





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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self ,							__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs'''] ,							model_result['''ss''']   ):
															snake_case_						:   Union[str, Any]           =  model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__magic_name__   )






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Any           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Optional[int]           =  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]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''sgugger/tiny-distilbert-classification'''
							snake_case_						:   List[Any]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							only_pretrain_model=__magic_name__ ,							)
							snake_case_						:   Optional[Any]           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   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       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   List[str]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							torchscript=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Dict           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Dict           =  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:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Optional[int]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							fpaa=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   List[str]           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   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       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   int           =  AutoConfig.from_pretrained(__magic_name__   )
							# set architectures equal to `None`
							snake_case_						:   List[str]           =  None
							snake_case_						:   Any           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   List[str]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[Any]           =  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]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Optional[int]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   int           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Optional[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       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Union[str, Any]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							fpaa=__magic_name__ ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Dict           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   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   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Dict           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[int]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   List[Any]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[Any]           =  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:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''sshleifer/tinier_bart'''
							snake_case_						:   Tuple           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   Any           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[int]           =  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]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   str           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   int           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   str           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   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       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   int           =  '''sshleifer/tinier_bart'''
							snake_case_						:   Optional[int]           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   str           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Optional[Any]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   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       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(__magic_name__   ):
											self.assertTrue(hasattr(__magic_name__ ,							'''sequential'''   )   )
											self.assertTrue(hasattr(__magic_name__ ,							'''cumulative'''   )   )
											self.assertTrue(hasattr(__magic_name__ ,							'''current'''   )   )
											self.assertTrue(hasattr(__magic_name__ ,							'''total'''   )   )

							with tempfile.TemporaryDirectory() as tmp_dir:
											snake_case_						:   Dict           =  PyTorchBenchmarkArguments(
											    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							log_filename=os.path.join(__magic_name__ ,							'''log.txt'''   ) ,							log_print=__magic_name__ ,							trace_memory_line_by_line=__magic_name__ ,							multi_process=__magic_name__ ,							)
											snake_case_						:   int           =  PyTorchBenchmark(__magic_name__   )
											snake_case_						:   Union[str, 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(__magic_name__ ,							'''log.txt'''   )   ).exists()   )







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase_    =    TypeVar('''KEY''')
lowerCAmelCase_    =    TypeVar('''VAL''')



@dataclass(frozen=_a,					slots=_a					)
class  __lowerCAmelCase	(      Generic[KEY, VAL]					):
			lowerCamelCase_					:					KEY
			lowerCamelCase_					:					VAL




class  __lowerCAmelCase	(      _Item					):






			def __init__(self   )					->       None:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )






			def __bool__(self   )					->       bool:


							'''simple docstring'''



							return False




lowerCAmelCase_    =    _DeletedItem()



class  __lowerCAmelCase	(      MutableMapping[KEY, VAL]					):






			def __init__(self ,							__magic_name__ = 8 ,							__magic_name__ = 0.75   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  initial_block_size
							snake_case_						:   list[_Item | None]           =  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							snake_case_						:   List[str]           =  capacity_factor
							snake_case_						:   int           =  0






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return hash(__magic_name__   ) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return (ind + 1) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self._buckets[ind]
							if not stored:
											snake_case_						:   Optional[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											self._len += 1
											return True
							elif stored.key == key:
											snake_case_						:   List[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											return True
							else:
											return False






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Any           =  len(self._buckets   ) * self._capacity_factor
							return len(self   ) >= int(__magic_name__   )






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							if len(self._buckets   ) <= self._initial_block_size:
											return False
							snake_case_						:   int           =  len(self._buckets   ) * self._capacity_factor / 2
							return len(self   ) < limit






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._buckets
							snake_case_						:   int           =  [None] * new_size
							snake_case_						:   Optional[int]           =  0
							for item in old_buckets:
											if item:
															self._add_item(item.key ,							item.val   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) * 2   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) // 2   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Iterator[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._get_bucket_index(__magic_name__   )
							for _ in range(len(self._buckets   )   ):
											yield ind
											snake_case_						:   Tuple           =  self._get_next_ind(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											if self._try_set(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
															break






			def __setitem__(self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if self._is_full():
											self._size_up()

							self._add_item(__magic_name__ ,							__magic_name__   )






			def __delitem__(self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self._buckets[ind]
											if item is None:
															raise KeyError(__magic_name__   )
											if item is _deleted:
															continue
											if item.key == key:
															snake_case_						:   Union[str, Any]           =  _deleted
															self._len -= 1
															break
							if self._is_sparse():
											self._size_down()






			def __getitem__(self ,							__magic_name__   )					->       VAL:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   List[Any]           =  self._buckets[ind]
											if item is None:
															break
											if item is _deleted:
															continue
											if item.key == key:
															return item.val
							raise KeyError(__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self._len






			def __iter__(self   )					->       Iterator[KEY]:


							'''simple docstring'''



							yield from (item.key for item in self._buckets if item)






			def __repr__(self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''' ,'''.join(
							    F'''{item.key}: {item.val}''' for item in self._buckets if item   )
							return F'''HashMap({val_string})'''
