

'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[dict, list, tuple, torch.Tensor]				)      ->			List[Tuple[int, ...]]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     if isinstance(_UpperCamelCase							, _UpperCamelCase				):
          for v in tree.values():
               shapes.extend(_fetch_dims(_UpperCamelCase				)				)
     elif isinstance(_UpperCamelCase							, (list, tuple)				):
          for t in tree:
               shapes.extend(_fetch_dims(_UpperCamelCase				)				)
     elif isinstance(_UpperCamelCase							, torch.Tensor				):
          shapes.append(tree.shape				)
     else:
          raise ValueError('Not supported'				)

     return shapes


@torch.jit.ignore
def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     Tuple[int, ...]				)      ->			Tuple[int, ...]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     for d in reversed(_UpperCamelCase				):
          idx.append(flat_idx % d				)
          _SCREAMING_SNAKE_CASE							    =flat_idx // d

     return tuple(reversed(_UpperCamelCase				)				)


@torch.jit.ignore
def        _lowerCAmelCase					(						_UpperCamelCase						:     Sequence[int]							, _UpperCamelCase						:     Sequence[int]							, _UpperCamelCase						:     Sequence[int]							, _UpperCamelCase						:     Optional[Sequence[bool]] = None							, _UpperCamelCase						:     Optional[Sequence[bool]] = None							, )      ->			List[Tuple[slice, ...]]:
     """simple docstring"""




     def reduce_edge_list(_UpperCamelCase						:     List[bool]				) -> None:
          _SCREAMING_SNAKE_CASE							    =True
          for i in range(len(_UpperCamelCase				)				):
               _SCREAMING_SNAKE_CASE							    =-1 * (i + 1)
               l[reversed_idx] &= tally
               _SCREAMING_SNAKE_CASE							    =l[reversed_idx]

     if start_edges is None:
          _SCREAMING_SNAKE_CASE							    =[s == 0 for s in start]
          reduce_edge_list(_UpperCamelCase				)
     if end_edges is None:
          _SCREAMING_SNAKE_CASE							    =[e == (d - 1) for e, d in zip(_UpperCamelCase							, _UpperCamelCase				)]
          reduce_edge_list(_UpperCamelCase				)

     # Base cases. Either start/end are empty and we're done, or the final,
     # one-dimensional tensor can be simply sliced
     if len(_UpperCamelCase				) == 0:
          return [()]
     elif len(_UpperCamelCase				) == 1:
          return [(slice(start[0]							, end[0] + 1				),)]

     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =[]

     # Dimensions common to start and end can be selected directly
     for s, e in zip(_UpperCamelCase							, _UpperCamelCase				):
          if s == e:
               path_list.append(slice(_UpperCamelCase							, s + 1				)				)
          else:
               break

     _SCREAMING_SNAKE_CASE							    =tuple(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =len(_UpperCamelCase				)

     # start == end, and we're done
     if divergence_idx == len(_UpperCamelCase				):
          return [path]

     def upper() -> Tuple[Tuple[slice, ...], ...]:
          assert start_edges is not None
          assert end_edges is not None

          _SCREAMING_SNAKE_CASE							    =start[divergence_idx]
          return tuple(
              path + (slice(_UpperCamelCase							, sdi + 1				),) + s
              for s in _get_minimal_slice_set(
                  start[divergence_idx + 1 :]							, [d - 1 for d in dims[divergence_idx + 1 :]]							, dims[divergence_idx + 1 :]							, start_edges=start_edges[divergence_idx + 1 :]							, end_edges=[True for _ in end_edges[divergence_idx + 1 :]]							, )				)

     def lower() -> Tuple[Tuple[slice, ...], ...]:
          assert start_edges is not None
          assert end_edges is not None

          _SCREAMING_SNAKE_CASE							    =end[divergence_idx]
          return tuple(
              path + (slice(_UpperCamelCase							, edi + 1				),) + s
              for s in _get_minimal_slice_set(
                  [0 for _ in start[divergence_idx + 1 :]]							, end[divergence_idx + 1 :]							, dims[divergence_idx + 1 :]							, start_edges=[True for _ in start_edges[divergence_idx + 1 :]]							, end_edges=end_edges[divergence_idx + 1 :]							, )				)

