




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCAmelCase_									=	'true'




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					List[str]=82  ,			A__       :					int=16	):





       '''simple docstring'''



       set_seed(42	)
       __lowerCamelCase   =						RegressionModel()
       __lowerCamelCase   =						deepcopy(A__	)
       __lowerCamelCase   =						RegressionDataset(length=A__	)
       __lowerCamelCase   =						DataLoader(A__  ,			batch_size=A__	)
       model.to(accelerator.device	)
       __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(A__  ,			A__	)
       return model, ddp_model, dataloader




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					List[Any]=False	):





       '''simple docstring'''



       __lowerCamelCase   =						AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""	)
       __lowerCamelCase   =						load_dataset("""glue"""  ,			"""mrpc"""  ,			split="""validation"""	)

       def tokenize_function(A__       :					str	):
              __lowerCamelCase   =						tokenizer(examples["""sentence1"""]  ,			examples["""sentence2"""]  ,			truncation=A__  ,			max_length=A__	)
              return outputs

       with accelerator.main_process_first():
              __lowerCamelCase   =						dataset.map(
                  A__  ,			batched=A__  ,			remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,			)

       __lowerCamelCase   =						tokenized_datasets.rename_column("""label"""  ,			"""labels"""	)

       def collate_fn(A__       :					List[str]	):
              if use_longest:
                     return tokenizer.pad(A__  ,			padding="""longest"""  ,			return_tensors="""pt"""	)
              return tokenizer.pad(A__  ,			padding="""max_length"""  ,			max_length=128  ,			return_tensors="""pt"""	)

       return DataLoader(A__  ,			shuffle=A__  ,			collate_fn=A__  ,			batch_size=16	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					List[Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						Accelerator(dispatch_batches=A__  ,			split_batches=A__	)
       __lowerCamelCase   =						get_dataloader(A__  ,			not dispatch_batches	)
       __lowerCamelCase   =						AutoModelForSequenceClassification.from_pretrained(
           """hf-internal-testing/mrpc-bert-base-cased"""  ,			return_dict=A__	)
       __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(A__  ,			A__	)
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					List[Any]  ,			A__       :					List[str]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for batch in dataloader:
              __lowerCamelCase,   __lowerCamelCase   =						batch.values()
              with torch.no_grad():
                     __lowerCamelCase   =						model(A__	)
                     __lowerCamelCase,   __lowerCamelCase   =						accelerator.gather_for_metrics((logit, target)	)
                     logits_and_targets.append((logit, target)	)
       __lowerCamelCase,   __lowerCamelCase   =						[], []
       for logit, targ in logits_and_targets:
              logits.append(A__	)
              targs.append(A__	)
       __lowerCamelCase,   __lowerCamelCase   =						torch.cat(A__	), torch.cat(A__	)
       return logits, targs




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					List[Any]=82  ,			A__       :					List[Any]=False  ,			A__       :					str=False  ,			A__       :					Union[str, Any]=16	):





       '''simple docstring'''



       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						get_basic_setup(A__  ,			A__  ,			A__	)
       __lowerCamelCase,   __lowerCamelCase   =						generate_predictions(A__  ,			A__  ,			A__	)
       assert (
           len(A__	) == num_samples
       ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__	)}'




def 					lowerCamelCase__       (						A__       :					bool = False  ,			A__       :					bool = False	):





       '''simple docstring'''



       __lowerCamelCase   =						evaluate.load("""glue"""  ,			"""mrpc"""	)
       __lowerCamelCase,   __lowerCamelCase   =						get_mrpc_setup(A__  ,			A__	)
       # First do baseline
       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						setup["""no"""]
       model.to(A__	)
       model.eval()
       for batch in dataloader:
              batch.to(A__	)
              with torch.inference_mode():
                     __lowerCamelCase   =						model(**A__	)
              __lowerCamelCase   =						outputs.logits.argmax(dim=-1	)
              metric.add_batch(predictions=A__  ,			references=batch["""labels"""]	)
       __lowerCamelCase   =						metric.compute()

       # Then do distributed
       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						setup["""ddp"""]
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     __lowerCamelCase   =						model(**A__	)
              __lowerCamelCase   =						outputs.logits.argmax(dim=-1	)
              __lowerCamelCase   =						batch["""labels"""]
              __lowerCamelCase,   __lowerCamelCase   =						accelerator.gather_for_metrics((preds, references)	)
              metric.add_batch(predictions=A__  ,			references=A__	)
       __lowerCamelCase   =						metric.compute()

       for key in "accuracy f1".split():
              assert math.isclose(
                  baseline[key]  ,			distributed[key]	), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						Accelerator(split_batches=A__  ,			dispatch_batches=A__	)
       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_warning()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
       # These are a bit slower so they should only be ran on the GPU or TPU
       if torch.cuda.is_available() or is_tpu_available():
              if accelerator.is_local_main_process:
                     print("""**Testing gather_for_metrics**"""	)
              for split_batches in [True, False]:
                     for dispatch_batches in [True, False]:
                            if accelerator.is_local_main_process:
                                   print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'	)
                            test_mrpc(A__  ,			A__	)
                            accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print("""**Test torch metrics**"""	)
       for split_batches in [True, False]:
              for dispatch_batches in [True, False]:
                     __lowerCamelCase   =						Accelerator(split_batches=A__  ,			dispatch_batches=A__	)
                     if accelerator.is_local_main_process:
                            print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'	)
                     test_torch_metrics(A__  ,			99	)
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print("""**Test last batch is not dropped when perfectly divisible**"""	)
       __lowerCamelCase   =						Accelerator()
       test_torch_metrics(A__  ,			512	)
       accelerator.state._reset_state()




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       main()


if __name__ == "__main__":
       main()







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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    '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__:
      def __init__(				self: str    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: str   ):
             logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
             __lowerCamelCase   =						model
             __lowerCamelCase   =						kwargs.get("""model_save_dir"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						kwargs.get("""latest_model_name"""    ,       UpperCamelCase_   )
      def __call__(				self: Dict    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{k: np.array(UpperCamelCase_   ) for k, v in kwargs.items()}
             return self.model.run(UpperCamelCase_    ,       UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Tuple=None    ,       UpperCamelCase_: Tuple=None   ):
             if provider is None:
                    logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""   )
                    __lowerCamelCase   =						"""CPUExecutionProvider"""

             return ort.InferenceSession(UpperCamelCase_    ,       providers=[provider]    ,       sess_options=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

             # copy external weights (for models >2GB)
             __lowerCamelCase   =						self.model_save_dir.joinpath(UpperCamelCase_   )
             if src_path.exists():
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
                    try:
                           shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
                    except shutil.SameFileError:
                           pass
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, os.PathLike]    ,       **UpperCamelCase_: Optional[Any]    ,       ):
             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: str    ,       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_: int    ,       ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
             # load model from local directory
             if os.path.isdir(UpperCamelCase_   ):
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(
                        os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
                    __lowerCamelCase   =						Path(UpperCamelCase_   )
             # load model from hub
             else:
                    # download model
                    __lowerCamelCase   =						hf_hub_download(
                        repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       )
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).parent
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).name
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(UpperCamelCase_    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
             return cls(model=UpperCamelCase_    ,       **UpperCamelCase_   )




      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[int]    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						None
             if len(str(UpperCamelCase_   ).split("""@"""   )   ) == 2:
                    __lowerCamelCase,   __lowerCamelCase   =						model_id.split("""@"""   )

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

