





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


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

__lowercase  :   Dict						      ={
    """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      A       (	__lowercase						):
      _snake_case							     ='''gptj'''
      _snake_case							     ={
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }



      def __init__(	self:						Optional[int]		,  _lowerCAmelCase:						List[str]=5_0400		,  _lowerCAmelCase:						Dict=2048		,  _lowerCAmelCase:						int=4096		,  _lowerCAmelCase:						str=28		,  _lowerCAmelCase:						int=16		,  _lowerCAmelCase:						Tuple=64		,  _lowerCAmelCase:						List[str]=None		,  _lowerCAmelCase:						Optional[Any]="gelu_new"		,  _lowerCAmelCase:						str=0.0		,  _lowerCAmelCase:						Union[str, Any]=0.0		,  _lowerCAmelCase:						List[Any]=0.0		,  _lowerCAmelCase:						Optional[int]=1e-5		,  _lowerCAmelCase:						Any=0.02		,  _lowerCAmelCase:						Optional[int]=True		,  _lowerCAmelCase:						List[Any]=5_0256		,  _lowerCAmelCase:						int=5_0256		,  _lowerCAmelCase:						List[str]=False		,  **_lowerCAmelCase:						List[Any]		,  )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=vocab_size
         UpperCAmelCase_											=n_positions
         UpperCAmelCase_											=n_embd
         UpperCAmelCase_											=n_layer
         UpperCAmelCase_											=n_head
         UpperCAmelCase_											=n_inner
         UpperCAmelCase_											=rotary_dim
         UpperCAmelCase_											=activation_function
         UpperCAmelCase_											=resid_pdrop
         UpperCAmelCase_											=embd_pdrop
         UpperCAmelCase_											=attn_pdrop
         UpperCAmelCase_											=layer_norm_epsilon
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=use_cache

         UpperCAmelCase_											=bos_token_id
         UpperCAmelCase_											=eos_token_id

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

class      A       (	__lowercase						):



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						PretrainedConfig		,  _lowerCAmelCase:						str = "default"		,  _lowerCAmelCase:						List[PatchingSpec] = None		,  _lowerCAmelCase:						bool = False		,  )						->    str:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase		,  task=_lowerCAmelCase		,  patching_specs=_lowerCAmelCase		,  use_past=_lowerCAmelCase       )
         if not getattr(self._config		,  "pad_token_id"		,  _lowerCAmelCase       ):
            # TODO: how to do that better?
            UpperCAmelCase_											=0



      @property
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Mapping[str, Mapping[int, str]]:
         '''simple docstring'''

         UpperCAmelCase_											=OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
         if self.use_past:
            self.fill_with_past_key_values_(_lowerCAmelCase		,  direction="inputs"       )
            UpperCAmelCase_											={0: "batch", 1: "past_sequence + sequence"}
         else:
            UpperCAmelCase_											={0: "batch", 1: "sequence"}

         return common_inputs



      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    int:
         '''simple docstring'''

         return self._config.n_layer



      @property
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         return self._config.n_head



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						PreTrainedTokenizer		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						Optional[TensorType] = None		,  )						->    Mapping[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=super(_lowerCAmelCase		,  self       ).generate_dummy_inputs(
             _lowerCAmelCase		,  batch_size=_lowerCAmelCase		,  seq_length=_lowerCAmelCase		,  is_pair=_lowerCAmelCase		,  framework=_lowerCAmelCase       )

         # We need to order the input in the way they appears in the forward()
         UpperCAmelCase_											=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

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

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

         return ordered_inputs






      @property
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    int:
         '''simple docstring'''

         return 13











__lowercase  :   Optional[Any]						      =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
   UpperCAmelCase_											=0
   UpperCAmelCase_											=0
   while place < len(lowercase__  ):
      if (place + 1 < len(lowercase__  )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   for arabic, roman in ROMAN:
      ((UpperCAmelCase_)						,       (UpperCAmelCase_))											=divmod(lowercase__  ,    lowercase__  )
      result.append(roman * factor  )
      if number == 0:
         break
   return "".join(lowercase__  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




