






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__   :      str				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      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     lowerCamelCase_					(   lowerCamelCase			):
				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							,       __lowerCAmelCase=5_0_4_0_0							,       __lowerCAmelCase=2_0_4_8							,       __lowerCAmelCase=4_0_9_6							,       __lowerCAmelCase=2_8							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=6_4							,       __lowerCAmelCase=None							,       __lowerCAmelCase="gelu_new"							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=1E-5							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=True							,       __lowerCAmelCase=5_0_2_5_6							,       __lowerCAmelCase=5_0_2_5_6							,       __lowerCAmelCase=False							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Dict   =   vocab_size
								__magic_name__		:int   =   n_positions
								__magic_name__		:Union[str, Any]   =   n_embd
								__magic_name__		:List[Any]   =   n_layer
								__magic_name__		:Dict   =   n_head
								__magic_name__		:Optional[Any]   =   n_inner
								__magic_name__		:str   =   rotary_dim
								__magic_name__		:int   =   activation_function
								__magic_name__		:Any   =   resid_pdrop
								__magic_name__		:Optional[Any]   =   embd_pdrop
								__magic_name__		:List[Any]   =   attn_pdrop
								__magic_name__		:Dict   =   layer_norm_epsilon
								__magic_name__		:Dict   =   initializer_range
								__magic_name__		:Union[str, Any]   =   use_cache

								__magic_name__		:Tuple   =   bos_token_id
								__magic_name__		:Dict   =   eos_token_id

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







class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = "default"							,       __lowerCAmelCase = None							,       __lowerCAmelCase = False							,       ):
								"""simple docstring"""
								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?
												__magic_name__		:Dict   =   0







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}  )
								if self.use_past:
												self.fill_with_past_key_values_(__lowerCAmelCase							,       direction='''inputs'''  )
												__magic_name__		:Union[str, Any]   =   {0: '''batch''', 1: '''past_sequence + sequence'''}
								else:
												__magic_name__		:List[Any]   =   {0: '''batch''', 1: '''sequence'''}

								return common_inputs







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self._config.n_layer







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self._config.n_head







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = -1							,       __lowerCAmelCase = -1							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:Any   =   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()
								__magic_name__		:Optional[int]   =   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

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

								__magic_name__		:Union[str, Any]   =   common_inputs['''attention_mask''']
								if self.use_past:
												__magic_name__		:int   =   ordered_inputs['''attention_mask'''].dtype
												__magic_name__		:Optional[Any]   =   torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(__lowerCAmelCase							,       __lowerCAmelCase							,       dtype=__lowerCAmelCase  )]							,       dim=1  )

								return ordered_inputs







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return 1_3














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






