

"""simple docstring"""






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		A_	(   __UpperCamelCase     :				str				):
		lowercase										=							min(re.search('''^ +'''       ,	__UpperCamelCase				).end() if t.startswith(''' '''				) else 0 for t in string.splitlines()				)
		return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(__UpperCamelCase				)]				)


__lowerCAmelCase				      =   '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

__lowerCAmelCase				      =   '''\
---
---
# Begin of markdown

Some cool dataset card
'''


__lowerCAmelCase				      =   '''\
# Begin of markdown

Some cool dataset card
'''



class 	_lowerCAmelCase    (      unittest.TestCase       ):

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

									"""simple docstring"""
									with tempfile.TemporaryDirectory() as tmp_dir:
											lowercase										=							Path(a    ) / '''README.md'''
											with open(a ,		'''w+'''    ) as readme_file:
													readme_file.write(a    )
											lowercase										=							DatasetMetadata.from_readme(a    )
											self.assertDictEqual(a ,		{'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}    )

											with open(a ,		'''w+'''    ) as readme_file:
													readme_file.write(a    )
											lowercase										=							DatasetMetadata.from_readme(a    )
											self.assertDictEqual(a ,		{}    )

											with open(a ,		'''w+'''    ) as readme_file:
													readme_file.write(a    )
											lowercase										=							DatasetMetadata.from_readme(a    )
											self.assertEqual(a ,		{}    )

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

									"""simple docstring"""
									lowercase										=							_dedent(
									    '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''    )
									assert DatasetMetadata.from_yaml_string(a    )

									lowercase										=							_dedent(
									    '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''    )
									with self.assertRaises(a    ):
											DatasetMetadata.from_yaml_string(a    )

									lowercase										=							_dedent(
									    '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''    )
									assert DatasetMetadata.from_yaml_string(a    )


"""simple docstring"""






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
							import torch

							from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
							import pyanvml.pyanvml as nvml


__lowerCAmelCase				      =   logging.get_logger(__name__)
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    PyTorchBenchmarkArguments
							__lowerCAmelCase					:    PretrainedConfig
							__lowerCAmelCase					:    str     =  "PyTorch"

							@property
							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Dict:

									"""simple docstring"""
									return torch.__version__

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    float:

									"""simple docstring"""
									lowercase										=							self._prepare_inference_func(a ,		a ,		a    )
									return self._measure_speed(_inference    )

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    [Memory, Optional[MemorySummary]]:

									"""simple docstring"""
									lowercase										=							self._prepare_inference_func(a ,		a ,		a    )
									return self._measure_memory(_inference    )

							def 					_lowerCAmelCase  ( self       :    Union[str, Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    float:

									"""simple docstring"""
									lowercase										=							self._prepare_train_func(a ,		a ,		a    )
									return self._measure_speed(_train    )

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    [Memory, Optional[MemorySummary]]:

									"""simple docstring"""
									lowercase										=							self._prepare_train_func(a ,		a ,		a    )
									return self._measure_memory(_train    )

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    Callable[[], None]:

									"""simple docstring"""
									lowercase										=							self.config_dict[model_name]

									if self.args.torchscript:
											lowercase										=							True

									lowercase										=							(
									    hasattr(a ,		'''architectures'''    )
									    and isinstance(config.architectures ,		a    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
													lowercase										=							config.architectures[0]
													lowercase										=							__import__('''transformers''' ,		fromlist=[model_class]    )
													lowercase										=							getattr(a ,		a    )
													lowercase										=							model_cls(a    )
											except ImportError:
													raise ImportError(
													    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
													    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''    )
									else:
											lowercase										=							MODEL_MAPPING[config.__class__](a    )

									model.eval()
									model.to(self.args.device    )

