



"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCamelCase_				)
class    SCREAMING_SNAKE_CASE__						(      UpperCamelCase_				):
   __lowerCAmelCase						:     str		=		field(default='question-answering-extractive'   ,    metadata={'include_in_asdict_even_if_is_default': True}				)
   __lowerCAmelCase						:     ClassVar[Features]		=		Features({'question': Value('string'				), 'context': Value('string'				)}				)
   __lowerCAmelCase						:     ClassVar[Features]		=		Features(
       {
           'answers': Sequence(
               {
                   'text': Value('string'				),
                   'answer_start': Value('int32'				),
               }				)
       }				)
   __lowerCAmelCase						:     str		=		"question"
   __lowerCAmelCase						:     str		=		"context"
   __lowerCAmelCase						:     str		=		"answers"

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict[str, str]:





    '''simple docstring'''




    return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}





"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
      from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    for model_result in results.values():
     for batch_size, sequence_length in zip(model_result["""bs"""]   ,  model_result["""ss"""]		):
      UpperCAmelCase			:      List[str]			=			model_result["""result"""][batch_size][sequence_length]
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Tuple			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sgugger/tiny-distilbert-classification"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  only_pretrain_model=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  torchscript=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   @unittest.skipIf(torch_device == """cpu"""   ,  """Cant do half precision"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[int]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  fpaa=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    # set architectures equal to `None`
    UpperCAmelCase			:      str			=			None
    UpperCAmelCase			:      Tuple			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      Dict			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   @unittest.skipIf(torch_device == """cpu"""   ,  """Can't do half precision"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  fpaa=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      int			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      str			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      List[str]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      Optional[int]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Optional[Any]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      int			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      str			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			"""sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  save_to_csv=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_time.csv"""		)   ,  train_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_mem.csv"""		)   ,  inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_mem.csv"""		)   ,  train_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_time.csv"""		)   ,  env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """env.csv"""		)   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
     UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     benchmark.run()
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_time.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_time.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_mem.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_mem.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """env.csv"""		)		).exists()		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE		):
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """sequential"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """cumulative"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """current"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """total"""		)		)

    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  log_filename=os.path.join(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)   ,  log_print=_SCREAMING_SNAKE_CASE   ,  trace_memory_line_by_line=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
     UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			benchmark.run()
     _check_summary_is_not_empty(result.inference_summary		)
     _check_summary_is_not_empty(result.train_summary		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)		).exists()		)
