"""simple docstring"""



import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
				import tensorflow as tf
				from tensorflow.python.framework.errors_impl import ResourceExhaustedError

				from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
				import pyanvml.pyanvml as nvml

_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							def run_func(lowerCAmelCase							):
														@wraps(lowerCAmelCase							)
														def run_in_eager_mode(*lowerCAmelCase			,  **lowerCAmelCase							):
																					return func(*lowerCAmelCase			,  **lowerCAmelCase							)

														@wraps(lowerCAmelCase							)
														@tf.function(experimental_compile=lowerCAmelCase							)
														def run_in_graph_mode(*lowerCAmelCase			,  **lowerCAmelCase							):
																					return func(*lowerCAmelCase			,  **lowerCAmelCase							)

														if do_eager_mode is True:
																					if use_xla is not False:
																												raise ValueError(
																												    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""							)
																					return run_in_eager_mode
														else:
																					return run_in_graph_mode

							return run_func

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			random.Random()
							A__       :     Optional[int]								=			[rng.randint(0			,  vocab_size - 1							) for i in range(batch_size * sequence_length							)]
							return tf.constant(lowerCAmelCase			,  shape=(batch_size, sequence_length)			,  dtype=tf.intaa							)



class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						TensorFlowBenchmarkArguments
			_UpperCamelCase  :						PretrainedConfig
			_UpperCamelCase  :						str					     =				"TensorFlow"





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return tf.__version__





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.args.strategy
										if strategy is None:
																	raise ValueError("""A device strategy has to be initialized before using TensorFlow."""							)
										A__       :     Any								=			self._prepare_inference_func(snake_case_   ,      snake_case_   ,      snake_case_							)
										return self._measure_speed(_inference							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[Any]								=			self.args.strategy
										if strategy is None:
																	raise ValueError("""A device strategy has to be initialized before using TensorFlow."""							)
										A__       :     Optional[int]								=			self._prepare_train_func(snake_case_   ,      snake_case_   ,      snake_case_							)
										return self._measure_speed(_train							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if self.args.is_gpu:
																	tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,      snake_case_							)
										A__       :     str								=			self.args.strategy
										if strategy is None:
																	raise ValueError("""A device strategy has to be initialized before using TensorFlow."""							)
										A__       :     Optional[int]								=			self._prepare_inference_func(snake_case_   ,      snake_case_   ,      snake_case_							)
										return self._measure_memory(_inference							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if self.args.is_gpu:
																	tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,      snake_case_							)
										A__       :     List[str]								=			self.args.strategy
										if strategy is None:
																	raise ValueError("""A device strategy has to be initialized before using TensorFlow."""							)

										A__       :     List[Any]								=			self._prepare_train_func(snake_case_   ,      snake_case_   ,      snake_case_							)
										return self._measure_memory(_train							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.config_dict[model_name]

										if self.args.fpaa:
																	raise NotImplementedError("""Mixed precision is currently not supported."""							)

										A__       :     Optional[Any]								=			(
										    hasattr(snake_case_   ,      """architectures"""							)
										    and isinstance(config.architectures   ,      snake_case_							)
										    and len(config.architectures							) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																	try:
																								A__       :     Tuple								=			"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																								A__       :     Any								=			__import__("""transformers"""   ,      fromlist=[model_class]							)
																								A__       :     Optional[Any]								=			getattr(snake_case_   ,      snake_case_							)
																								A__       :     Optional[int]								=			model_cls(snake_case_							)
																	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:
																	A__       :     Tuple								=			TF_MODEL_MAPPING[config.__class__](snake_case_							)

										# encoder-decoder has vocab size saved differently
										A__       :     Union[str, Any]								=			config.vocab_size if hasattr(snake_case_   ,      """vocab_size"""							) else config.encoder.vocab_size
										A__       :     List[str]								=			random_input_ids(snake_case_   ,      snake_case_   ,      snake_case_							)

										@run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla							)
										def encoder_decoder_forward():
																	return model(snake_case_   ,      decoder_input_ids=snake_case_   ,      training=snake_case_							)

										@run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla							)
										def encoder_forward():
																	return model(snake_case_   ,      training=snake_case_							)

										A__       :     Tuple								=			encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

										return _inference





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			self.config_dict[model_name]

										if self.args.eager_mode is not False:
																	raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""							)

										if self.args.fpaa:
																	raise NotImplementedError("""Mixed precision is currently not supported."""							)

										A__       :     List[Any]								=			(
										    hasattr(snake_case_   ,      """architectures"""							)
										    and isinstance(config.architectures   ,      snake_case_							)
										    and len(config.architectures							) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																	try:
																								A__       :     str								=			"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																								A__       :     Any								=			__import__("""transformers"""   ,      fromlist=[model_class]							)
																								A__       :     Tuple								=			getattr(snake_case_   ,      snake_case_							)
																								A__       :     List[str]								=			model_cls(snake_case_							)
																	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:
																	A__       :     Optional[Any]								=			TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case_							)

										# encoder-decoder has vocab size saved differently
										A__       :     Dict								=			config.vocab_size if hasattr(snake_case_   ,      """vocab_size"""							) else config.encoder.vocab_size
										A__       :     Optional[int]								=			random_input_ids(snake_case_   ,      snake_case_   ,      snake_case_							)

										@run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla							)
										def encoder_decoder_train():
																	A__       :     List[str]								=			model(snake_case_   ,      decoder_input_ids=snake_case_   ,      labels=snake_case_   ,      training=snake_case_							)[0]
																	A__       :     Optional[Any]								=			tf.gradients(snake_case_   ,      model.trainable_variables							)
																	return gradients

										@run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla							)
										def encoder_train():
																	A__       :     List[str]								=			model(snake_case_   ,      labels=snake_case_   ,      training=snake_case_							)[0]
																	A__       :     Optional[Any]								=			tf.gradients(snake_case_   ,      model.trainable_variables							)
																	return gradients

										A__       :     Optional[Any]								=			encoder_decoder_train if config.is_encoder_decoder else encoder_train

										return _train





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										with self.args.strategy.scope():
																	try:
																								if self.args.is_tpu or self.args.use_xla:
																															# run additional 10 times to stabilize compilation for tpu
																															logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""							)
																															timeit.repeat(snake_case_   ,      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
																								A__       :     List[Any]								=			timeit.repeat(
																								    snake_case_   ,      repeat=self.args.repeat   ,      number=10   ,      )

																								return min(snake_case_							) / 10.0
																	except ResourceExhaustedError as e:
																								self.print_fn(F'''Doesn\'t fit on GPU. {e}'''							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										logger.info(
										    """Note that TensorFlow allocates more memory than """
										    """it might need to speed up computation. """
										    """The memory reported here corresponds to the memory """
										    """reported by `nvidia-smi`, which can vary depending """
										    """on total available memory on the GPU that is used."""							)
										with self.args.strategy.scope():
																	try:
																								if self.args.trace_memory_line_by_line:
																															if not self.args.eager_mode:
																																						raise ValueError(
																																						    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																																						    """ consumption line by line."""							)
																															A__       :     Tuple								=			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 `args.memory=False`"""							)
																								elif self.args.is_gpu:
																															# 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."""							)
																																						A__       :     List[Any]								=			"""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()
																																						A__       :     Dict								=			nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
																																						A__       :     List[str]								=			nvml.nvmlDeviceGetMemoryInfo(snake_case_							)
																																						A__       :     str								=			meminfo.used
																																						A__       :     List[str]								=			Memory(snake_case_							)
																																						# shutdown nvml
																																						nvml.nvmlShutdown()
																								else:
																															# cpu
																															if self.args.trace_memory_line_by_line:
																																						logger.info(
																																						    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																																						    """ TensorFlow."""							)
																																						A__       :     Optional[int]								=			None
																															else:
																																						A__       :     Optional[Any]								=			measure_peak_memory_cpu(snake_case_							)
																																						A__       :     str								=			Memory(snake_case_							) if isinstance(snake_case_   ,      snake_case_							) else memory_bytes
																								if self.args.trace_memory_line_by_line:
																															A__       :     int								=			stop_memory_tracing(snake_case_							)
																															if memory is None:
																																						A__       :     Tuple								=			summary.total
																								else:
																															A__       :     Any								=			None

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






"""simple docstring"""



import datasets

from .evaluate import evaluate


_UpperCamelCase								=		"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

_UpperCamelCase								=		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

_UpperCamelCase								=		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,   _KWARGS_DESCRIPTION     )
class 						__UpperCAmelCase    (datasets.Metric     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return datasets.MetricInfo(
										    description=_DESCRIPTION   ,      citation=_CITATION   ,      inputs_description=_KWARGS_DESCRIPTION   ,      features=datasets.Features(
										        {
										            """predictions""": {
										                """id""": datasets.Value("""string"""							),
										                """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""							)							),
										            },
										            """references""": {
										                """id""": datasets.Value("""string"""							),
										                """answers""": datasets.features.Sequence(
										                    {
										                        """text""": datasets.Value("""string"""							),
										                        """answer_start""": datasets.Value("""int32"""							),
										                    }							),
										            },
										        }							)   ,      codebase_urls=["""https://www.atticusprojectai.org/cuad"""]   ,      reference_urls=["""https://www.atticusprojectai.org/cuad"""]   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
										A__       :     Optional[Any]								=			[
										    {
										        """paragraphs""": [
										            {
										                """qas""": [
										                    {
										                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
										                        """id""": ref["""id"""],
										                    }
										                    for ref in references
										                ]
										            }
										        ]
										    }
										]
										A__       :     Optional[Any]								=			evaluate(dataset=snake_case_   ,      predictions=snake_case_							)
										return score





