




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


lowerCamelCase__					:  Dict      							=				logging.get_logger(__name__)

lowerCamelCase__					:  int      							=				{
    '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 			lowerCamelCase_				:


 '''simple docstring'''







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



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



 @staticmethod
 def  lowerCAmelCase_    (				_lowerCAmelCase    :						Union[str, Path]							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Dict=None		):
       if provider is None:
             logger.info('No onnxruntime provider specified, using CPUExecutionProvider'		)
             SCREAMING_SNAKE_CASE_      		=     'CPUExecutionProvider'

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



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Union[str, Path]							,							_lowerCAmelCase    :						Optional[str] = None							,							**_lowerCAmelCase    :						str		):
       SCREAMING_SNAKE_CASE_      		=     file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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



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




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

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





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

      SCREAMING_SNAKE_CASE_      		=     AutoTokenizer.from_pretrained('bert-base-cased'				)
      SCREAMING_SNAKE_CASE_      		=     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      SCREAMING_SNAKE_CASE_      		=     load_dataset('csv'     ,     data_files=__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     datasets['train'].unique('label'				)

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

      def tokenize_function(__UpperCAmelCase		:      Optional[int]				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_      		=     tokenizer(
                examples['sentence1']     ,     examples['sentence2']     ,     truncation=__UpperCAmelCase     ,     max_length=__UpperCAmelCase     ,     padding='max_length'				)
            if "label" in examples:
                  SCREAMING_SNAKE_CASE_      		=     [label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE_      		=     datasets.map(
          __UpperCAmelCase     ,     batched=__UpperCAmelCase     ,     remove_columns=['sentence1', 'sentence2', 'label']     ,     )

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__UpperCAmelCase     ,     padding='max_length'     ,     max_length=1_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader