




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__			:       int         =							logging.get_logger(__name__)

lowerCamelCase__			:       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 	lowerCamelCase_							(				lowerCamelCase__ ):


	'''simple docstring'''







	lowercase_       =   "gptj"
	lowercase_       =   {
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}



	def __init__(							self  :		Any    ,    _lowerCAmelCase  :		Union[str, Any]=50_400    ,    _lowerCAmelCase  :		Any=2_048    ,    _lowerCAmelCase  :		Optional[Any]=4_096    ,    _lowerCAmelCase  :		Tuple=28    ,    _lowerCAmelCase  :		List[Any]=16    ,    _lowerCAmelCase  :		Any=64    ,    _lowerCAmelCase  :		str=None    ,    _lowerCAmelCase  :		Optional[Any]="gelu_new"    ,    _lowerCAmelCase  :		Tuple=0.0    ,    _lowerCAmelCase  :		List[str]=0.0    ,    _lowerCAmelCase  :		Tuple=0.0    ,    _lowerCAmelCase  :		Optional[int]=1E-5    ,    _lowerCAmelCase  :		List[str]=0.02    ,    _lowerCAmelCase  :		int=True    ,    _lowerCAmelCase  :		Any=50_256    ,    _lowerCAmelCase  :		Optional[Any]=50_256    ,    _lowerCAmelCase  :		int=False    ,    **_lowerCAmelCase  :		Any    ,    ):
							SCREAMING_SNAKE_CASE_     						=      vocab_size
							SCREAMING_SNAKE_CASE_     						=      n_positions
							SCREAMING_SNAKE_CASE_     						=      n_embd
							SCREAMING_SNAKE_CASE_     						=      n_layer
							SCREAMING_SNAKE_CASE_     						=      n_head
							SCREAMING_SNAKE_CASE_     						=      n_inner
							SCREAMING_SNAKE_CASE_     						=      rotary_dim
							SCREAMING_SNAKE_CASE_     						=      activation_function
							SCREAMING_SNAKE_CASE_     						=      resid_pdrop
							SCREAMING_SNAKE_CASE_     						=      embd_pdrop
							SCREAMING_SNAKE_CASE_     						=      attn_pdrop
							SCREAMING_SNAKE_CASE_     						=      layer_norm_epsilon
							SCREAMING_SNAKE_CASE_     						=      initializer_range
							SCREAMING_SNAKE_CASE_     						=      use_cache

							SCREAMING_SNAKE_CASE_     						=      bos_token_id
							SCREAMING_SNAKE_CASE_     						=      eos_token_id

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





class 	lowerCamelCase_							(				lowerCamelCase__ ):


	'''simple docstring'''







	def __init__(							self  :		List[str]    ,    _lowerCAmelCase  :		PretrainedConfig    ,    _lowerCAmelCase  :		str = "default"    ,    _lowerCAmelCase  :		List[PatchingSpec] = None    ,    _lowerCAmelCase  :		bool = False    ,    ):
							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?
													SCREAMING_SNAKE_CASE_     						=      0



	@property
	def 				lowerCAmelCase_  (							self  :		List[Any]						):
							SCREAMING_SNAKE_CASE_     						=      OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
							if self.use_past:
													self.fill_with_past_key_values_(_lowerCAmelCase    ,    direction='inputs'						)
													SCREAMING_SNAKE_CASE_     						=      {0: "batch", 1: "past_sequence + sequence"}
							else:
													SCREAMING_SNAKE_CASE_     						=      {0: "batch", 1: "sequence"}

							return common_inputs



	@property
	def 				lowerCAmelCase_  (							self  :		Any						):
							return self._config.n_layer



	@property
	def 				lowerCAmelCase_  (							self  :		str						):
							return self._config.n_head



	def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		PreTrainedTokenizer    ,    _lowerCAmelCase  :		int = -1    ,    _lowerCAmelCase  :		int = -1    ,    _lowerCAmelCase  :		bool = False    ,    _lowerCAmelCase  :		Optional[TensorType] = None    ,    ):
							SCREAMING_SNAKE_CASE_     						=      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()
							SCREAMING_SNAKE_CASE_     						=      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_     						=      common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			SCREAMING_SNAKE_CASE_     						=      seqlen + 2
																			SCREAMING_SNAKE_CASE_     						=      (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			SCREAMING_SNAKE_CASE_     						=      [
																			    (torch.zeros(_lowerCAmelCase						), torch.zeros(_lowerCAmelCase						)) for _ in range(self.num_layers						)
																			]

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

							return ordered_inputs




	@property
	def 				lowerCAmelCase_  (							self  :		str						):
							return 13





from abc import ABC, abstractmethod
from argparse import ArgumentParser





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @staticmethod
 @abstractmethod
 def 				lowerCAmelCase_  (							_lowerCAmelCase  :		ArgumentParser						):
       raise NotImplementedError()




 @abstractmethod
 def 				lowerCAmelCase_  (							self  :		Dict						):
       raise NotImplementedError()