





"""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


_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {
    """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  lowerCamelCase__ (	__snake_case			):
    SCREAMING_SNAKE_CASE		=      'gptj'
    SCREAMING_SNAKE_CASE		=      {
        'max_position_embeddings': 'n_positions',
        'hidden_size': 'n_embd',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }






    def __init__(			self     ,A=50_400     ,A=2_048     ,A=4_096     ,A=28     ,A=16     ,A=64     ,A=None     ,A="gelu_new"     ,A=0.0     ,A=0.0     ,A=0.0     ,A=1e-5     ,A=0.02     ,A=True     ,A=50_256     ,A=50_256     ,A=False     ,**A     ,):
          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=A     ,eos_token_id=A     ,tie_word_embeddings=A     ,**A )







class  lowerCamelCase__ (	__snake_case			):






    def __init__(			self     ,A     ,A = "default"     ,A = None     ,A = False     ,):
          super().__init__(A     ,task=A     ,patching_specs=A     ,use_past=A )
          if not getattr(self._config     ,"""pad_token_id"""     ,A ):
                # TODO: how to do that better?
                UpperCAmelCase          =	0






    @property
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
          if self.use_past:
                self.fill_with_past_key_values_(A     ,direction="""inputs""" )
                UpperCAmelCase          =	{0: """batch""", 1: """past_sequence + sequence"""}
          else:
                UpperCAmelCase          =	{0: """batch""", 1: """sequence"""}

          return common_inputs






    @property
    def     _UpperCamelCase							(			self ):
          return self._config.n_layer






    @property
    def     _UpperCamelCase							(			self ):
          return self._config.n_head






    def     _UpperCamelCase							(			self     ,A     ,A = -1     ,A = -1     ,A = False     ,A = None     ,):
          UpperCAmelCase          =	super(A     ,self ).generate_dummy_inputs(
              A     ,batch_size=A     ,seq_length=A     ,is_pair=A     ,framework=A )

          # 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(A ), torch.zeros(A )) 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(A     ,A     ,dtype=A )]     ,dim=1 )

          return ordered_inputs






    @property
    def     _UpperCamelCase							(			self ):
          return 13











"""simple docstring"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case = None				,	_snake_case = None							):







      """simple docstring"""






      if start is None:
            UpperCAmelCase          =	0

      if end is None:
            UpperCAmelCase          =	len(_snake_case							) - 1

      if start >= end:
            return

      UpperCAmelCase          =	(start + end) // 2

      slowsort(_snake_case				,	_snake_case				,	_snake_case							)
      slowsort(_snake_case				,	mid + 1				,	_snake_case							)

      if sequence[end] < sequence[mid]:
            UpperCAmelCase      , UpperCAmelCase          =	sequence[mid], sequence[end]

      slowsort(_snake_case				,	_snake_case				,	end - 1							)


if __name__ == "__main__":
  from doctest import testmod

  testmod()



