





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					(    a__    ):
						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				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]=5_04_00							,							__lowerCamelCase				:      Dict=20_48							,							__lowerCamelCase				:      str=40_96							,							__lowerCamelCase				:      Any=28							,							__lowerCamelCase				:      List[Any]=16							,							__lowerCamelCase				:      Dict=64							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      int="gelu_new"							,							__lowerCamelCase				:      str=0.0							,							__lowerCamelCase				:      Optional[int]=0.0							,							__lowerCamelCase				:      Optional[Any]=0.0							,							__lowerCamelCase				:      Optional[Any]=1E-5							,							__lowerCamelCase				:      Dict=0.02							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      Dict=5_02_56							,							__lowerCamelCase				:      Optional[Any]=5_02_56							,							__lowerCamelCase				:      Union[str, Any]=False							,							**__lowerCamelCase				:      List[str]							,							)							->		Dict:


									'''simple docstring'''

									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=lowerCAmelCase__							,							eos_token_id=lowerCAmelCase__							,							tie_word_embeddings=lowerCAmelCase__							,							**lowerCAmelCase__   )






class      __lowercase					(    a__    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[str] = "default"							,							__lowerCamelCase				:      Tuple = None							,							__lowerCamelCase				:      Optional[int] = False							,							)							->		Dict:


									'''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?
												lowercase							=		0

						@property
						def        __a		(	self				:      Tuple   )							->		Mapping[str, Mapping[int, str]]:


									'''simple docstring'''

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

									return common_inputs

						@property
						def        __a		(	self				:      str   )							->		int:


									'''simple docstring'''

									return self._config.n_layer

						@property
						def        __a		(	self				:      List[str]   )							->		int:


									'''simple docstring'''

									return self._config.n_head

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Tuple = -1							,							__lowerCamelCase				:      Optional[Any] = -1							,							__lowerCamelCase				:      Dict = False							,							__lowerCamelCase				:      List[Any] = None							,							)							->		Mapping[str, Any]:


									'''simple docstring'''

									lowercase							=		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()
									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							=		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(lowerCAmelCase__   ), torch.zeros(lowerCAmelCase__   )) 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(lowerCAmelCase__							,							lowerCAmelCase__							,							dtype=lowerCAmelCase__   )]							,							dim=1   )

									return ordered_inputs



						@property
						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return 13






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_      =      {
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPTBigCodeForSequenceClassification",
						    "GPTBigCodeForTokenClassification",
						    "GPTBigCodeForCausalLM",
						    "GPTBigCodeModel",
						    "GPTBigCodePreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_bigcode import (
												    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTBigCodeForCausalLM,
												    GPTBigCodeForSequenceClassification,
												    GPTBigCodeForTokenClassification,
												    GPTBigCodeModel,
												    GPTBigCodePreTrainedModel,
												)


else:
						import sys

						A_      =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





