'''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: int          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



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


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = -1     ):
 return LambdaLR(UpperCAmelCase		,     lambda UpperCAmelCase     : 1		,     last_epoch=UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = -1     ):

 def lr_lambda(UpperCAmelCase     ):
  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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = -1     ):
 lowercase__				:    Optional[Any]  =					{}
 lowercase__				:    List[Any]  =					step_rules.split(''','''     )
 for rule_str in rule_list[:-1]:
  lowercase__ ,							lowercase__				:    List[Any]  =					rule_str.split(''':'''     )
  lowercase__				:    int  =					int(UpperCAmelCase     )
  lowercase__				:    Optional[Any]  =					float(UpperCAmelCase     )
  lowercase__				:    str  =					value
 lowercase__				:    Dict  =					float(rule_list[-1]     )

 def create_rules_function(UpperCAmelCase		,     UpperCAmelCase     ):
  def rule_func(UpperCAmelCase     ) -> float:
   lowercase__				:    int  =					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

 lowercase__				:    Optional[int]  =					create_rules_function(UpperCAmelCase		,     UpperCAmelCase     )

 return LambdaLR(UpperCAmelCase		,     UpperCAmelCase		,     last_epoch=UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=-1     ):

 def lr_lambda(UpperCAmelCase     ):
  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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = 0.5		,     UpperCAmelCase = -1     ):

 def lr_lambda(UpperCAmelCase     ):
  if current_step < num_warmup_steps:
   return float(UpperCAmelCase     ) / float(max(1		,     UpperCAmelCase     )     )
  lowercase__				:    List[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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = 1		,     UpperCAmelCase = -1     ):

 def lr_lambda(UpperCAmelCase     ):
  if current_step < num_warmup_steps:
   return float(UpperCAmelCase     ) / float(max(1		,     UpperCAmelCase     )     )
  lowercase__				:    List[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(UpperCAmelCase     ) * progress) % 1.0)     ))     )

 return LambdaLR(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=1E-7		,     UpperCAmelCase=1.0		,     UpperCAmelCase=-1     ):
 lowercase__				:    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(UpperCAmelCase     ):
  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:
   lowercase__				:    str  =					lr_init - lr_end
   lowercase__				:    Dict  =					num_training_steps - num_warmup_steps
   lowercase__				:    int  =					1 - (current_step - num_warmup_steps) / decay_steps
   lowercase__				:    int  =					lr_range * pct_remaining**power + lr_end
   return decay / lr_init  # as LambdaLR multiplies by lr_init

 return LambdaLR(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )


__a: Dict          =   {
    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       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 1.0		,     UpperCAmelCase = -1		,     ):
 lowercase__				:    Union[str, Any]  =					SchedulerType(UpperCAmelCase     )
 lowercase__				:    str  =					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     )


'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().setUp()

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

