
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


__a  			=					logging.get_logger(__name__)

__a  			=					{
    '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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:int         =	'gptj'
	a			:List[Any]         =	{
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}
	def __init__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=5_0_4_0_0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=2_0_4_8       ,							SCREAMING_SNAKE_CASE_  :			Any=4_0_9_6       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2_8       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_6       ,							SCREAMING_SNAKE_CASE_  :			int=6_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=None       ,							SCREAMING_SNAKE_CASE_  :			str="gelu_new"       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.0       ,							SCREAMING_SNAKE_CASE_  :			Any=0.0       ,							SCREAMING_SNAKE_CASE_  :			str=0.0       ,							SCREAMING_SNAKE_CASE_  :			str=1e-5       ,							SCREAMING_SNAKE_CASE_  :			Any=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			str=5_0_2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=5_0_2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							**SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							)       ->     Union[str, Any]:
					lowercase_       =					vocab_size
					lowercase_       =					n_positions
					lowercase_       =					n_embd
					lowercase_       =					n_layer
					lowercase_       =					n_head
					lowercase_       =					n_inner
					lowercase_       =					rotary_dim
					lowercase_       =					activation_function
					lowercase_       =					resid_pdrop
					lowercase_       =					embd_pdrop
					lowercase_       =					attn_pdrop
					lowercase_       =					layer_norm_epsilon
					lowercase_       =					initializer_range
					lowercase_       =					use_cache

					lowercase_       =					bos_token_id
					lowercase_       =					eos_token_id

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

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			PretrainedConfig       ,							SCREAMING_SNAKE_CASE_  :			str = "default"       ,							SCREAMING_SNAKE_CASE_  :			List[PatchingSpec] = None       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							)       ->     Union[str, Any]:
					super().__init__(SCREAMING_SNAKE_CASE_       ,							task=SCREAMING_SNAKE_CASE_       ,							patching_specs=SCREAMING_SNAKE_CASE_       ,							use_past=SCREAMING_SNAKE_CASE_       )
					if not getattr(self._config       ,							'''pad_token_id'''       ,							SCREAMING_SNAKE_CASE_       ):
									# TODO: how to do that better?
									lowercase_       =					0
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     Mapping[str, Mapping[int, str]]:
					lowercase_       =					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
					if self.use_past:
									self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_       ,							direction='''inputs'''       )
									lowercase_       =					{0: '''batch''', 1: '''past_sequence + sequence'''}
					else:
									lowercase_       =					{0: '''batch''', 1: '''sequence'''}

					return common_inputs
	@property
	def 						_lowercase  (       self  :			str       )       ->     int:
					return self._config.n_layer
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     int:
					return self._config.n_head
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			PreTrainedTokenizer       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional[TensorType] = None       ,							)       ->     Mapping[str, Any]:
					lowercase_       =					super(SCREAMING_SNAKE_CASE_       ,							self       ).generate_dummy_inputs(
					    SCREAMING_SNAKE_CASE_       ,							batch_size=SCREAMING_SNAKE_CASE_       ,							seq_length=SCREAMING_SNAKE_CASE_       ,							is_pair=SCREAMING_SNAKE_CASE_       ,							framework=SCREAMING_SNAKE_CASE_       )

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

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

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

					return ordered_inputs




	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     int:
					return 1_3






import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




