


"""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


__snake_case    					=					logging.get_logger(__name__)

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       str									=    '''linear'''
   _a   :       str									=    '''cosine'''
   _a   :       Any									=    '''cosine_with_restarts'''
   _a   :       Any									=    '''polynomial'''
   _a   :       Dict									=    '''constant'''
   _a   :       Optional[int]									=    '''constant_with_warmup'''
   _a   :       Optional[Any]									=    '''piecewise_constant'''


def    _lowerCamelCase   (     lowerCamelCase__	:							Optimizer       ,		lowerCamelCase__	:							int = -1  ):
 return LambdaLR(lowerCamelCase__       ,		lambda lowerCamelCase__  : 1       ,		last_epoch=lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							Optimizer       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int = -1  ):

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

 return LambdaLR(lowerCamelCase__       ,		lowerCamelCase__       ,		last_epoch=lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							Optimizer       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							int = -1  ):
 lowercase__       :       str				=						{}
 lowercase__       :       str				=						step_rules.split(""","""  )
 for rule_str in rule_list[:-1]:
  lowercase__	,       lowercase__       :       Union[str, Any]				=						rule_str.split(""":"""  )
  lowercase__       :       Optional[int]				=						int(lowerCamelCase__  )
  lowercase__       :       List[str]				=						float(lowerCamelCase__  )
  lowercase__       :       Optional[int]				=						value
 lowercase__       :       Optional[Any]				=						float(rule_list[-1]  )

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

  return rule_func

 lowercase__       :       Tuple				=						create_rules_function(lowerCamelCase__       ,		lowerCamelCase__  )

 return LambdaLR(lowerCamelCase__       ,		lowerCamelCase__       ,		last_epoch=lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict=-1  ):

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

 return LambdaLR(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							Optimizer       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							float = 0.5       ,		lowerCamelCase__	:							int = -1  ):

 def lr_lambda(lowerCamelCase__	:							Tuple  ):
  if current_step < num_warmup_steps:
   return float(lowerCamelCase__  ) / float(max(1       ,		lowerCamelCase__  )  )
  lowercase__       :       Union[str, 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(lowerCamelCase__  ) * 2.0 * progress  ))  )

 return LambdaLR(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							Optimizer       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							int = 1       ,		lowerCamelCase__	:							int = -1  ):

 def lr_lambda(lowerCamelCase__	:							Union[str, Any]  ):
  if current_step < num_warmup_steps:
   return float(lowerCamelCase__  ) / float(max(1       ,		lowerCamelCase__  )  )
  lowercase__       :       Tuple				=						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(lowerCamelCase__  ) * progress) % 1.0)  ))  )

 return LambdaLR(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							List[str]=1E-7       ,		lowerCamelCase__	:							Optional[int]=1.0       ,		lowerCamelCase__	:							List[str]=-1  ):
 lowercase__       :       List[str]				=						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(lowerCamelCase__  ) / float(max(1       ,		lowerCamelCase__  )  )
  elif current_step > num_training_steps:
   return lr_end / lr_init  # as LambdaLR multiplies by lr_init
  else:
   lowercase__       :       Dict				=						lr_init - lr_end
   lowercase__       :       List[Any]				=						num_training_steps - num_warmup_steps
   lowercase__       :       List[Any]				=						1 - (current_step - num_warmup_steps) / decay_steps
   lowercase__       :       Dict				=						lr_range * pct_remaining**power + lr_end
   return decay / lr_init  # as LambdaLR multiplies by lr_init

 return LambdaLR(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )


__snake_case    					=					{
    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       ,		):
 lowercase__       :       Optional[int]				=						SchedulerType(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						TYPE_TO_SCHEDULER_FUNCTION[name]
 if name == SchedulerType.CONSTANT:
  return schedule_func(lowerCamelCase__       ,		last_epoch=lowerCamelCase__  )

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

 # 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(lowerCamelCase__       ,		num_warmup_steps=lowerCamelCase__       ,		last_epoch=lowerCamelCase__  )

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

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

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



"""simple docstring"""


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    LayoutLMTokenizer
   _a   :       Dict									=    LayoutLMTokenizerFast
   _a   :       Dict									=    True
   _a   :       Any									=    True


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    super().setUp()

    lowercase__       :       int				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       Dict				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Optional[int]:
    return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[str]				=						"""UNwant\u00E9d,running"""
    lowercase__       :       Optional[int]				=						"""unwanted, running"""
    return input_text, output_text


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						self.tokenizer_class(self.vocab_file						)

    lowercase__       :       Optional[Any]				=						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
    self.assertListEqual(lowerCamelCase__ ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__						) ,     [7, 4, 5, 10, 8, 9]						)







   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass