'''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 				lowercase__  (self : Optional[int],					__UpperCAmelCase : str      )     ->			str:

					"""simple docstring"""

					for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['''bs'''],					model_result['''ss''']      ):
													SCREAMING_SNAKE_CASE  :						List[str]      					= model_result['''result'''][batch_size][sequence_length]
													self.assertIsNotNone(_UpperCAmelCase      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= PyTorchBenchmark(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= '''sgugger/tiny-distilbert-classification'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					only_pretrain_model=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Dict      					= PyTorchBenchmark(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )


	def 				lowercase__  (self : Dict      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					torchscript=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Any      					= PyTorchBenchmark(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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 				lowercase__  (self : Dict      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Dict      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					fpaa=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						int      					= PyTorchBenchmark(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )


	def 				lowercase__  (self : Optional[int]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= AutoConfig.from_pretrained(_UpperCAmelCase      )
					# set architectures equal to `None`
					SCREAMING_SNAKE_CASE  :						Tuple      					= None
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						str      					= PyTorchBenchmark(_UpperCAmelCase,					configs=[config]      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )


	def 				lowercase__  (self : Any      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Any      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Any      					= PyTorchBenchmark(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[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 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Any      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					fpaa=_UpperCAmelCase,					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Dict      					= PyTorchBenchmark(_UpperCAmelCase      )
					SCREAMING_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 				lowercase__  (self : Tuple      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= AutoConfig.from_pretrained(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						str      					= PyTorchBenchmark(_UpperCAmelCase,					configs=[config]      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )


	def 				lowercase__  (self : Tuple      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''sshleifer/tinier_bart'''
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= AutoConfig.from_pretrained(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Dict      					= PyTorchBenchmark(_UpperCAmelCase,					configs=[config]      )
					SCREAMING_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 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''sshleifer/tiny-gpt2'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= AutoConfig.from_pretrained(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						List[str]      					= PyTorchBenchmark(_UpperCAmelCase,					configs=[config]      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result      )
					self.check_results_dict_not_empty(results.memory_train_result      )


	def 				lowercase__  (self : Tuple      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''sshleifer/tinier_bart'''
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= AutoConfig.from_pretrained(_UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= PyTorchBenchmarkArguments(
					    models=[MODEL_ID],					training=_UpperCAmelCase,					inference=_UpperCAmelCase,					sequence_lengths=[8],					batch_sizes=[1],					multi_process=_UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= PyTorchBenchmark(_UpperCAmelCase,					configs=[config]      )
					SCREAMING_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 				lowercase__  (self : str      )     ->			List[str]:

					"""simple docstring"""

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






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''sshleifer/tiny-gpt2'''

					def _check_summary_is_not_empty(__UpperCAmelCase : str      ):
									self.assertTrue(hasattr(_UpperCAmelCase,					'''sequential'''      )      )
									self.assertTrue(hasattr(_UpperCAmelCase,					'''cumulative'''      )      )
									self.assertTrue(hasattr(_UpperCAmelCase,					'''current'''      )      )
									self.assertTrue(hasattr(_UpperCAmelCase,					'''total'''      )      )

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









'''simple docstring'''

from manim import *


class 	a__     (				_lowercase	):


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= Rectangle(height=0.5,					width=0.5      )
					SCREAMING_SNAKE_CASE  :						str      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= VGroup(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= Text('''CPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					cpu.move_to([-2.5, -0.5, 0]      )
					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= [mem.copy() for i in range(1      )]
					SCREAMING_SNAKE_CASE  :						Dict      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= Text('''GPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					gpu.align_to(__UpperCAmelCase,					__UpperCAmelCase      )
					gpu.set_x(gpu.get_x() - 1      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Any      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Text('''Model''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					model.move_to([3, -1.0, 0]      )

					self.play(
					    Create(__UpperCAmelCase,					run_time=1      ),					Create(__UpperCAmelCase,					run_time=1      ),					Create(__UpperCAmelCase,					run_time=1      ),					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= MarkupText(
					    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',					font_size=24,					)

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Square(side_length=2.2      )
					key.move_to([-5, 2, 0]      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',					font_size=18,					)

					key_text.move_to([-5, 2.4, 0]      )

					step_a.move_to([2, 2, 0]      )
					self.play(Write(__UpperCAmelCase,					run_time=2.5      ),					Write(__UpperCAmelCase      ),					Write(__UpperCAmelCase      )      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						Dict      					= []
					for i, rect in enumerate(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0.0      ).set_fill(__UpperCAmelCase,					opacity=0.7      )
									cpu_target.move_to(__UpperCAmelCase      )
									cpu_target.generate_target()
									SCREAMING_SNAKE_CASE  :						List[str]      					= 0.46 / 4
									SCREAMING_SNAKE_CASE  :						Any      					= 0.46 / 3

									if i == 0:
													cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      ),					buff=0.02,					direction=__UpperCAmelCase      )
													cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
									elif i == 3:
													cpu_target.target.next_to(cpu_targs[0].target,					direction=__UpperCAmelCase,					buff=0.0      )
									else:
													cpu_target.target.next_to(cpu_targs[i - 1].target,					direction=__UpperCAmelCase,					buff=0.0      )
									cpu_targs.append(__UpperCAmelCase      )

									first_animations.append(rect.animate(run_time=0.5      ).set_stroke(__UpperCAmelCase      )      )
									second_animations.append(MoveToTarget(__UpperCAmelCase,					run_time=1.5      )      )

					self.play(*__UpperCAmelCase      )
					self.play(*__UpperCAmelCase      )

					self.wait()




