


"""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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						"""▁"""

UpperCAmelCase				=						{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase				=						{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase				=						{
    """facebook/xglm-564M""": 2048,
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase							       =					['''input_ids''', '''attention_mask''']





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

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

      _lowerCamelCase	:					Union[str, Any]   =		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      ,   )

      _lowerCamelCase	:					Tuple   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(str(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Union[str, Any]   =		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
      _lowerCamelCase	:					List[Any]   =		1

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

      _lowerCamelCase	:					str   =		len(self.sp_model)
      _lowerCamelCase	:					List[Any]   =		{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)

      _lowerCamelCase	:					Dict   =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}





   def __getstate__(							self)  ->  Optional[Any]:
      _lowerCamelCase	:					str   =		self.__dict__.copy()
      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					List[str]   =		self.sp_model.serialized_model_proto()
      return state





   def __setstate__(							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					int   =		d

      # for backward compatibility
      if not hasattr(self      ,   """sp_model_kwargs"""):
         _lowerCamelCase	:					Optional[int]   =		{}

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:

      if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
      _lowerCamelCase	:					List[Any]   =		[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = False)  ->  List[int]:

      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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:
      _lowerCamelCase	:					Optional[int]   =		[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 		UpperCamelCase_  (							self)  ->  List[str]:
      return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Dict   =		{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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      return self.sp_model.encode(SCREAMING_SNAKE_CASE      ,   out_type=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _lowerCamelCase	:					List[Any]   =		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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		"""""".join(SCREAMING_SNAKE_CASE).replace(SCREAMING_SNAKE_CASE      ,   """ """).strip()
      return out_string





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  Tuple[str]:
      if not os.path.isdir(SCREAMING_SNAKE_CASE):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
      _lowerCamelCase	:					Tuple   =		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:
            _lowerCamelCase	:					List[Any]   =		self.sp_model.serialized_model_proto()
            fi.write(SCREAMING_SNAKE_CASE)

      return (out_vocab_file,)





"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch

def 				_snake_case      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   for param in module.parameters():
      _lowerCamelCase	:					Optional[Any]   =		False

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      _lowerCamelCase	:					Any   =		"""mps"""
   if device == "mps":
      print(
          """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
          """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
          """ with generations."""   )
   return device

def 				_snake_case      (   __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		plt.imshow(__snake_case   )
   fig.axes.get_xaxis().set_visible(__snake_case   )
   fig.axes.get_yaxis().set_visible(__snake_case   )
   plt.show()





def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		datetime.now()
   _lowerCamelCase	:					Optional[Any]   =		current_time.strftime("""%H:%M:%S"""   )
   return timestamp