									# encoder-decoder has vocab size saved differently
									lowercase										=							config.vocab_size if hasattr(a ,		'''vocab_size'''    ) else config.encoder.vocab_size
									lowercase										=							torch.randint(a ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device    )

									if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''    )
											if not self.args.is_gpu:
													raise ValueError('''Mixed precision is possible only for GPU.'''    )
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

									if self.args.torchscript:
											with torch.no_grad():
													lowercase										=							torch.jit.trace(a ,		a    )
									else:
											lowercase										=							model

									def encoder_decoder_forward():
											with torch.no_grad():
													lowercase										=							inference_model(a ,		decoder_input_ids=a    )
											return outputs

									def encoder_forward():
											with torch.no_grad():
													lowercase										=							inference_model(a    )
											return outputs

									lowercase										=							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    Callable[[], None]:

									"""simple docstring"""
									lowercase										=							self.config_dict[model_name]

									lowercase										=							(
									    hasattr(a ,		'''architectures'''    )
									    and isinstance(config.architectures ,		a    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
													lowercase										=							config.architectures[0]
													lowercase										=							__import__('''transformers''' ,		fromlist=[model_class]    )
													lowercase										=							getattr(a ,		a    )
													lowercase										=							model_cls(a    )
											except ImportError:
													raise ImportError(
													    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
													    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''    )
									else:
											lowercase										=							MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a    )

									if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''    )
									else:
											lowercase										=							model

									model.train()
									model.to(self.args.device    )

									# encoder-decoder has vocab size saved differently
									lowercase										=							config.vocab_size if hasattr(a ,		'''vocab_size'''    ) else config.encoder.vocab_size
									lowercase										=							torch.randint(a ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device    )

									if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''    )
											if not self.args.is_gpu:
													raise ValueError('''Mixed precision is possible only for GPU.'''    )

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

									def compute_loss_and_backprob_encoder():
											lowercase										=							train_model(a ,		labels=a    )[0]
											loss.backward()
											return loss

									def compute_loss_and_backprob_encoder_decoder():
											lowercase										=							train_model(a ,		decoder_input_ids=a ,		labels=a    )[0]
											loss.backward()
											return loss

									lowercase										=							(
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    Optional[Any]    )   ->    float:

									"""simple docstring"""
									try:
											if self.args.is_tpu or self.args.torchscript:
													# run additional 10 times to stabilize compilation for tpu and torchscript
													logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''    )
													timeit.repeat(
													    a ,		repeat=1 ,		number=5 ,		)

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											lowercase										=							timeit.repeat(
											    a ,		repeat=self.args.repeat ,		number=10 ,		)

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
													import torch_xla.debug.metrics as met

													self.print_fn(met.metrics_report()    )

											return min(a    ) / 10.0
									except RuntimeError as e:
											self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
											return "N/A"

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    Callable[[], None]    )   ->    [Memory, MemorySummary]:

									"""simple docstring"""
									try:
											if self.args.trace_memory_line_by_line:
													lowercase										=							start_memory_tracing('''transformers'''    )

											if self.args.is_tpu:
													# tpu
													raise NotImplementedError(
													    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
													    ''' `--no-memory` or `args.memory=False`'''    )
											elif self.args.is_gpu:
													if not is_pyanvml_available():
															logger.warning(
															    '''py3nvml not installed, we won\'t log GPU memory usage. '''
															    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''    )
															lowercase										=							'''N/A'''
													else:
															logger.info(
															    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
															    ''' on the same GPU.'''    )
															# init nvml
															nvml.nvmlInit()
															func()
															lowercase										=							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
															lowercase										=							nvml.nvmlDeviceGetMemoryInfo(a    )
															lowercase										=							meminfo.used
															lowercase										=							Memory(a    )
															# shutdown nvml
															nvml.nvmlShutdown()
											else:
													# cpu
													lowercase										=							measure_peak_memory_cpu(a    )
													lowercase										=							Memory(a    ) if isinstance(a ,		a    ) else memory_bytes

											if self.args.trace_memory_line_by_line:
													lowercase										=							stop_memory_tracing(a    )
											else:
													lowercase										=							None

											return memory, summary
									except RuntimeError as e:
											self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
											return "N/A", None