




"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''nllb-moe'''
 UpperCAmelCase       :	Optional[int]     =      ['''past_key_values''']
 UpperCAmelCase       :	Union[str, Any]     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			Union[str, Any]=128_112       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			List[str]=12       ,				_UpperCAmelCase			:			Union[str, Any]=4_096       ,				_UpperCAmelCase			:			Tuple=16       ,				_UpperCAmelCase			:			Union[str, Any]=12       ,				_UpperCAmelCase			:			Dict=4_096       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			Any=0.05       ,				_UpperCAmelCase			:			List[str]=0.05       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			Any="relu"       ,				_UpperCAmelCase			:			Tuple=1_024       ,				_UpperCAmelCase			:			Dict=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[str]=0.0       ,				_UpperCAmelCase			:			Tuple=0.02       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Tuple=False       ,				_UpperCAmelCase			:			Dict="float32"       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Optional[Any]=128       ,				_UpperCAmelCase			:			List[Any]=64       ,				_UpperCAmelCase			:			Union[str, Any]=4       ,				_UpperCAmelCase			:			Tuple=4       ,				_UpperCAmelCase			:			Tuple=0.001       ,				_UpperCAmelCase			:			Union[str, Any]=0.001       ,				_UpperCAmelCase			:			Dict="all"       ,				_UpperCAmelCase			:			Optional[Any]=False       ,				_UpperCAmelCase			:			Tuple=False       ,				_UpperCAmelCase			:			Optional[Any]=1.0       ,				_UpperCAmelCase			:			int=0.2       ,				_UpperCAmelCase			:			Optional[Any]=1       ,				_UpperCAmelCase			:			str=0       ,				_UpperCAmelCase			:			List[str]=2       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				**_UpperCAmelCase			:			List[Any]       ,				):
       _A     =  vocab_size
       _A     =  max_position_embeddings
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  router_z_loss_coef
       _A     =  router_aux_loss_coef
       _A     =  decoder_sparse_step
       _A     =  encoder_sparse_step
       _A     =  num_experts
       _A     =  expert_capacity
       _A     =  router_bias
       if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''						)
       _A     =  router_dtype

       _A     =  router_ignore_padding_tokens
       _A     =  batch_prioritized_routing
       _A     =  second_expert_policy
       _A     =  normalize_router_prob_before_dropping
       _A     =  moe_eval_capacity_token_fraction
       _A     =  moe_token_dropout
       _A     =  output_router_logits
       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)







"""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							      =     logging.get_logger(__name__)

a							      =     {
    '''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 							lowercase_			:



 '''simple docstring'''

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

 def __call__(					self			:			Dict       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  {k: np.array(_UpperCAmelCase						) for k, v in kwargs.items()}
       return self.model.run(_UpperCAmelCase       ,				_UpperCAmelCase						)

 @staticmethod
 def      lowerCAmelCase_				(					_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[Any]=None						):
       if provider is None:
             logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
             _A     =  'CPUExecutionProvider'

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

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

       _A     =  self.model_save_dir.joinpath(self.latest_model_name						)
       _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
       try:
             shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
       except shutil.SameFileError:
             pass

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

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, os.PathLike]       ,				**_UpperCAmelCase			:			List[str]       ,				):
       if os.path.isfile(_UpperCAmelCase						):
             logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
             return

       os.makedirs(_UpperCAmelCase       ,				exist_ok=_UpperCAmelCase						)

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

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



 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  None
       if len(str(_UpperCAmelCase						).split('@'						)						) == 2:
             _A  ,       _A     =  model_id.split('@'						)

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

