




import sys
from pathlib import Path


lowerCamelCase    :		Dict			   =       Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCamelCase    :		Union[str, Any]			   =       {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowerCamelCase    :		Optional[int]			   =       "zero2"
lowerCamelCase    :		Optional[Any]			   =       "zero3"
lowerCamelCase    :		Tuple			   =       [ZEROa, ZEROa]

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[int]     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   parameterized.to_safe_name('_'.join(str(lowercase__					) for x in param.args					)					)
      return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
lowerCamelCase    :		Union[str, Any]			   =       list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class   A(						A__       ):



 '''simple docstring'''


 @parameterized.expand(__A						,							name_func=__A  )
 def     a__			(    self :  List[str]						,							A_ :  Dict						,							A_ :  List[str]  )       ->     str:

       """simple docstring"""






       self.run_and_check(
           stage=__A						,							model=__A						,							distributed=__A						,							fpaa=__A						,							)

 @require_torch_multi_gpu
 @parameterized.expand(__A						,							name_func=__A  )
 def     a__			(    self :  str						,							A_ :  Union[str, Any]						,							A_ :  str  )       ->     List[Any]:

       """simple docstring"""






       self.run_and_check(
           stage=__A						,							model=__A						,							distributed=__A						,							fpaa=__A						,							)

 @parameterized.expand(__A						,							name_func=__A  )
 def     a__			(    self :  Dict						,							A_ :  int						,							A_ :  Dict  )       ->     str:

       """simple docstring"""






       self.run_and_check(
           stage=__A						,							model=__A						,							distributed=__A						,							fpaa=__A						,							)

 @require_torch_multi_gpu
 @parameterized.expand(__A						,							name_func=__A  )
 def     a__			(    self :  Optional[int]						,							A_ :  Dict						,							A_ :  List[str]  )       ->     Tuple:

       """simple docstring"""






       self.run_and_check(
           stage=__A						,							model=__A						,							distributed=__A						,							fpaa=__A						,							)

 def     a__			(    self :  Optional[int]						,							A_ :  int  )       ->     List[str]:

       """simple docstring"""






       pass

 def     a__			(    self :  Dict						,							A_ :  Any						,							A_ :  List[str]						,							A_ :  str = 10						,							A_ :  str = True						,							A_ :  Dict = True						,							A_ :  Dict = True						,							)       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   models[model]

       lowerCamelCase_							=   self.run_trainer(
           stage=__A						,							model_name=__A						,							eval_steps=__A						,							num_train_epochs=1						,							distributed=__A						,							fpaa=__A						,							)

       self.do_checks(__A  )

       return output_dir

 def     a__			(    self :  List[Any]						,							A_ :  Dict						,							A_ :  int						,							A_ :  Union[str, Any] = 10						,							A_ :  List[str] = 1						,							A_ :  List[Any] = True						,							A_ :  Tuple = True						,							)       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   self.get_auto_remove_tmp_dir('./xxx'						,							after=__A  )
       lowerCamelCase_							=   f"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(__A  )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

       if fpaa:
             args.extend(['--fp16']  )

       # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
       # hence the separate config files
       lowerCamelCase_							=   f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
       lowerCamelCase_							=   [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
       lowerCamelCase_							=   self.get_launcher(__A  )

       lowerCamelCase_							=   launcher + script + args + ds_args
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       execute_subprocess_async(__A						,							env=self.get_env()  )

       return output_dir





 def     a__			(    self :  Union[str, Any]						,							A_ :  Tuple=False  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   min(2						,							get_gpu_count()  ) if distributed else 1
       return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1_000 / max_iters))



