


'''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  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Dict:
														for model_result in results.values():
																					for batch_size, sequence_length in zip(model_result['''bs'''] ,   model_result['''ss''']       ):
																												lowerCAmelCase__	       =     model_result['''result'''][batch_size][sequence_length]
																												self.assertIsNotNone(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
														lowerCAmelCase__	       =     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[int]:
														lowerCAmelCase__	       =     '''sgugger/tiny-distilbert-classification'''
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   only_pretrain_model=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
														lowerCAmelCase__	       =     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]:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   torchscript=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
														lowerCAmelCase__	       =     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[Any]:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   fpaa=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
														lowerCAmelCase__	       =     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]:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     AutoConfig.from_pretrained(lowerCamelCase_       )
														# set architectures equal to `None`
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_ ,   configs=[config]       )
														lowerCAmelCase__	       =     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:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
														lowerCAmelCase__	       =     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       )	->							Any:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   fpaa=lowerCamelCase_ ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
														lowerCAmelCase__	       =     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]:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     AutoConfig.from_pretrained(lowerCamelCase_       )
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_ ,   configs=[config]       )
														lowerCAmelCase__	       =     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[int]:
														lowerCAmelCase__	       =     '''sshleifer/tinier_bart'''
														lowerCAmelCase__	       =     AutoConfig.from_pretrained(lowerCamelCase_       )
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_ ,   configs=[config]       )
														lowerCAmelCase__	       =     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:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														lowerCAmelCase__	       =     AutoConfig.from_pretrained(lowerCamelCase_       )
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_ ,   configs=[config]       )
														lowerCAmelCase__	       =     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       )	->							List[Any]:
														lowerCAmelCase__	       =     '''sshleifer/tinier_bart'''
														lowerCAmelCase__	       =     AutoConfig.from_pretrained(lowerCamelCase_       )
														lowerCAmelCase__	       =     PyTorchBenchmarkArguments(
														    models=[MODEL_ID] ,   training=lowerCamelCase_ ,   inference=lowerCamelCase_ ,   sequence_lengths=[8] ,   batch_sizes=[1] ,   multi_process=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_ ,   configs=[config]       )
														lowerCAmelCase__	       =     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       )	->							Tuple:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''
														with tempfile.TemporaryDirectory() as tmp_dir:
																					lowerCAmelCase__	       =     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_ ,   )
																					lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     '''sshleifer/tiny-gpt2'''

														def _check_summary_is_not_empty(lowerCamelCase_       ):
																					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:
																					lowerCAmelCase__	       =     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_ ,   )
																					lowerCAmelCase__	       =     PyTorchBenchmark(lowerCamelCase_       )
																					lowerCAmelCase__	       =     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'''






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

							for _ in range(A	):
														# Approximates curve as a sequence of linear lines and sums their length
														lowerCAmelCase__	       =     (x_end - x_start) / steps + xa
														lowerCAmelCase__	       =     fnc(A	)
														length += math.hypot(xa - xa			,   fxa - fxa	)

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10