
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 	_lowerCAmelCase					(	unittest.TestCase       ):




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[int]:
							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(_UpperCamelCase     )




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

							def _check_summary_is_not_empty(_UpperCamelCase     ):
										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:
										lowerCAmelCase_						=	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				,				)
										lowerCAmelCase_						=	PyTorchBenchmark(_UpperCamelCase     )
										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(_UpperCamelCase				,				"log.txt"     )     ).exists()     )





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A   =     logging.get_logger(__name__)


_A   =     TypeVar("DatasetType", Dataset, IterableDataset)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,							):




			"""simple docstring"""


			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""				)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)
			else:
						return _concatenate_iterable_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)