     # If both start and end are at the edges of the subtree rooted at
     # divergence_idx, we can just select the whole subtree at once
     if start_edges[divergence_idx] and end_edges[divergence_idx]:
          slices.append(path + (slice(start[divergence_idx]							, end[divergence_idx] + 1				),)				)
     # If just start is at the edge, we can grab almost all of the subtree,
     # treating only the ragged bottom edge as an edge case
     elif start_edges[divergence_idx]:
          slices.append(path + (slice(start[divergence_idx]							, end[divergence_idx]				),)				)
          slices.extend(lower()				)
     # Analogous to the previous case, but the top is ragged this time
     elif end_edges[divergence_idx]:
          slices.extend(upper()				)
          slices.append(path + (slice(start[divergence_idx] + 1							, end[divergence_idx] + 1				),)				)
     # If both sides of the range are ragged, we need to handle both sides
     # separately. If there's contiguous meat in between them, we can index it
     # in one big chunk
     else:
          slices.extend(upper()				)
          _SCREAMING_SNAKE_CASE							    =end[divergence_idx] - start[divergence_idx]
          if middle_ground > 1:
               slices.append(path + (slice(start[divergence_idx] + 1							, end[divergence_idx]				),)				)
          slices.extend(lower()				)

     return slices


@torch.jit.ignore
def        _lowerCAmelCase					(						_UpperCamelCase						:     torch.Tensor							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			torch.Tensor:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =t.shape[:no_batch_dims]
     _SCREAMING_SNAKE_CASE							    =list(_flat_idx_to_idx(_UpperCamelCase							, _UpperCamelCase				)				)
     # _get_minimal_slice_set is inclusive
     _SCREAMING_SNAKE_CASE							    =list(_flat_idx_to_idx(flat_end - 1							, _UpperCamelCase				)				)

     # Get an ordered list of slices to perform
     _SCREAMING_SNAKE_CASE							    =_get_minimal_slice_set(
         _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, )

     _SCREAMING_SNAKE_CASE							    =[t[s] for s in slices]

     return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Callable							, _UpperCamelCase						:     Dict[str, Any]							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int							, _UpperCamelCase						:     bool = False							, _UpperCamelCase						:     Any = None							, _UpperCamelCase						:     bool = False							, )      ->			Any:
     """simple docstring"""




     if not (len(_UpperCamelCase				) > 0):
          raise ValueError('Must provide at least one input'				)

     _SCREAMING_SNAKE_CASE							    =[shape[:no_batch_dims] for shape in _fetch_dims(_UpperCamelCase				)]
     _SCREAMING_SNAKE_CASE							    =tuple([max(_UpperCamelCase				) for s in zip(*_UpperCamelCase				)]				)

     def _prep_inputs(_UpperCamelCase						:     torch.Tensor				) -> torch.Tensor:
          if not low_mem:
               if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
                    _SCREAMING_SNAKE_CASE							    =t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
               _SCREAMING_SNAKE_CASE							    =t.reshape(-1							, *t.shape[no_batch_dims:]				)
          else:
               _SCREAMING_SNAKE_CASE							    =t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
          return t

     _SCREAMING_SNAKE_CASE							    =tensor_tree_map(_prep_inputs							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =None
     if _out is not None:
          _SCREAMING_SNAKE_CASE							    =tensor_tree_map(lambda _UpperCamelCase				: t.view([-1] + list(t.shape[no_batch_dims:]				)				)							, _out				)

     _SCREAMING_SNAKE_CASE							    =1
     for d in orig_batch_dims:
          flat_batch_dim *= d

     _SCREAMING_SNAKE_CASE							    =flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

     def _select_chunk(_UpperCamelCase						:     torch.Tensor				) -> torch.Tensor:
          return t[i : i + chunk_size] if t.shape[0] != 1 else t

     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =prepped_outputs
     for _ in range(_UpperCamelCase				):
          # Chunk the input
          if not low_mem:
               _SCREAMING_SNAKE_CASE							    =_select_chunk
          else:
               _SCREAMING_SNAKE_CASE							    =partial(
                   _chunk_slice							, flat_start=_UpperCamelCase							, flat_end=min(_UpperCamelCase							, i + chunk_size				)							, no_batch_dims=len(_UpperCamelCase				)							, )

          _SCREAMING_SNAKE_CASE							    =tensor_tree_map(_UpperCamelCase							, _UpperCamelCase				)

          # Run the layer on the chunk
          _SCREAMING_SNAKE_CASE							    =layer(**_UpperCamelCase				)

          # Allocate space for the output
          if out is None:
               _SCREAMING_SNAKE_CASE							    =tensor_tree_map(lambda _UpperCamelCase				: t.new_zeros((flat_batch_dim,) + t.shape[1:]				)							, _UpperCamelCase				)

