




"""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(     __a ):







 '''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__							:					Optimizer		,   snake_case__							:					int = -1    ):
  """simple docstring"""

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


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

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

  return LambdaLR(snake_case__		,   snake_case__		,   last_epoch=snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Optimizer		,   snake_case__							:					str		,   snake_case__							:					int = -1    ):
  """simple docstring"""

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

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

    return rule_func

  _snake_case							:		List[str]								=  create_rules_function(snake_case__		,   snake_case__    )

  return LambdaLR(snake_case__		,   snake_case__		,   last_epoch=snake_case__    )


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

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

  return LambdaLR(snake_case__		,   snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Optimizer		,   snake_case__							:					int		,   snake_case__							:					int		,   snake_case__							:					float = 0.5		,   snake_case__							:					int = -1    ):
  """simple docstring"""

  def lr_lambda(snake_case__							:					Dict    ):
    if current_step < num_warmup_steps:
      return float(snake_case__    ) / float(max(1		,   snake_case__    )    )
    _snake_case							:		List[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(snake_case__    ) * 2.0 * progress    ))    )

  return LambdaLR(snake_case__		,   snake_case__		,   snake_case__    )


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

  def lr_lambda(snake_case__							:					Dict    ):
    if current_step < num_warmup_steps:
      return float(snake_case__    ) / float(max(1		,   snake_case__    )    )
    _snake_case							:		str								=  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(snake_case__    ) * progress) % 1.0)    ))    )

  return LambdaLR(snake_case__		,   snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[Any]		,   snake_case__							:					List[Any]		,   snake_case__							:					int=1e-7		,   snake_case__							:					Optional[Any]=1.0		,   snake_case__							:					Any=-1    ):
  """simple docstring"""

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

  return LambdaLR(snake_case__		,   snake_case__		,   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__							:					Union[str, SchedulerType]		,   snake_case__							:					Optimizer		,   snake_case__							:					Optional[str] = None		,   snake_case__							:					Optional[int] = None		,   snake_case__							:					Optional[int] = None		,   snake_case__							:					int = 1		,   snake_case__							:					float = 1.0		,   snake_case__							:					int = -1		,   ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  SchedulerType(snake_case__    )
  _snake_case							:		Optional[Any]								=  TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(snake_case__		,   last_epoch=snake_case__    )

  if name == SchedulerType.PIECEWISE_CONSTANT:
    return schedule_func(snake_case__		,   step_rules=snake_case__		,   last_epoch=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(snake_case__		,   num_warmup_steps=snake_case__		,   last_epoch=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(
        snake_case__		,   num_warmup_steps=snake_case__		,   num_training_steps=snake_case__		,   num_cycles=snake_case__		,   last_epoch=snake_case__		,   )

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

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






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  TransfoXLTokenizer
    UpperCAmelCase__						:		str      =  False
    UpperCAmelCase__						:		Tuple      =  False






    def _a					(			self    )  ->       Union[str, Any]:
         super().setUp()

         __UpperCamelCase    							=[
             '<unk>',
             '[CLS]',
             '[SEP]',
             'want',
             'unwanted',
             'wa',
             'un',
             'running',
             ',',
             'low',
             'l',
         ]
         __UpperCamelCase    							=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 _a					(			self					,   **A_    )  ->       Optional[int]:
         __UpperCamelCase    							=True
         return TransfoXLTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='<unk> UNwanted , running'
         __UpperCamelCase    							='<unk> unwanted, running'
         return input_text, output_text






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=TransfoXLTokenizer(vocab_file=self.vocab_file					,   lower_case=A_    )

         __UpperCamelCase    							=tokenizer.tokenize('<unk> UNwanted , running'    )
         self.assertListEqual(A_					,   ['<unk>', 'unwanted', ',', 'running']    )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   [0, 4, 8, 7]    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['hello', '!', 'how', 'are', 'you', '?']    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )
         __UpperCamelCase    							='Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
         __UpperCamelCase    							=[
             'Hello',
             '(',
             'bracket',
             ')',
             'and',
             'side',
             '@-@',
             'scrolled',
             '[',
             'and',
             ']',
             'Henry',
             '\'s',
             '$',
             '5',
             '@,@',
             '000',
             'with',
             '3',
             '@.@',
             '34',
             'm',
             '.',
             'What',
             '\'s',
             'up',
             '!',
             '?',
         ]

         self.assertListEqual(tokenizer.tokenize(A_    )					,   A_    )

         self.assertEqual(tokenizer.convert_tokens_to_string(A_    )					,   A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=len(A_    )

         tokenizer.add_tokens(['new1', 'new2']    )
         tokenizer.move_added_token('new1'					,   1    )

         # Check that moved token is not copied (duplicate)
         self.assertEqual(len(A_    )					,   original_len + 2    )
         # Check that token is moved to specified id
         self.assertEqual(tokenizer.encode('new1'    )					,   [1]    )
         self.assertEqual(tokenizer.decode([1]    )					,   'new1'    )





