

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 						snake_case__     (      self :       Optional[int],lowercase_ :       Optional[Any]		)->    Union[str, Any]:





        '''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(lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_		)
        A__          =   benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__     (      self :       List[str]		)->    List[Any]:





        '''simple docstring'''







        A__          =   'sgugger/tiny-distilbert-classification'
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_		)
        A__          =   benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__     (      self :       List[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_		)
        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 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_		)
        A__          =   benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__          =   None
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_,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 						snake_case__     (      self :       List[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_		)
        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 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_		)
        A__          =   benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   AutoConfig.from_pretrained(lowercase_		)
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_,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 						snake_case__     (      self :       Dict		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   'sshleifer/tinier_bart'
        A__          =   AutoConfig.from_pretrained(lowercase_		)
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_,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 						snake_case__     (      self :       int		)->    Dict:





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'
        A__          =   AutoConfig.from_pretrained(lowercase_		)
        A__          =   PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__          =   PyTorchBenchmark(lowercase_,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 						snake_case__     (      self :       Optional[int]		)->    Tuple:





        '''simple docstring'''







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





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__          =   'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_ :       Union[str, Any]		):
            self.assertTrue(hasattr(lowercase_,'sequential'		)		)
            self.assertTrue(hasattr(lowercase_,'cumulative'		)		)
            self.assertTrue(hasattr(lowercase_,'current'		)		)
            self.assertTrue(hasattr(lowercase_,'total'		)		)

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





"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Callable[[int | float], int | float] ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int = 1_00 ,			)   ->						float:
						SCREAMING_SNAKE_CASE													=					x_start
						SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0.0
						for _ in range(SCREAMING_SNAKE_CASE_  ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												SCREAMING_SNAKE_CASE													=					(x_end - x_start) / steps + xa
												SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
												area += abs(fxa + fxa  ) * (xa - xa) / 2
												# Increment step
												SCREAMING_SNAKE_CASE													=					xa
												SCREAMING_SNAKE_CASE													=					fxa
						return area


if __name__ == "__main__":


	def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Dict:
							return x**3 + x**2

	print('''f(x) = x^3 + x^2''')
	print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
	__UpperCamelCase						=      10
	while i <= 100000:
		print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
		i *= 10


