



"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	str=True    ,	UpperCamelCase		:	str="pt"		):
 UpperCAmelCase			:      List[str]			=			{"""add_prefix_space""": True} if isinstance(UpperCamelCase    ,	UpperCamelCase		) and not line.startswith(""" """		) else {}
 UpperCAmelCase			:      Optional[int]			=			padding_side
 return tokenizer(
     [line]    ,	max_length=UpperCamelCase    ,	padding="""max_length""" if pad_to_max_length else None    ,	truncation=UpperCamelCase    ,	return_tensors=UpperCamelCase    ,	add_special_tokens=UpperCamelCase    ,	**UpperCamelCase    ,	)

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	List[str]=None    ,	):
 UpperCAmelCase			:      Optional[Any]			=			input_ids.ne(UpperCamelCase		).any(dim=0		)
 if attention_mask is None:
  return input_ids[:, keep_column_mask]
 else:
  return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="train"   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=""   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      int			=			Path(_SCREAMING_SNAKE_CASE		).joinpath(type_path + """.source"""		)
    UpperCAmelCase			:      Tuple			=			Path(_SCREAMING_SNAKE_CASE		).joinpath(type_path + """.target"""		)
    UpperCAmelCase			:      Optional[int]			=			self.get_char_lens(self.src_file		)
    UpperCAmelCase			:      Tuple			=			max_source_length
    UpperCAmelCase			:      int			=			max_target_length
    assert min(self.src_lens		) > 0, F"found empty line in {self.src_file}"
    UpperCAmelCase			:      Any			=			tokenizer
    UpperCAmelCase			:      Dict			=			prefix
    if n_obs is not None:
     UpperCAmelCase			:      List[Any]			=			self.src_lens[:n_obs]
    UpperCAmelCase			:      Tuple			=			src_lang
    UpperCAmelCase			:      Any			=			tgt_lang

   def __len__(     self		)						->   str:





    '''simple docstring'''




    return len(self.src_lens		)

   def __getitem__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict[str, torch.Tensor]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			index + 1  # linecache starts at 1
    UpperCAmelCase			:      List[Any]			=			self.prefix + linecache.getline(str(self.src_file		)   ,  _SCREAMING_SNAKE_CASE		).rstrip("""\n"""		)
    UpperCAmelCase			:      Any			=			linecache.getline(str(self.tgt_file		)   ,  _SCREAMING_SNAKE_CASE		).rstrip("""\n"""		)
    assert source_line, F"empty source line for index {index}"
    assert tgt_line, F"empty tgt line for index {index}"

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer   ,  _SCREAMING_SNAKE_CASE		):
     source_line += self.tokenizer.eos_token
     tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    UpperCAmelCase			:      List[Any]			=			(
        self.tokenizer.question_encoder if isinstance(self.tokenizer   ,  _SCREAMING_SNAKE_CASE		) else self.tokenizer
    )
    UpperCAmelCase			:      List[Any]			=			self.tokenizer.generator if isinstance(self.tokenizer   ,  _SCREAMING_SNAKE_CASE		) else self.tokenizer

    UpperCAmelCase			:      int			=			encode_line(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  self.max_source_length   ,  """right"""		)
    UpperCAmelCase			:      List[str]			=			encode_line(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  self.max_target_length   ,  """right"""		)

    UpperCAmelCase			:      Tuple			=			source_inputs["""input_ids"""].squeeze()
    UpperCAmelCase			:      Any			=			target_inputs["""input_ids"""].squeeze()
    UpperCAmelCase			:      int			=			source_inputs["""attention_mask"""].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }

   @staticmethod
   def 			SCREAMING_SNAKE_CASE  (     _SCREAMING_SNAKE_CASE		)						->   Optional[int]:





    '''simple docstring'''




    return [len(_SCREAMING_SNAKE_CASE		) for x in Path(_SCREAMING_SNAKE_CASE		).open().readlines()]




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict[str, torch.Tensor]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			torch.stack([x["""input_ids"""] for x in batch]		)
    UpperCAmelCase			:      str			=			torch.stack([x["""attention_mask"""] for x in batch]		)
    UpperCAmelCase			:      Union[str, Any]			=			torch.stack([x["""decoder_input_ids"""] for x in batch]		)
    UpperCAmelCase			:      Optional[int]			=			(
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer   ,  _SCREAMING_SNAKE_CASE		)
        else self.tokenizer.pad_token_id
    )
    UpperCAmelCase			:      Optional[int]			=			(
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer   ,  _SCREAMING_SNAKE_CASE		)
        else self.tokenizer.pad_token_id
    )
    UpperCAmelCase			:      Any			=			trim_batch(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			trim_batch(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			{
        """input_ids""": source_ids,
        """attention_mask""": source_mask,
        """decoder_input_ids""": y,
    }
    return batch
A:     Dict										=	getLogger(__name__)

def    _snake_case		(		UpperCamelCase		:	List[List]		):
 return list(itertools.chain.from_iterable(UpperCamelCase		)		)

def    _snake_case		(		UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[Any]			=			get_git_info()
 save_json(UpperCamelCase    ,	os.path.join(UpperCamelCase    ,	"""git_log.json"""		)		)

