'''simple docstring'''

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_A     : Union[str, Any]    =							logging.get_logger(__name__)

_A     : Any    =							{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE	:			Optional[Any]       =   """gptj"""
 _SCREAMING_SNAKE_CASE	:			str       =   {
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__( self  :      Optional[int] ,			SCREAMING_SNAKE_CASE__  :      Tuple=5_04_00 ,			SCREAMING_SNAKE_CASE__  :      Tuple=20_48 ,			SCREAMING_SNAKE_CASE__  :      Tuple=40_96 ,			SCREAMING_SNAKE_CASE__  :      Dict=28 ,			SCREAMING_SNAKE_CASE__  :      Dict=16 ,			SCREAMING_SNAKE_CASE__  :      Tuple=64 ,			SCREAMING_SNAKE_CASE__  :      int=None ,			SCREAMING_SNAKE_CASE__  :      str="gelu_new" ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=0.0 ,			SCREAMING_SNAKE_CASE__  :      Tuple=0.0 ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any]=0.0 ,			SCREAMING_SNAKE_CASE__  :      Optional[int]=1e-5 ,			SCREAMING_SNAKE_CASE__  :      Dict=0.0_2 ,			SCREAMING_SNAKE_CASE__  :      Dict=True ,			SCREAMING_SNAKE_CASE__  :      Tuple=5_02_56 ,			SCREAMING_SNAKE_CASE__  :      Tuple=5_02_56 ,			SCREAMING_SNAKE_CASE__  :      Any=False ,			**SCREAMING_SNAKE_CASE__  :      List[str] ,			)    ->       Any:
  __lowerCAmelCase											=		vocab_size
  __lowerCAmelCase											=		n_positions
  __lowerCAmelCase											=		n_embd
  __lowerCAmelCase											=		n_layer
  __lowerCAmelCase											=		n_head
  __lowerCAmelCase											=		n_inner
  __lowerCAmelCase											=		rotary_dim
  __lowerCAmelCase											=		activation_function
  __lowerCAmelCase											=		resid_pdrop
  __lowerCAmelCase											=		embd_pdrop
  __lowerCAmelCase											=		attn_pdrop
  __lowerCAmelCase											=		layer_norm_epsilon
  __lowerCAmelCase											=		initializer_range
  __lowerCAmelCase											=		use_cache

  __lowerCAmelCase											=		bos_token_id
  __lowerCAmelCase											=		eos_token_id

  super().__init__(
      bos_token_id=snake_case__ ,			eos_token_id=snake_case__ ,			tie_word_embeddings=snake_case__ ,			**snake_case__				)







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 def __init__( self  :      Any ,			SCREAMING_SNAKE_CASE__  :      PretrainedConfig ,			SCREAMING_SNAKE_CASE__  :      str = "default" ,			SCREAMING_SNAKE_CASE__  :      List[PatchingSpec] = None ,			SCREAMING_SNAKE_CASE__  :      bool = False ,			)    ->       List[Any]:
  super().__init__(snake_case__ ,			task=snake_case__ ,			patching_specs=snake_case__ ,			use_past=snake_case__				)
  if not getattr(self._config ,			"""pad_token_id""" ,			snake_case__				):
   # TODO: how to do that better?
   __lowerCAmelCase											=		0
 @property
 def a   ( self  :      Optional[Any]				)    ->       List[str]:
  __lowerCAmelCase											=		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
  if self.use_past:
   self.fill_with_past_key_values_(snake_case__ ,			direction="""inputs"""				)
   __lowerCAmelCase											=		{0: """batch""", 1: """past_sequence + sequence"""}
  else:
   __lowerCAmelCase											=		{0: """batch""", 1: """sequence"""}

  return common_inputs
 @property
 def a   ( self  :      List[str]				)    ->       Any:
  return self._config.n_layer
 @property
 def a   ( self  :      Tuple				)    ->       Optional[int]:
  return self._config.n_head
 def a   ( self  :      Dict ,			SCREAMING_SNAKE_CASE__  :      PreTrainedTokenizer ,			SCREAMING_SNAKE_CASE__  :      int = -1 ,			SCREAMING_SNAKE_CASE__  :      int = -1 ,			SCREAMING_SNAKE_CASE__  :      bool = False ,			SCREAMING_SNAKE_CASE__  :      Optional[TensorType] = None ,			)    ->       Tuple:
  __lowerCAmelCase											=		super(snake_case__ ,			self				).generate_dummy_inputs(
      snake_case__ ,			batch_size=snake_case__ ,			seq_length=snake_case__ ,			is_pair=snake_case__ ,			framework=snake_case__				)

  # We need to order the input in the way they appears in the forward()
  __lowerCAmelCase											=		OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}				)

  # Need to add the past_keys
  if self.use_past:
   if not is_torch_available():
    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
   else:
    import torch

    __lowerCAmelCase				,			__lowerCAmelCase											=		common_inputs["""input_ids"""].shape
    # Not using the same length for past_key_values
    __lowerCAmelCase											=		seqlen + 2
    __lowerCAmelCase											=		(
        batch,
        self.num_attention_heads,
        past_key_values_length,
        self._config.hidden_size // self.num_attention_heads,
    )
    __lowerCAmelCase											=		[
        (torch.zeros(snake_case__				), torch.zeros(snake_case__				)) for _ in range(self.num_layers				)
    ]

  __lowerCAmelCase											=		common_inputs["""attention_mask"""]
  if self.use_past:
   __lowerCAmelCase											=		ordered_inputs["""attention_mask"""].dtype
   __lowerCAmelCase											=		torch.cat(
       [ordered_inputs["""attention_mask"""], torch.ones(snake_case__ ,			snake_case__ ,			dtype=snake_case__				)] ,			dim=1				)

  return ordered_inputs





 @property
 def a   ( self  :      Optional[int]				)    ->       Tuple:
  return 13









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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(snake_case__				,     lstrip=snake_case__				,     rstrip=snake_case__     ) if isinstance(snake_case__				,     snake_case__     ) else mask_token

     super().__init__(
         snake_case__				,     tokenizer_file=snake_case__				,     do_lower_case=snake_case__				,     remove_space=snake_case__				,     keep_accents=snake_case__				,     bos_token=snake_case__				,     eos_token=snake_case__				,     unk_token=snake_case__				,     sep_token=snake_case__				,     pad_token=snake_case__				,     cls_token=snake_case__				,     mask_token=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]
     if token_ids_a is None:
      return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'     )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep     ) * [0]
     return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      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__     ):
      copyfile(self.vocab_file				,     snake_case__     )

     return (out_vocab_file,)

