





'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a        =						logging.get_logger(__name__)


a        =						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

a        =						{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

a        =						{"facebook/blenderbot_small-90M": 512}

def 						__magic_name__       (							__UpperCAmelCase      )						->					List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       set()
   __SCREAMING_SNAKE_CASE      							=       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)      )
      __SCREAMING_SNAKE_CASE      							=       char

   __SCREAMING_SNAKE_CASE      							=       set(__UpperCAmelCase      )
   return pairs







class __a (   _snake_case			):
   __UpperCamelCase   : List[Any]	      =	VOCAB_FILES_NAMES
   __UpperCamelCase   : Dict	      =	PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase   : Union[str, Any]	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase   : Optional[int]	      =	['input_ids', 'attention_mask']





   def __init__(					self				:			Union[str, Any]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Tuple="__start__"				,lowerCamelCase				:			Tuple="__end__"				,lowerCamelCase				:			Dict="__unk__"				,lowerCamelCase				:			str="__null__"				,**lowerCamelCase				:			List[Any]				,):

      '''simple docstring'''

      super().__init__(unk_token=lowerCamelCase				,bos_token=lowerCamelCase				,eos_token=lowerCamelCase				,pad_token=lowerCamelCase				,**lowerCamelCase    )

      with open(lowerCamelCase				,encoding="""utf-8"""    ) as vocab_handle:
         __SCREAMING_SNAKE_CASE      							=       json.load(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       {v: k for k, v in self.encoder.items()}
      with open(lowerCamelCase				,encoding="""utf-8"""    ) as merges_handle:
         __SCREAMING_SNAKE_CASE      							=       merges_handle.read().split("""\n"""    )[1:-1]
      __SCREAMING_SNAKE_CASE      							=       [tuple(merge.split()    ) for merge in merges]
      __SCREAMING_SNAKE_CASE      							=       dict(zip(lowerCamelCase				,range(len(lowerCamelCase    )    )    )    )
      __SCREAMING_SNAKE_CASE      							=       {}





   @property
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      return len(self.encoder    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      return dict(self.encoder				,**self.added_tokens_encoder    )





   def      UpperCAmelCase__     (					self				:			Any				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      if token in self.cache:
         return self.cache[token]
      __SCREAMING_SNAKE_CASE      							=       re.sub("""([.,!?()])"""				,r""" \1"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       re.sub("""(')"""				,r""" \1 """				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       re.sub(r"""\s{2,}"""				,""" """				,lowerCamelCase    )
      if "\n" in token:
         __SCREAMING_SNAKE_CASE      							=       token.replace("""\n"""				,""" __newln__"""    )

      __SCREAMING_SNAKE_CASE      							=       token.split(""" """    )
      __SCREAMING_SNAKE_CASE      							=       []
      for token in tokens:
         if not len(lowerCamelCase    ):
            continue

         __SCREAMING_SNAKE_CASE      							=       token.lower()
         __SCREAMING_SNAKE_CASE      							=       tuple(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       tuple(list(word[:-1]    ) + [word[-1] + """</w>"""]    )
         __SCREAMING_SNAKE_CASE      							=       get_pairs(lowerCamelCase    )

         if not pairs:
            words.append(lowerCamelCase    )
            continue

         while True:
            __SCREAMING_SNAKE_CASE      							=       min(lowerCamelCase				,key=lambda lowerCamelCase    : self.bpe_ranks.get(lowerCamelCase				,float("""inf"""    )    )    )
            if bigram not in self.bpe_ranks:
               break
            __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       bigram
            __SCREAMING_SNAKE_CASE      							=       []
            __SCREAMING_SNAKE_CASE      							=       0

            while i < len(lowerCamelCase    ):
               try:
                  __SCREAMING_SNAKE_CASE      							=       word.index(lowerCamelCase				,lowerCamelCase    )
                  new_word.extend(word[i:j]    )
                  __SCREAMING_SNAKE_CASE      							=       j
               except ValueError:
                  new_word.extend(word[i:]    )
                  break

               if word[i] == first and i < len(lowerCamelCase    ) - 1 and word[i + 1] == second:
                  new_word.append(first + second    )
                  i += 2
               else:
                  new_word.append(word[i]    )
                  i += 1
            __SCREAMING_SNAKE_CASE      							=       tuple(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       new_word
            if len(lowerCamelCase    ) == 1:
               break
            else:
               __SCREAMING_SNAKE_CASE      							=       get_pairs(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       """@@ """.join(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       word[:-4]

