

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


A_	:			str  	=      logging.get_logger(__name__)

A_	:			List[Any]  	=      {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       int  							=	'transfo-xl'
					UpperCAmelCase__:       Dict  							=	['mems']
					UpperCAmelCase__:       Union[str, Any]  							=	{
					    'n_token': 'vocab_size',
					    'hidden_size': 'd_model',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}






					def __init__(			self   ,	A__=26_7735   ,	A__=[2_0000, 4_0000, 20_0000]   ,	A__=1024   ,	A__=1024   ,	A__=16   ,	A__=64   ,	A__=4096   ,	A__=4   ,	A__=False   ,	A__=18   ,	A__=1600   ,	A__=1000   ,	A__=True   ,	A__=True   ,	A__=0   ,	A__=-1   ,	A__=True   ,	A__=0.1   ,	A__=0.0   ,	A__=True   ,	A__="normal"   ,	A__=0.0_1   ,	A__=0.0_1   ,	A__=0.0_2   ,	A__=1e-5   ,	A__=0   ,	**A__   ,	):
						A__							:   Dict					       =		vocab_size
						A__							:   List[str]					       =		[]
						self.cutoffs.extend(__lowerCAmelCase      )
						if proj_share_all_but_first:
							A__							:   Any					       =		[False] + [True] * len(self.cutoffs      )
						else:
							A__							:   Optional[Any]					       =		[False] + [False] * len(self.cutoffs      )
						A__							:   List[str]					       =		d_model
						A__							:   int					       =		d_embed
						A__							:   List[Any]					       =		d_head
						A__							:   str					       =		d_inner
						A__							:   List[str]					       =		div_val
						A__							:   str					       =		pre_lnorm
						A__							:   Dict					       =		n_layer
						A__							:   Union[str, Any]					       =		n_head
						A__							:   Any					       =		mem_len
						A__							:   Optional[int]					       =		same_length
						A__							:   Union[str, Any]					       =		attn_type
						A__							:   List[Any]					       =		clamp_len
						A__							:   Optional[int]					       =		sample_softmax
						A__							:   Optional[int]					       =		adaptive
						A__							:   str					       =		dropout
						A__							:   Union[str, Any]					       =		dropatt
						A__							:   Optional[int]					       =		untie_r
						A__							:   Optional[Any]					       =		init
						A__							:   List[str]					       =		init_range
						A__							:   Optional[Any]					       =		proj_init_std
						A__							:   Optional[int]					       =		init_std
						A__							:   List[Any]					       =		layer_norm_epsilon
						super().__init__(eos_token_id=__lowerCAmelCase   ,	**__lowerCAmelCase      )






					@property
					def   __A      (			self      ):
						logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""      )
						return -1






					@max_position_embeddings.setter
					def   __A      (			self   ,	A__      ):
						raise NotImplementedError(
						    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""      )






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

A_	:			str  	=      {
    '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    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__=None   ,	**A__      ):
						logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""      )
						A__							:   Dict					       =		model
						A__							:   Any					       =		kwargs.get("""model_save_dir"""   ,	A__      )
						A__							:   Optional[int]					       =		kwargs.get("""latest_model_name"""   ,	A__      )






					def __call__(			self   ,	**A__      ):
						A__							:   int					       =		{k: np.array(A__      ) for k, v in kwargs.items()}
						return self.model.run(A__   ,	A__      )






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

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






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

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






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






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

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



