



'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase							:str				    =					logging.get_logger(__name__)

lowerCamelCase							:Union[str, Any]				    =					'▁'

lowerCamelCase							:Dict				    =					{'vocab_file': 'sentencepiece.bpe.model'}

lowerCamelCase							:List[Any]				    =					{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCamelCase							:Dict				    =					{
    'facebook/xglm-564M': 2_0_4_8,
}







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Tuple					=					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE   :		str					=					PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE   :		int					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE   :		Dict					=					['input_ids', 'attention_mask']


	def __init__(self       ,      lowercase       ,      lowercase="<s>"       ,      lowercase="</s>"       ,      lowercase="</s>"       ,      lowercase="<s>"       ,      lowercase="<unk>"       ,      lowercase="<pad>"       ,      lowercase = None       ,      **lowercase       ,      ):
				A_	:						Tuple	=     {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				A_	:						List[Any]	=     7
				A_	:						int	=     [F'<madeupword{i}>' for i in range(self.num_madeup_words							)]

				A_	:						int	=     kwargs.get("""additional_special_tokens"""       ,      []							)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=__SCREAMING_SNAKE_CASE       ,      eos_token=__SCREAMING_SNAKE_CASE       ,      unk_token=__SCREAMING_SNAKE_CASE       ,      sep_token=__SCREAMING_SNAKE_CASE       ,      cls_token=__SCREAMING_SNAKE_CASE       ,      pad_token=__SCREAMING_SNAKE_CASE       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **__SCREAMING_SNAKE_CASE       ,      )

				A_	:						List[Any]	=     spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(str(__SCREAMING_SNAKE_CASE							)							)
				A_	:						Dict	=     vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				A_	:						Optional[Any]	=     1

				# Mimic fairseq token-to-id alignment for the first 4 token
				A_	:						Tuple	=     {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

				A_	:						Optional[int]	=     len(self.sp_model							)
				A_	:						Dict	=     {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
				self.fairseq_tokens_to_ids.update(__SCREAMING_SNAKE_CASE							)

				A_	:						Union[str, Any]	=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}


	def __getstate__(self							):
				A_	:						Tuple	=     self.__dict__.copy()
				A_	:						int	=     None
				A_	:						Any	=     self.sp_model.serialized_model_proto()
				return state


	def __setstate__(self       ,      lowercase							):
				A_	:						List[str]	=     d

				# for backward compatibility
				if not hasattr(self       ,      """sp_model_kwargs"""							):
							A_	:						Union[str, Any]	=     {}

				A_	:						List[str]	=     spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


	def     _a	(self       ,      lowercase       ,      lowercase = None							):




				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				A_	:						Optional[int]	=     [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a


	def     _a	(self       ,      lowercase       ,      lowercase = None       ,      lowercase = False							):




				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__SCREAMING_SNAKE_CASE       ,      token_ids_a=__SCREAMING_SNAKE_CASE       ,      already_has_special_tokens=__SCREAMING_SNAKE_CASE							)

				if token_ids_a is None:
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE							))
				return [1] + ([0] * len(__SCREAMING_SNAKE_CASE							)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE							))


	def     _a	(self       ,      lowercase       ,      lowercase = None							):
				A_	:						List[str]	=     [self.sep_token_id]

				if token_ids_a is None:
							return len(sep + token_ids_a							) * [0]
				return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]


	@property
	def     _a	(self							):
				return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words


	def     _a	(self							):
				A_	:						Any	=     {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def     _a	(self       ,      lowercase							):
				return self.sp_model.encode(__SCREAMING_SNAKE_CASE       ,      out_type=__SCREAMING_SNAKE_CASE							)


	def     _a	(self       ,      lowercase							):




				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				A_	:						List[str]	=     self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE							)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


	def     _a	(self       ,      lowercase							):




				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset							)


	def     _a	(self       ,      lowercase							):
				A_	:						str	=     """""".join(__SCREAMING_SNAKE_CASE							).replace(__SCREAMING_SNAKE_CASE       ,      """ """							).strip()
				return out_string


	def     _a	(self       ,      lowercase       ,      lowercase = None							):




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

				if os.path.abspath(self.vocab_file							) != os.path.abspath(__SCREAMING_SNAKE_CASE							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file       ,      __SCREAMING_SNAKE_CASE							)
				elif not os.path.isfile(self.vocab_file							):
							with open(__SCREAMING_SNAKE_CASE       ,      """wb"""							) as fi:
										A_	:						Optional[int]	=     self.sp_model.serialized_model_proto()
										fi.write(__SCREAMING_SNAKE_CASE							)

				return (out_vocab_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


UpperCAmelCase :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



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







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


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

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """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(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

     def create_rules_function(a__      ,    a__						):
          def rule_func(a__						) -> float:
               __SCREAMING_SNAKE_CASE     =      sorted(rules_dict.keys()						)
               for i, sorted_step in enumerate(a__						):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


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

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __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(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __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(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """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(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          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(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    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    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(a__      ,    last_epoch=a__						)

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

     # 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(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

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

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

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






