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


__lowerCamelCase					:	Optional[Any]       =  logging.get_logger(__name__)

__lowerCamelCase					:	List[str]       =  {
    '''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__						(					snake_case_					):
					A	       =							'gptj'
					A	       =							{
					    'max_position_embeddings': 'n_positions',
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}

					def __init__(    self      :       str,_A      :       List[str]=5_0400,_A      :       str=2048,_A      :       int=4096,_A      :       str=28,_A      :       int=16,_A      :       List[str]=64,_A      :       Any=None,_A      :       Tuple="gelu_new",_A      :       str=0.0,_A      :       List[Any]=0.0,_A      :       List[str]=0.0,_A      :       Union[str, Any]=1E-5,_A      :       List[Any]=0.02,_A      :       Tuple=True,_A      :       Tuple=5_0256,_A      :       List[Any]=5_0256,_A      :       List[Any]=False,**_A      :       Optional[int],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		int				      =    n_positions
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    n_embd
							SCREAMING_SNAKE_CASE_  :		Dict				      =    n_layer
							SCREAMING_SNAKE_CASE_  :		Dict				      =    n_head
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    n_inner
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    rotary_dim
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    activation_function
							SCREAMING_SNAKE_CASE_  :		Dict				      =    resid_pdrop
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    embd_pdrop
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    attn_pdrop
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    layer_norm_epsilon
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		int				      =    use_cache

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    bos_token_id
							SCREAMING_SNAKE_CASE_  :		Dict				      =    eos_token_id

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







class 		a__						(					snake_case_					):

					def __init__(    self      :       int,_A      :       PretrainedConfig,_A      :       str = "default",_A      :       List[PatchingSpec] = None,_A      :       bool = False,):







							"""simple docstring"""






							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?
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    0

					@property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
							if self.use_past:
									self.fill_with_past_key_values_(_A,direction="inputs"    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {0: 'batch', 1: 'past_sequence + sequence'}
							else:
									SCREAMING_SNAKE_CASE_  :		int				      =    {0: 'batch', 1: 'sequence'}

							return common_inputs

					@property
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							return self._config.n_layer

					@property
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							return self._config.n_head

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       PreTrainedTokenizer,_A      :       int = -1,_A      :       int = -1,_A      :       bool = False,_A      :       Optional[TensorType] = None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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()
							SCREAMING_SNAKE_CASE_  :		Any				      =    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

											SCREAMING_SNAKE_CASE_  :		Tuple				      =    common_inputs['input_ids'].shape
											# Not using the same length for past_key_values
											SCREAMING_SNAKE_CASE_  :		Any				      =    seqlen + 2
											SCREAMING_SNAKE_CASE_  :		Any				      =    (
											    batch,
											    self.num_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // self.num_attention_heads,
											)
											SCREAMING_SNAKE_CASE_  :		Dict				      =    [
											    (torch.zeros(_A    ), torch.zeros(_A    )) for _ in range(self.num_layers    )
											]

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    common_inputs['attention_mask']
							if self.use_past:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    ordered_inputs['attention_mask'].dtype
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(_A,_A,dtype=_A    )],dim=1    )

							return ordered_inputs






					@property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							return 13

from __future__ import annotations





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]	):  # noqa: E741
 while r - l > 1:
  UpperCamelCase_					:						Union[str, Any] =					(l + r) // 2
  if v[m] >= key:
   UpperCamelCase_					:						str =					m
  else:
   UpperCamelCase_					:						List[Any] =					m  # noqa: E741
 return r





def    __lowercase     (						lowerCamelCase					:     list[int]	):
 if len(lowerCamelCase	) == 0:
  return 0

 UpperCamelCase_					:						Tuple =					[0] * len(lowerCamelCase	)
 UpperCamelCase_					:						int =					1

 UpperCamelCase_					:						Dict =					v[0]

 for i in range(1  ,  len(lowerCamelCase	)	):
  if v[i] < tail[0]:
   UpperCamelCase_					:						Any =					v[i]
  elif v[i] > tail[length - 1]:
   UpperCamelCase_					:						Dict =					v[i]
   length += 1
  else:
   UpperCamelCase_					:						List[str] =					v[i]

 return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()



