





"""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


__magic_name__       :					Any						=			logging.get_logger(__name__)

__magic_name__       :					List[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 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A=None						,   **_A    ):






									'''simple docstring'''







									logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""    )
									UpperCamelCase				:		Union[str, Any]				   =			model
									UpperCamelCase				:		List[str]				   =			kwargs.get("""model_save_dir"""						,   _UpperCAmelCase    )
									UpperCamelCase				:		List[str]				   =			kwargs.get("""latest_model_name"""						,   _UpperCAmelCase    )







			def __call__(      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			{k: np.array(_UpperCAmelCase    ) for k, v in kwargs.items()}
									return self.model.run(_UpperCAmelCase						,   _UpperCAmelCase    )







			@staticmethod
			def   _a      (      _A						,   _A=None						,   _A=None    ):






									'''simple docstring'''







									if provider is None:
															logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""    )
															UpperCamelCase				:		str				   =			"""CPUExecutionProvider"""

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







			def   _a      (      self						,   _A						,   _A = None						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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







			def   _a      (      self						,   _A						,   **_A						,   ):






									'''simple docstring'''







									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   _a      (      cls						,   _A						,   _A = None						,   _A = None						,   _A = False						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







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







			@classmethod
			def   _a      (      cls						,   _A						,   _A = True						,   _A = None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			None
									if len(str(_UpperCAmelCase    ).split("""@"""    )    ) == 2:
															UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			model_id.split("""@"""    )

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









"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[Any]     =      '''xmod'''

 def __init__(					self			:			str       ,				_UpperCAmelCase			:			Optional[Any]=30_522       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			List[str]=12       ,				_UpperCAmelCase			:			Dict=3_072       ,				_UpperCAmelCase			:			Union[str, Any]="gelu"       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[str]=512       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Optional[int]=0.02       ,				_UpperCAmelCase			:			Any=1E-1_2       ,				_UpperCAmelCase			:			Tuple=1       ,				_UpperCAmelCase			:			int=0       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			List[str]="absolute"       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			int=False       ,				_UpperCAmelCase			:			Union[str, Any]=2       ,				_UpperCAmelCase			:			List[Any]=False       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Tuple=("en_XX",)       ,				_UpperCAmelCase			:			List[str]=None       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  hidden_act
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  classifier_dropout
       _A     =  pre_norm
       _A     =  adapter_reduction_factor
       _A     =  adapter_layer_norm
       _A     =  adapter_reuse_layer_norm
       _A     =  ln_before_adapter
       _A     =  list(_UpperCAmelCase						)
       _A     =  default_language





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       if self.task == "multiple-choice":
             _A     =  {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             _A     =  {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]						)