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Dict    ,	UpperCamelCase		:	Optional[int]=4    ,	**UpperCamelCase		:	List[Any]		):
 with open(UpperCamelCase    ,	"""w"""		) as f:
  json.dump(UpperCamelCase    ,	UpperCamelCase    ,	indent=UpperCamelCase    ,	**UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	str		):
 with open(UpperCamelCase		) as f:
  return json.load(UpperCamelCase		)

def    _snake_case		(		):
 UpperCAmelCase			:      Optional[Any]			=			git.Repo(search_parent_directories=UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			{
     """repo_id""": str(UpperCamelCase		),
     """repo_sha""": str(repo.head.object.hexsha		),
     """repo_branch""": str(repo.active_branch		),
     """hostname""": str(socket.gethostname()		),
 }
 return repo_infos

def    _snake_case		(		UpperCamelCase		:	Callable    ,	UpperCamelCase		:	Iterable		):
 return list(map(UpperCamelCase    ,	UpperCamelCase		)		)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	int		):
 with open(UpperCamelCase    ,	"""wb"""		) as f:
  return pickle.dump(UpperCamelCase    ,	UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Optional[Any]		):

 def remove_articles(UpperCamelCase		:	Any		):
  return re.sub(R"""\b(a|an|the)\b"""    ,	""" """    ,	UpperCamelCase		)

 def white_space_fix(UpperCamelCase		:	List[Any]		):
  return " ".join(text.split()		)

 def remove_punc(UpperCamelCase		:	Dict		):
  UpperCAmelCase			:      Optional[Any]			=			set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(UpperCamelCase		:	Dict		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase		)		)		)		)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	List[Any]		):
 UpperCAmelCase			:      str			=			normalize_answer(UpperCamelCase		).split()
 UpperCAmelCase			:      Optional[int]			=			normalize_answer(UpperCamelCase		).split()
 UpperCAmelCase			:      Optional[Any]			=			Counter(UpperCamelCase		) & Counter(UpperCamelCase		)
 UpperCAmelCase			:      Optional[int]			=			sum(common.values()		)
 if num_same == 0:
  return 0
 UpperCAmelCase			:      Any			=			1.0 * num_same / len(UpperCamelCase		)
 UpperCAmelCase			:      Tuple			=			1.0 * num_same / len(UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			(2 * precision * recall) / (precision + recall)
 return fa

def    _snake_case		(		UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	List[str]		):
 return normalize_answer(UpperCamelCase		) == normalize_answer(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	List[str]		):
 assert len(UpperCamelCase		) == len(UpperCamelCase		)
 UpperCAmelCase			:      Any			=			0
 for hypo, pred in zip(UpperCamelCase    ,	UpperCamelCase		):
  em += exact_match_score(UpperCamelCase    ,	UpperCamelCase		)
 if len(UpperCamelCase		) > 0:
  em /= len(UpperCamelCase		)
 return {"em": em}

def    _snake_case		(		UpperCamelCase		:	int		):
 return model_prefix.startswith("""rag"""		)

def    _snake_case		(		UpperCamelCase		:	Any    ,	UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	List[str]		):
 UpperCAmelCase			:      int			=			{p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 UpperCAmelCase			:      int			=			"""dropout_rate"""
 for p in extra_params:
  if getattr(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		):
   if not hasattr(UpperCamelCase    ,	UpperCamelCase		) and not hasattr(UpperCamelCase    ,	equivalent_param[p]		):
    logger.info("""config doesn't have a `{}` attribute""".format(UpperCamelCase		)		)
    delattr(UpperCamelCase    ,	UpperCamelCase		)
    continue
   UpperCAmelCase			:      List[str]			=			p if hasattr(UpperCamelCase    ,	UpperCamelCase		) else equivalent_param[p]
   setattr(UpperCamelCase    ,	UpperCamelCase    ,	getattr(UpperCamelCase    ,	UpperCamelCase		)		)
   delattr(UpperCamelCase    ,	UpperCamelCase		)
 return hparams, config





"""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()		)
