





"""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 		__snake_case :





 """simple docstring"""


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



     '''simple docstring'''




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

 def __call__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Tuple									=					{k: np.array(__lowerCamelCase  ) for k, v in kwargs.items()}
     return self.model.run(__lowerCamelCase ,						__lowerCamelCase  )

 @staticmethod
 def  UpperCamelCase__(   __lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase=None  ):



     '''simple docstring'''




     if provider is None:
         logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''  )
         __A   :						Optional[Any]									=					'''CPUExecutionProvider'''

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					file_name if file_name is not None else ONNX_WEIGHTS_NAME

     __A   :						str									=					self.model_save_dir.joinpath(self.latest_model_name  )
     __A   :						Dict									=					Path(__lowerCamelCase  ).joinpath(__lowerCamelCase  )
     try:
         shutil.copyfile(__lowerCamelCase ,						__lowerCamelCase  )
     except shutil.SameFileError:
         pass

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     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  UpperCamelCase__(   cls ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						int									=					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   :						int									=					Path(__lowerCamelCase  )
     # load model from hub
     else:
         # download model
         __A   :						str									=					hf_hub_download(
             repo_id=__lowerCamelCase ,						filename=__lowerCamelCase ,						use_auth_token=__lowerCamelCase ,						revision=__lowerCamelCase ,						cache_dir=__lowerCamelCase ,						force_download=__lowerCamelCase ,						)
         __A   :						str									=					Path(__lowerCamelCase  ).parent
         __A   :						Tuple									=					Path(__lowerCamelCase  ).name
         __A   :						Optional[Any]									=					OnnxRuntimeModel.load_model(__lowerCamelCase ,						provider=__lowerCamelCase ,						sess_options=__lowerCamelCase  )
     return cls(model=__lowerCamelCase ,						**__lowerCamelCase  )







 @classmethod
 def  UpperCamelCase__(   cls ,						__lowerCamelCase ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						str									=					None
     if len(str(__lowerCamelCase  ).split('''@'''  )  ) == 2:
         __A			,	__A   :						List[Any]									=					model_id.split('''@'''  )

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











"""simple docstring"""



import functools
import operator

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


a_			=      logging.get_logger(__name__)

a_			=      {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""sew-d"""

 def __init__(   self ,						__lowerCamelCase=32 ,						__lowerCamelCase=768 ,						__lowerCamelCase=12 ,						__lowerCamelCase=12 ,						__lowerCamelCase=3072 ,						__lowerCamelCase=2 ,						__lowerCamelCase=512 ,						__lowerCamelCase=256 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=("p2c", "c2p") ,						__lowerCamelCase="layer_norm" ,						__lowerCamelCase="gelu_python" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-7 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase="group" ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,						__lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,						__lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,						__lowerCamelCase=False ,						__lowerCamelCase=128 ,						__lowerCamelCase=16 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0_5 ,						__lowerCamelCase=10 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=10 ,						__lowerCamelCase=0 ,						__lowerCamelCase="mean" ,						__lowerCamelCase=False ,						__lowerCamelCase=False ,						__lowerCamelCase=256 ,						__lowerCamelCase=0 ,						__lowerCamelCase=1 ,						__lowerCamelCase=2 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase ,						pad_token_id=__lowerCamelCase ,						bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase  )
     __A   :						str									=					hidden_size
     __A   :						List[Any]									=					feat_extract_norm
     __A   :						Tuple									=					feat_extract_activation
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						int									=					list(__lowerCamelCase  )
     __A   :						List[Any]									=					list(__lowerCamelCase  )
     __A   :						Any									=					conv_bias
     __A   :						List[Any]									=					num_conv_pos_embeddings
     __A   :						Any									=					num_conv_pos_embedding_groups
     __A   :						Optional[Any]									=					len(self.conv_dim  )
     __A   :						int									=					num_hidden_layers
     __A   :						Union[str, Any]									=					intermediate_size
     __A   :						Union[str, Any]									=					squeeze_factor
     __A   :						int									=					max_position_embeddings
     __A   :						Tuple									=					position_buckets
     __A   :						Tuple									=					share_att_key
     __A   :						List[str]									=					relative_attention
     __A   :						Optional[Any]									=					norm_rel_ebd
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						str									=					hidden_act
     __A   :						List[str]									=					num_attention_heads
     __A   :						Union[str, Any]									=					hidden_dropout
     __A   :						Optional[int]									=					attention_dropout
     __A   :						Optional[Any]									=					activation_dropout
     __A   :						List[str]									=					feat_proj_dropout
     __A   :						str									=					final_dropout
     __A   :						Tuple									=					layer_norm_eps
     __A   :						int									=					feature_layer_norm_eps
     __A   :						Optional[int]									=					initializer_range
     __A   :						Dict									=					vocab_size

     if (
         (len(self.conv_stride  ) != self.num_feat_extract_layers)
         or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
         or (len(self.conv_dim  ) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect.'''
             '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
             F"""but is `len(config.conv_dim) = {len(self.conv_dim  )}`, `len(config.conv_stride)"""
             F"""= {len(self.conv_stride  )}`, `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     __A   :						int									=					apply_spec_augment
     __A   :						Any									=					mask_time_prob
     __A   :						Optional[int]									=					mask_time_length
     __A   :						Any									=					mask_time_min_masks
     __A   :						int									=					mask_feature_prob
     __A   :						Tuple									=					mask_feature_length
     __A   :						Dict									=					mask_feature_min_masks

     # ctc loss
     __A   :						Tuple									=					ctc_loss_reduction
     __A   :						Union[str, Any]									=					ctc_zero_infinity

     # sequence classification
     __A   :						Tuple									=					use_weighted_layer_sum
     __A   :						List[str]									=					classifier_proj_size







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return functools.reduce(operator.mul ,						self.conv_stride ,						1  )




