




"""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


A_						=				logging.get_logger(__name__)






class 					lowercase(     __lowerCamelCase ):







 '''simple docstring'''





 lowercase__ 					=					"linear"
 lowercase__ 					=					"cosine"
 lowercase__ 					=					"cosine_with_restarts"
 lowercase__ 					=					"polynomial"
 lowercase__ 					=					"constant"
 lowercase__ 					=					"constant_with_warmup"
 lowercase__ 					=					"piecewise_constant"







def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[Any] = -1    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Any		,   snake_case__							:					Any = -1    ):
  """simple docstring"""

  def lr_lambda(snake_case__							:					Optional[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 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					List[str] = -1    ):
  """simple docstring"""

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

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

  _snake_case							:		List[Any]								=  create_rules_function(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__    )

  return LambdaLR(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__		,   last_epoch=SCREAMING_SNAKE_CASE__    )


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					List[str]		,   snake_case__							:					Any		,   snake_case__							:					Tuple=-1    ):
  """simple docstring"""

  def lr_lambda(snake_case__							:					Tuple    ):
    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 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					int		,   snake_case__							:					Tuple		,   snake_case__							:					Union[str, Any] = 0.5		,   snake_case__							:					List[str] = -1    ):
  """simple docstring"""

  def lr_lambda(snake_case__							:					int    ):
    if current_step < num_warmup_steps:
      return float(SCREAMING_SNAKE_CASE__    ) / float(max(1		,   SCREAMING_SNAKE_CASE__    )    )
    _snake_case							:		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 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]		,   snake_case__							:					Optional[int] = 1		,   snake_case__							:					List[Any] = -1    ):
  """simple docstring"""

  def lr_lambda(snake_case__							:					Optional[int]    ):
    if current_step < num_warmup_steps:
      return float(SCREAMING_SNAKE_CASE__    ) / float(max(1		,   SCREAMING_SNAKE_CASE__    )    )
    _snake_case							:		Any								=  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 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					Any=1e-7		,   snake_case__							:					Dict=1.0		,   snake_case__							:					Any=-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(snake_case__							:					Tuple    ):
    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:
      _snake_case							:		List[str]								=  lr_init - lr_end
      _snake_case							:		Any								=  num_training_steps - num_warmup_steps
      _snake_case							:		Any								=  1 - (current_step - num_warmup_steps) / decay_steps
      _snake_case							:		Any								=  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__    )


A_						=				{
    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 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					Dict		,   snake_case__							:					Optional[Any] = None		,   snake_case__							:					int = None		,   snake_case__							:					Any = None		,   snake_case__							:					int = 1		,   snake_case__							:					Tuple = 1.0		,   snake_case__							:					Dict = -1		,   ):
  """simple docstring"""

  _snake_case							:		List[str]								=  SchedulerType(SCREAMING_SNAKE_CASE__    )
  _snake_case							:		Any								=  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__    )









import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class     __snake_case       (							unittest.TestCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[int]   =						load_tool("""text-classification"""		)
         self.tool.setup()
         __snake_case:						Dict   =						load_tool("""text-classification"""							,				remote=A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[str]   =						self.tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						List[str]   =						self.remote_tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						self.tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						int   =						self.remote_tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






