





'''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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    '''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_		(		a__		):
 __lowerCamelCase						:							Union[str, Any]         =					"""gptj"""
 __lowerCamelCase						:							Dict         =					{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }




 def __init__(	self			,     _lowerCAmelCase=50400			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4096			,     _lowerCAmelCase=28			,     _lowerCAmelCase=16			,     _lowerCAmelCase=64			,     _lowerCAmelCase=None			,     _lowerCAmelCase="gelu_new"			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=1E-5			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=True			,     _lowerCAmelCase=50256			,     _lowerCAmelCase=50256			,     _lowerCAmelCase=False			,     **_lowerCAmelCase			,     )    ->							Dict:
     _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=lowerCAmelCase__			,     eos_token_id=lowerCAmelCase__			,     tie_word_embeddings=lowerCAmelCase__			,     **lowerCAmelCase__		)







class    lowerCAmelCase_		(		a__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = "default"			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False			,     )    ->							Dict:
     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?
         _lowerCAmelCase				=				0




 @property
 def 							_snake_case   (	self		)    ->							Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase				=				OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
     if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase__			,     direction="inputs"		)
         _lowerCAmelCase				=				{0: "batch", 1: "past_sequence + sequence"}
     else:
         _lowerCAmelCase				=				{0: "batch", 1: "sequence"}

     return common_inputs




 @property
 def 							_snake_case   (	self		)    ->							int:
     return self._config.n_layer




 @property
 def 							_snake_case   (	self		)    ->							int:
     return self._config.n_head




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = -1			,     _lowerCAmelCase = -1			,     _lowerCAmelCase = False			,     _lowerCAmelCase = None			,     )    ->							Mapping[str, Any]:
     _lowerCAmelCase				=				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()
     _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				=				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(lowerCAmelCase__		), torch.zeros(lowerCAmelCase__		)) 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(lowerCAmelCase__			,     lowerCAmelCase__			,     dtype=lowerCAmelCase__		)]			,     dim=1		)

     return ordered_inputs





 @property
 def 							_snake_case   (	self		)    ->							int:
     return 13









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise ValueError("check_bouncy() accepts only integer arguments"    )
    _lowerCAmelCase				=      str(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      "".join(sorted(SCREAMING_SNAKE_CASE_    )    )
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 	__a(SCREAMING_SNAKE_CASE_  :					float = 99    ):



    '''simple docstring'''





    if not 0 < percent < 100:
        raise ValueError("solution() only accepts values from 0 to 100"    )
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      1

    while True:
        if check_bouncy(SCREAMING_SNAKE_CASE_    ):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


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

       testmod()
       print(f'''{solution(99)}''')