          # Put the chunk in its pre-allocated space
          if isinstance(_UpperCamelCase							, _UpperCamelCase				):

               def assign(_UpperCamelCase						:     dict							, _UpperCamelCase						:     dict				) -> None:
                    for k, v in da.items():
                         if isinstance(_UpperCamelCase							, _UpperCamelCase				):
                              assign(_UpperCamelCase							, da[k]				)
                         else:
                              if _add_into_out:
                                   v[i : i + chunk_size] += da[k]
                              else:
                                   _SCREAMING_SNAKE_CASE							    =da[k]

               assign(_UpperCamelCase							, _UpperCamelCase				)
          elif isinstance(_UpperCamelCase							, _UpperCamelCase				):
               for xa, xa in zip(_UpperCamelCase							, _UpperCamelCase				):
                    if _add_into_out:
                         xa[i : i + chunk_size] += xa
                    else:
                         _SCREAMING_SNAKE_CASE							    =xa
          elif isinstance(_UpperCamelCase							, torch.Tensor				):
               if _add_into_out:
                    out[i : i + chunk_size] += output_chunk
               else:
                    _SCREAMING_SNAKE_CASE							    =output_chunk
          else:
               raise ValueError('Not supported'				)

          i += chunk_size

     _SCREAMING_SNAKE_CASE							    =tensor_tree_map(lambda _UpperCamelCase				: t.view(orig_batch_dims + t.shape[1:]				)							, _UpperCamelCase				)

     return out







class 							A__    :





  def __init__(		self      :	str							,					_a      :	int = 512							,					)	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =max_chunk_size
       _SCREAMING_SNAKE_CASE							    =None
       _SCREAMING_SNAKE_CASE							    =None





  def 						A  (		self      :	Optional[Any]							,					_a      :	Callable							,					_a      :	tuple							,					_a      :	int )	->    int:





       '''simple docstring'''






       logging.info('Tuning chunk size...' )

       if min_chunk_size >= self.max_chunk_size:
            return min_chunk_size

       _SCREAMING_SNAKE_CASE							    =[2**l for l in range(int(math.log(self.max_chunk_size							,					2 ) ) + 1 )]
       _SCREAMING_SNAKE_CASE							    =[c for c in candidates if c > min_chunk_size]
       _SCREAMING_SNAKE_CASE							    =[min_chunk_size] + candidates
       candidates[-1] += 4

       def test_chunk_size(_a      :	int ) -> bool:
            try:
                 with torch.no_grad():
                      fn(*_a							,					chunk_size=_a )
                 return True
            except RuntimeError:
                 return False

       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =len(_a ) - 1
       while i > min_viable_chunk_size_index:
            _SCREAMING_SNAKE_CASE							    =test_chunk_size(candidates[i] )
            if not viable:
                 _SCREAMING_SNAKE_CASE							    =(min_viable_chunk_size_index + i) // 2
            else:
                 _SCREAMING_SNAKE_CASE							    =i
                 _SCREAMING_SNAKE_CASE							    =(i + len(_a ) - 1) // 2

       return candidates[min_viable_chunk_size_index]





  def 						A  (		self      :	Dict							,					_a      :	Iterable							,					_a      :	Iterable )	->    bool:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =True
       for aa, aa in zip(_a							,					_a ):
            assert type(_a ) == type(_a )
            if isinstance(_a							,					(list, tuple) ):
                 consistent &= self._compare_arg_caches(_a							,					_a )
            elif isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =[v for _, v in sorted(aa.items()							,					key=lambda _a : x[0] )]
                 _SCREAMING_SNAKE_CASE							    =[v for _, v in sorted(aa.items()							,					key=lambda _a : x[0] )]
                 consistent &= self._compare_arg_caches(_a							,					_a )
            else:
                 consistent &= aa == aa

       return consistent





  def 						A  (		self      :	List[str]							,					_a      :	Callable							,					_a      :	tuple							,					_a      :	int							,					)	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =True
       _SCREAMING_SNAKE_CASE							    =tree_map(lambda _a : a.shape if isinstance(_a							,					torch.Tensor ) else a							,					_a							,					_a )
       if self.cached_arg_data is not None:
            # If args have changed shape/value, we need to re-tune
            assert len(self.cached_arg_data ) == len(_a )
            _SCREAMING_SNAKE_CASE							    =self._compare_arg_caches(self.cached_arg_data							,					_a )
       else:
            # Otherwise, we can reuse the precomputed value
            _SCREAMING_SNAKE_CASE							    =False

       if not consistent:
            _SCREAMING_SNAKE_CASE							    =self._determine_favorable_chunk_size(
                _a							,					_a							,					_a							,					)
            _SCREAMING_SNAKE_CASE							    =arg_data

       assert self.cached_chunk_size is not None

       return self.cached_chunk_size




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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['bs']			,model_result['ss']      ):
																										a__             =     model_result['result'][batch_size][sequence_length]
																										self.assertIsNotNone(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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  lowerCamelCase__(      self   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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  lowerCamelCase__(      self   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :str      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,train_memory_csv_file=os.path.join(__snake_case			,'train_mem.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,train_time_csv_file=os.path.join(__snake_case			,'train_time.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :List[str]      ):
																			self.assertTrue(hasattr(__snake_case			,'sequential'      )      )
																			self.assertTrue(hasattr(__snake_case			,'cumulative'      )      )
																			self.assertTrue(hasattr(__snake_case			,'current'      )      )
																			self.assertTrue(hasattr(__snake_case			,'total'      )      )

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




