


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__    :str       =				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 (							UpperCAmelCase     ):
		_a      :    Optional[Any]           =      'gptj'
		_a      :    List[str]           =      {
		    'max_position_embeddings': 'n_positions',
		    'hidden_size': 'n_embd',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50400     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=4096     ,       _SCREAMING_SNAKE_CASE=28     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=64     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="gelu_new"     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=1e-5     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=50256     ,       _SCREAMING_SNAKE_CASE=50256     ,       _SCREAMING_SNAKE_CASE=False     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     n_positions
									_UpperCAmelCase											=     n_embd
									_UpperCAmelCase											=     n_layer
									_UpperCAmelCase											=     n_head
									_UpperCAmelCase											=     n_inner
									_UpperCAmelCase											=     rotary_dim
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     resid_pdrop
									_UpperCAmelCase											=     embd_pdrop
									_UpperCAmelCase											=     attn_pdrop
									_UpperCAmelCase											=     layer_norm_epsilon
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     use_cache

									_UpperCAmelCase											=     bos_token_id
									_UpperCAmelCase											=     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 						__a (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = "default"     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False     ,       )					->			List[str]:




									"""simple docstring"""





									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?
																_UpperCAmelCase											=     0
		@property
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}		)
									if self.use_past:
																self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE     ,       direction='inputs'		)
																_UpperCAmelCase											=     {0: "batch", 1: "past_sequence + sequence"}
									else:
																_UpperCAmelCase											=     {0: "batch", 1: "sequence"}

									return common_inputs
		@property
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									return self._config.n_layer
		@property
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									return self._config.n_head
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     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()
									_UpperCAmelCase											=     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

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

									_UpperCAmelCase											=     common_inputs["attention_mask"]
									if self.use_past:
																_UpperCAmelCase											=     ordered_inputs["attention_mask"].dtype
																_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									return 13



"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'dpr'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=2			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case = 0			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(pad_token_id=snake_case			,					**snake_case   )

        lowerCAmelCase__   :				Dict 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				num_hidden_layers
        lowerCAmelCase__   :				List[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Any 			=				hidden_act
        lowerCAmelCase__   :				Any 			=				intermediate_size
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[str] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				max_position_embeddings
        lowerCAmelCase__   :				Optional[Any] 			=				type_vocab_size
        lowerCAmelCase__   :				Tuple 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps
        lowerCAmelCase__   :				Dict 			=				projection_dim
        lowerCAmelCase__   :				Any 			=				position_embedding_type






