





"""simple docstring"""




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		:				Union[str, Any]  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         ="""gptj"""
							lowerCamelCase     :      Dict         ={
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(     self   ,      lowerCAmelCase__=5_0400   ,      lowerCAmelCase__=2048   ,      lowerCAmelCase__=4096   ,      lowerCAmelCase__=28   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__="gelu_new"   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=5_0256   ,      lowerCAmelCase__=5_0256   ,      lowerCAmelCase__=False   ,      **lowerCAmelCase__   ,      )			->				Dict:
								a   :	int			=       vocab_size
								a   :	Tuple			=       n_positions
								a   :	List[Any]			=       n_embd
								a   :	int			=       n_layer
								a   :	Union[str, Any]			=       n_head
								a   :	Optional[int]			=       n_inner
								a   :	Any			=       rotary_dim
								a   :	Any			=       activation_function
								a   :	Optional[int]			=       resid_pdrop
								a   :	Optional[int]			=       embd_pdrop
								a   :	int			=       attn_pdrop
								a   :	Optional[Any]			=       layer_norm_epsilon
								a   :	Optional[int]			=       initializer_range
								a   :	Union[str, Any]			=       use_cache

								a   :	Union[str, Any]			=       bos_token_id
								a   :	Dict			=       eos_token_id

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


class  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = "default"   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      )			->				Dict:
								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?
									a   :	Optional[Any]			=       0






							@property
							def 					__a     (     self   )			->				Mapping[str, Mapping[int, str]]:
								a   :	Union[str, Any]			=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}   )
								if self.use_past:
									self.fill_with_past_key_values_(lowerCAmelCase__   ,      direction="inputs"   )
									a   :	Tuple			=       {0: "batch", 1: "past_sequence + sequence"}
								else:
									a   :	List[Any]			=       {0: "batch", 1: "sequence"}

								return common_inputs






							@property
							def 					__a     (     self   )			->				int:
								return self._config.n_layer






							@property
							def 					__a     (     self   )			->				int:
								return self._config.n_head






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

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

								a   :	List[str]			=       common_inputs["attention_mask"]
								if self.use_past:
									a   :	List[str]			=       ordered_inputs["attention_mask"].dtype
									a   :	Union[str, Any]			=       torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase__   ,      lowerCAmelCase__   ,      dtype=lowerCAmelCase__   )]   ,      dim=1   )

								return ordered_inputs






							@property
							def 					__a     (     self   )			->				int:
								return 13









"""simple docstring"""




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a, a   :	Tuple			=       model_id.split("@"   )

								return cls._from_pretrained(
								    model_id=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )


