

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 						__UpperCamelCase    (					unittest.TestCase						):




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		for model_result in results.values():
			for batch_size, sequence_length in zip(model_result["bs"]		,	model_result["ss"]							):
				UpperCAmelCase_       				=			model_result["result"][batch_size][sequence_length]
				self.assertIsNotNone(_UpperCAmelCase							)




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tiny-gpt2"
		UpperCAmelCase_       				=			PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	torchscript=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
		UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase							)
		UpperCAmelCase_       				=			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       __A					(		self	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						str							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tiny-gpt2"
		UpperCAmelCase_       				=			PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
		UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase							)
		UpperCAmelCase_       				=			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       __A					(		self	:						List[Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tiny-gpt2"
		UpperCAmelCase_       				=			AutoConfig.from_pretrained(_UpperCAmelCase							)
		UpperCAmelCase_       				=			PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
		UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]							)
		UpperCAmelCase_       				=			benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result							)
		self.check_results_dict_not_empty(results.memory_inference_result							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tinier_bart"
		UpperCAmelCase_       				=			AutoConfig.from_pretrained(_UpperCAmelCase							)
		UpperCAmelCase_       				=			PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
		UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]							)
		UpperCAmelCase_       				=			benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result							)
		self.check_results_dict_not_empty(results.memory_inference_result							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tiny-gpt2"
		UpperCAmelCase_       				=			AutoConfig.from_pretrained(_UpperCAmelCase							)
		UpperCAmelCase_       				=			PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
		UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]							)
		UpperCAmelCase_       				=			benchmark.run()
		self.check_results_dict_not_empty(results.time_train_result							)
		self.check_results_dict_not_empty(results.memory_train_result							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tinier_bart"
		UpperCAmelCase_       				=			AutoConfig.from_pretrained(_UpperCAmelCase							)
		UpperCAmelCase_       				=			PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
		UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]							)
		UpperCAmelCase_       				=			benchmark.run()
		self.check_results_dict_not_empty(results.time_train_result							)
		self.check_results_dict_not_empty(results.memory_train_result							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tiny-gpt2"
		with tempfile.TemporaryDirectory() as tmp_dir:
			UpperCAmelCase_       				=			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		,	)
			UpperCAmelCase_       				=			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       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"sshleifer/tiny-gpt2"

		def _check_summary_is_not_empty(lowerCAmelCase	:						Optional[int]							):
			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:
			UpperCAmelCase_       				=			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		,	)
			UpperCAmelCase_       				=			PyTorchBenchmark(_UpperCAmelCase							)
			UpperCAmelCase_       				=			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()							)


from .imports import is_tqdm_available


if is_tqdm_available():
				from tqdm.auto import tqdm as _tqdm

from ..state import PartialState



def       __lowerCAmelCase      (						A = True       ,     *A       ,     **A    ):
	if not is_tqdm_available():
		raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."    )
	UpperCAmelCase_       				=			False
	if main_process_only:
		UpperCAmelCase_       				=			PartialState().local_process_index == 0
	return _tqdm(*A       ,     **A       ,     disable=A    )