			Optional[Any]          =				'cosine'
		A_	:			Union[str, Any]          =				'cosine_with_restarts'
		A_	:			List[Any]          =				'polynomial'
		A_	:			Optional[Any]          =				'constant'
		A_	:			Dict          =				'constant_with_warmup'
		A_	:			int          =				'piecewise_constant'







def lowerCamelCase			(			lowerCAmelCase				:	Optimizer		,		lowerCAmelCase				:	int = -1		):





	"""simple docstring"""







	return LambdaLR(SCREAMING_SNAKE_CASE__		,		lambda lowerCAmelCase		: 1		,		last_epoch=SCREAMING_SNAKE_CASE__		)





def lowerCamelCase			(			lowerCAmelCase				:	Optimizer		,		lowerCAmelCase				:	int		,		lowerCAmelCase				:	int = -1		):





	"""simple docstring"""







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

	return LambdaLR(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		last_epoch=SCREAMING_SNAKE_CASE__		)





def lowerCamelCase			(			lowerCAmelCase				:	Optimizer		,		lowerCAmelCase				:	str		,		lowerCAmelCase				:	int = -1		):





	"""simple docstring"""







	__magic_name__       :    List[str]     	=     {}
	__magic_name__       :    int     	=     step_rules.split(','		)
	for rule_str in rule_list[:-1]:
		__magic_name__       :    str     	=     rule_str.split(':'		)
		__magic_name__       :    Union[str, Any]     	=     int(SCREAMING_SNAKE_CASE__		)
		__magic_name__       :    Dict     	=     float(SCREAMING_SNAKE_CASE__		)
		__magic_name__       :    int     	=     value
	__magic_name__       :    Optional[int]     	=     float(rule_list[-1]		)

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

		return rule_func

	__magic_name__       :    List[str]     	=     create_rules_function(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		)

	return LambdaLR(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		last_epoch=SCREAMING_SNAKE_CASE__		)





def lowerCamelCase			(			lowerCAmelCase				:	Optional[Any]		,		lowerCAmelCase				:	str		,		lowerCAmelCase				:	Union[str, Any]		,		lowerCAmelCase				:	Optional[int]=-1		):





	"""simple docstring"""







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

	return LambdaLR(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		)





def lowerCamelCase			(			lowerCAmelCase				:	Optimizer		,		lowerCAmelCase				:	int		,		lowerCAmelCase				:	int		,		lowerCAmelCase				:	float = 0.5		,		lowerCAmelCase				:	int = -1		):





	"""simple docstring"""







	def lr_lambda(lowerCAmelCase				:	Any		):
		if current_step < num_warmup_steps:
			return float(SCREAMING_SNAKE_CASE__		) / float(max(1		,		SCREAMING_SNAKE_CASE__		)		)
		__magic_name__       :    Any     	=     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(SCREAMING_SNAKE_CASE__		) * 2.0 * progress		))		)

	return LambdaLR(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		)





def lowerCamelCase			(			lowerCAmelCase				:	Optimizer		,		lowerCAmelCase				:	int		,		lowerCAmelCase				:	int		,		lowerCAmelCase				:	int = 1		,		lowerCAmelCase				:	int = -1		):





	"""simple docstring"""







	def lr_lambda(lowerCAmelCase				:	Tuple		):
		if current_step < num_warmup_steps:
			return float(SCREAMING_SNAKE_CASE__		) / float(max(1		,		SCREAMING_SNAKE_CASE__		)		)
		__magic_name__       :    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(SCREAMING_SNAKE_CASE__		) * progress) % 1.0)		))		)

	return LambdaLR(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		)





def lowerCamelCase			(			lowerCAmelCase				:	str		,		lowerCAmelCase				:	str		,		lowerCAmelCase				:	Tuple		,		lowerCAmelCase				:	Union[str, Any]=1e-7		,		lowerCAmelCase				:	List[str]=1.0		,		lowerCAmelCase				:	List[Any]=-1		):





	"""simple docstring"""







	__magic_name__       :    Tuple     	=     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(lowerCAmelCase				:	int		):
		if current_step < num_warmup_steps:
			return float(SCREAMING_SNAKE_CASE__		) / float(max(1		,		SCREAMING_SNAKE_CASE__		)		)
		elif current_step > num_training_steps:
			return lr_end / lr_init  # as LambdaLR multiplies by lr_init
		else:
			__magic_name__       :    List[Any]     	=     lr_init - lr_end
			__magic_name__       :    Optional[Any]     	=     num_training_steps - num_warmup_steps
			__magic_name__       :    List[Any]     	=     1 - (current_step - num_warmup_steps) / decay_steps
			__magic_name__       :    str     	=     lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		)


lowerCAmelCase      :Union[str, Any]    	=			{
    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 lowerCamelCase			(			lowerCAmelCase				:	Union[str, SchedulerType]		,		lowerCAmelCase				:	Optimizer		,		lowerCAmelCase				:	Optional[str] = None		,		lowerCAmelCase				:	Optional[int] = None		,		lowerCAmelCase				:	Optional[int] = None		,		lowerCAmelCase				:	int = 1		,		lowerCAmelCase				:	float = 1.0		,		lowerCAmelCase				:	int = -1		,		):





	"""simple docstring"""







	__magic_name__       :    Optional[int]     	=     SchedulerType(SCREAMING_SNAKE_CASE__		)
	__magic_name__       :    int     	=     TYPE_TO_SCHEDULER_FUNCTION[name]
	if name == SchedulerType.CONSTANT:
		return schedule_func(SCREAMING_SNAKE_CASE__		,		last_epoch=SCREAMING_SNAKE_CASE__		)

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

	# 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(SCREAMING_SNAKE_CASE__		,		num_warmup_steps=SCREAMING_SNAKE_CASE__		,		last_epoch=SCREAMING_SNAKE_CASE__		)

	# 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(
		    SCREAMING_SNAKE_CASE__		,		num_warmup_steps=SCREAMING_SNAKE_CASE__		,		num_training_steps=SCREAMING_SNAKE_CASE__		,		num_cycles=SCREAMING_SNAKE_CASE__		,		last_epoch=SCREAMING_SNAKE_CASE__		,		)

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

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
 from ..ta.tokenization_ta import TaTokenizer
else:
 from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase											=       TaTokenizer

if is_tokenizers_available():
 from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
 from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase											=       TaTokenizerFast

lowerCamelCase											=       {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       [
     'MT5EncoderModel',
     'MT5ForConditionalGeneration',
     'MT5ForQuestionAnswering',
     'MT5Model',
     'MT5PreTrainedModel',
     'MT5Stack',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
 from .configuration_mta import MTaConfig, MTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mta import (
      MTaEncoderModel,
      MTaForConditionalGeneration,
      MTaForQuestionAnswering,
      MTaModel,
      MTaPreTrainedModel,
      MTaStack,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
 import sys

 lowerCamelCase											=       _LazyModule(
     __name__,
     globals()['__file__'],
     _import_structure,
     extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
     module_spec=__spec__,
 )


