'''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   :	Tuple 							=   logging.get_logger(__name__)

_a   :	Optional[Any] 							=   {
    "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       :






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






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






       @staticmethod
       def 			_snake_case						(      UpperCamelCase_				,     UpperCamelCase_=None				,     UpperCamelCase_=None      ):
              if provider is None:
                     logger.info("No onnxruntime provider specified, using CPUExecutionProvider"      )
                     __UpperCAmelCase  :				Dict       				=		"CPUExecutionProvider"

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     **UpperCamelCase_				,     ):
              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 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __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  :				Optional[int]       				=		OnnxRuntimeModel.load_model(
                         os.path.join(UpperCamelCase_				,     UpperCamelCase_      )				,     provider=UpperCamelCase_				,     sess_options=UpperCamelCase_      )
                     __UpperCAmelCase  :				Tuple       				=		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  :				Optional[Any]       				=		Path(UpperCamelCase_      ).parent
                     __UpperCAmelCase  :				int       				=		Path(UpperCamelCase_      ).name
                     __UpperCAmelCase  :				Optional[Any]       				=		OnnxRuntimeModel.load_model(UpperCamelCase_				,     provider=UpperCamelCase_				,     sess_options=UpperCamelCase_      )
              return cls(model=UpperCamelCase_				,     **UpperCamelCase_      )







       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Dict       				=		None
              if len(str(UpperCamelCase_      ).split("@"      )      ) == 2:
                     __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	int 							=   {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					"speech_to_text_2"
       snake_case       :List[Any]				    =					["past_key_values"]
       snake_case       :str				    =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






       def __init__(      self				,     UpperCamelCase_=1_00_00				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=4				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_=10_24				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Any       				=		vocab_size
              __UpperCAmelCase  :				Optional[int]       				=		d_model
              __UpperCAmelCase  :				Tuple       				=		decoder_ffn_dim
              __UpperCAmelCase  :				List[str]       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Dict       				=		dropout
              __UpperCAmelCase  :				Optional[Any]       				=		attention_dropout
              __UpperCAmelCase  :				int       				=		activation_dropout
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Tuple       				=		init_std
              __UpperCAmelCase  :				Any       				=		decoder_layerdrop
              __UpperCAmelCase  :				str       				=		use_cache
              __UpperCAmelCase  :				int       				=		decoder_layers
              __UpperCAmelCase  :				Any       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
              __UpperCAmelCase  :				Union[str, Any]       				=		max_target_positions

              super().__init__(
                  pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     decoder_start_token_id=UpperCamelCase_				,     **UpperCamelCase_				,     )