         __SCREAMING_SNAKE_CASE      							=       word
         words.append(lowerCamelCase    )
      return " ".join(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			int				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       []

      __SCREAMING_SNAKE_CASE      							=       re.findall(r"""\S+\n?"""				,lowerCamelCase    )

      for token in words:
         split_tokens.extend(list(self.bpe(lowerCamelCase    ).split(""" """    )    )    )
      return split_tokens





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       token.lower()
      return self.encoder.get(lowerCamelCase				,self.encoder.get(self.unk_token    )    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			int    ):

      '''simple docstring'''

      return self.decoder.get(lowerCamelCase				,self.unk_token    )





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       """ """.join(lowerCamelCase    ).replace("""@@ """				,""""""    ).strip()
      return out_string





   def      UpperCAmelCase__     (					self				:			List[str]				,lowerCamelCase				:			str				,lowerCamelCase				:			Optional[str] = None    ):

      '''simple docstring'''

      if not os.path.isdir(lowerCamelCase    ):
         logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
         return
      __SCREAMING_SNAKE_CASE      							=       os.path.join(
          lowerCamelCase				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
      __SCREAMING_SNAKE_CASE      							=       os.path.join(
          lowerCamelCase				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]    )

      with open(lowerCamelCase				,"""w"""				,encoding="""utf-8"""    ) as f:
         f.write(json.dumps(self.encoder				,indent=2				,sort_keys=lowerCamelCase				,ensure_ascii=lowerCamelCase    ) + """\n"""    )

      __SCREAMING_SNAKE_CASE      							=       0
      with open(lowerCamelCase				,"""w"""				,encoding="""utf-8"""    ) as writer:
         writer.write("""#version: 0.2\n"""    )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,key=lambda lowerCamelCase    : kv[1]    ):
            if index != token_index:
               logger.warning(
                   f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                   """ Please check that the tokenizer is not corrupted!"""    )
               __SCREAMING_SNAKE_CASE      							=       token_index
            writer.write(""" """.join(lowerCamelCase    ) + """\n"""    )
            index += 1

      return vocab_file, merge_file












'''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 __a (   _snake_case			):
   __UpperCamelCase   : int	      =	'linear'
   __UpperCamelCase   : Tuple	      =	'cosine'
   __UpperCamelCase   : Tuple	      =	'cosine_with_restarts'
   __UpperCamelCase   : List[Any]	      =	'polynomial'
   __UpperCamelCase   : Optional[Any]	      =	'constant'
   __UpperCamelCase   : Optional[int]	      =	'constant_with_warmup'
   __UpperCamelCase   : List[Any]	      =	'piecewise_constant'





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					List[Any]:

   '''simple docstring'''







   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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}
   __SCREAMING_SNAKE_CASE      							=       step_rules.split(""","""      )
   for rule_str in rule_list[:-1]:
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       rule_str.split(""":"""      )
      __SCREAMING_SNAKE_CASE      							=       int(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       float(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       value
   __SCREAMING_SNAKE_CASE      							=       float(rule_list[-1]      )

   def create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      ):
      def rule_func(__UpperCAmelCase      ) -> float:
         __SCREAMING_SNAKE_CASE      							=       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

   __SCREAMING_SNAKE_CASE      							=       create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=-1      )						->					int:

   '''simple docstring'''







   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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0.5							,	__UpperCAmelCase = -1      )						->					Dict:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = -1      )						->					Tuple:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=1e-7							,	__UpperCAmelCase=1.0							,	__UpperCAmelCase=-1      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       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:
         __SCREAMING_SNAKE_CASE      							=       lr_init - lr_end
         __SCREAMING_SNAKE_CASE      							=       num_training_steps - num_warmup_steps
         __SCREAMING_SNAKE_CASE      							=       1 - (current_step - num_warmup_steps) / decay_steps
         __SCREAMING_SNAKE_CASE      							=       lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )


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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = 1.0							,	__UpperCAmelCase = -1							,	)						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SchedulerType(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       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      )





