
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_  :	List[str] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	List[str] 				=					"""▁"""

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_  :	Tuple 				=					{
    """facebook/xglm-564M""": 20_48,
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  str    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Tuple    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Dict    =	["""input_ids""", """attention_mask"""]

			def __init__(    self	:    Optional[Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Tuple="<s>"  ,		snake_case_	:    Optional[int]="</s>"  ,		snake_case_	:    Tuple="</s>"  ,		snake_case_	:    Tuple="<s>"  ,		snake_case_	:    Optional[int]="<unk>"  ,		snake_case_	:    int="<pad>"  ,		snake_case_	:    Optional[Dict[str, Any]] = None  ,		**snake_case_	:    List[Any]  ,		):
							UpperCamelCase_:							str		=  {} if sp_model_kwargs is None else sp_model_kwargs

							# Compatibility with the original tokenizer
							UpperCamelCase_:							Optional[Any]		=  7
							UpperCamelCase_:							Any		=  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )]

							UpperCamelCase_:							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=snake_case_  ,		eos_token=snake_case_  ,		unk_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		pad_token=snake_case_  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**snake_case_  ,		)

							UpperCamelCase_:							str		=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(str(snake_case_ ) )
							UpperCamelCase_:							List[str]		=  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
							UpperCamelCase_:							List[Any]		=  1

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

							UpperCamelCase_:							str		=  len(self.sp_model )
							UpperCamelCase_:							Tuple		=  {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
							self.fairseq_tokens_to_ids.update(snake_case_ )

							UpperCamelCase_:							Optional[Any]		=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def __getstate__(    self	:    Tuple ):
							UpperCamelCase_:							List[str]		=  self.__dict__.copy()
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							List[str]		=  self.sp_model.serialized_model_proto()
							return state

			def __setstate__(    self	:    Optional[int]  ,		snake_case_	:    Union[str, Any] ):
							UpperCamelCase_:							Tuple		=  d

							# for backward compatibility
							if not hasattr(self  ,		"""sp_model_kwargs""" ):
											UpperCamelCase_:							Any		=  {}

							UpperCamelCase_:							Tuple		=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto )

			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):

							if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
							UpperCamelCase_:							Optional[Any]		=  [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None  ,		snake_case_	:    bool = False ):

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

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

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Any		=  [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  lowerCAmelCase__		(    self	:    int ):
							return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Tuple		=  {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    str ):
							return self.sp_model.encode(snake_case_  ,		out_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    Union[str, Any] ):
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							UpperCamelCase_:							Optional[Any]		=  self.sp_model.PieceToId(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  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Union[str, Any] ):
							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  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Any ):
							UpperCamelCase_:							int		=  """""".join(snake_case_ ).replace(snake_case_  ,		""" """ ).strip()
							return out_string




			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							if not os.path.isdir(snake_case_ ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
											return
							UpperCamelCase_:							Optional[int]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

							return (out_vocab_file,)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Optional[int] 				=					{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	"""ctrl"""
			__UpperCamelCase :  Dict    =	["""past_key_values"""]
			__UpperCamelCase :  List[str]    =	{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}

			def __init__(    self	:    Dict  ,		snake_case_	:    Any=24_6534  ,		snake_case_	:    Dict=256  ,		snake_case_	:    str=1280  ,		snake_case_	:    Optional[int]=8192  ,		snake_case_	:    Union[str, Any]=48  ,		snake_case_	:    Any=16  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    Any=0.1  ,		snake_case_	:    Any=1e-6  ,		snake_case_	:    Optional[Any]=0.02  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  vocab_size
							UpperCamelCase_:							Union[str, Any]		=  n_positions
							UpperCamelCase_:							Optional[int]		=  n_embd
							UpperCamelCase_:							int		=  n_layer
							UpperCamelCase_:							str		=  n_head
							UpperCamelCase_:							Optional[int]		=  dff
							UpperCamelCase_:							Optional[Any]		=  resid_pdrop
							UpperCamelCase_:							Union[str, Any]		=  embd_pdrop
							UpperCamelCase_:							List[str]		=  layer_norm_epsilon
							UpperCamelCase_:							Optional[Any]		=  initializer_range

							UpperCamelCase_:							Optional[Any]		=  use_cache

							super().__init__(**snake_case_ )






