




'''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     A					(	unittest.TestCase					):







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						str      )    ->							Optional[int]:


														"""simple docstring"""


														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(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_      )
														_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]      )    ->							int:


														"""simple docstring"""


														_a			  =       '''sgugger/tiny-distilbert-classification'''
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    only_pretrain_model=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_      )
														_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[str]      )    ->							Dict:


														"""simple docstring"""


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


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    fpaa=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_      )
														_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      )    ->							List[str]:


														"""simple docstring"""


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


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_      )
														_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 :						Dict      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    fpaa=lowerCAmelCase_							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_      )
														_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 :						List[Any]      )    ->							str:


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''
														_a			  =       AutoConfig.from_pretrained(lowerCAmelCase_      )
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_							,    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 :						str      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       '''sshleifer/tinier_bart'''
														_a			  =       AutoConfig.from_pretrained(lowerCAmelCase_      )
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_							,    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[str]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''
														_a			  =       AutoConfig.from_pretrained(lowerCAmelCase_      )
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_							,    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]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       '''sshleifer/tinier_bart'''
														_a			  =       AutoConfig.from_pretrained(lowerCAmelCase_      )
														_a			  =       PyTorchBenchmarkArguments(
														    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=lowerCAmelCase_							,    )
														_a			  =       PyTorchBenchmark(lowerCAmelCase_							,    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]      )    ->							Optional[int]:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a			  =       '''sshleifer/tiny-gpt2'''

														def _check_summary_is_not_empty(lowerCAmelCase_ :						Optional[int]      ):
																					self.assertTrue(hasattr(lowerCAmelCase_							,    '''sequential'''      )      )
																					self.assertTrue(hasattr(lowerCAmelCase_							,    '''cumulative'''      )      )
																					self.assertTrue(hasattr(lowerCAmelCase_							,    '''current'''      )      )
																					self.assertTrue(hasattr(lowerCAmelCase_							,    '''total'''      )      )

														with tempfile.TemporaryDirectory() as tmp_dir:
																					_a			  =       PyTorchBenchmarkArguments(
																					    models=[MODEL_ID]							,    training=lowerCAmelCase_							,    inference=lowerCAmelCase_							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    log_filename=os.path.join(lowerCAmelCase_							,    '''log.txt'''      )							,    log_print=lowerCAmelCase_							,    trace_memory_line_by_line=lowerCAmelCase_							,    multi_process=lowerCAmelCase_							,    )
																					_a			  =       PyTorchBenchmark(lowerCAmelCase_      )
																					_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(lowerCAmelCase_							,    '''log.txt'''      )      ).exists()      )







'''simple docstring'''


_snake_case   :   Any     							=	tuple[float, float, float]
_snake_case   :   Optional[int]     							=	tuple[float, float, float]
def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad   ):





							'''simple docstring'''


							_a			  =       end_pointa[0] - end_pointa[0]
							_a			  =       end_pointa[1] - end_pointa[1]
							_a			  =       end_pointa[2] - end_pointa[2]
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						Vectorad   ):





							'''simple docstring'''


							_a			  =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							_a			  =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							_a			  =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							return tuple(round(UpperCamelCase				,		UpperCamelCase   ) for x in vector   ) == (0, 0, 0)

def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						int = 10   ):





							'''simple docstring'''


							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							return is_zero_vector(get_ad_vectors_cross(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase   )

