



"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase__		:    Optional[int]				   =     logging.get_logger(__name__)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str=None   ,       lowerCAmelCase__				:    Dict=None						)						->       Tuple:

  """simple docstring"""




  return field(default_factory=lambda: default   ,       metadata=lowerCAmelCase__						)







@dataclass
class 				UpperCamelCase__       :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					list_field(
          default=[], metadata={
              """help""": (
                  """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                  """ of all available models"""
              )
          }, )

      _SCREAMING_SNAKE_CASE      =					list_field(
          default=[8], metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}  )

      _SCREAMING_SNAKE_CASE      =					list_field(
          default=[8, 32, 128, 512], metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}, )

      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}  )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Use FP16 to accelerate inference."""}  )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Benchmark training of model"""}  )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Verbose memory tracing"""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={
              """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
          }, )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Trace memory line by line"""}  )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Save result to a CSV file"""}  )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Save all print statements in a log file"""}  )
      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Whether to print environment information"""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={
              """help""": (
                  """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                  """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                  """ for debugging / testing and on TPU."""
              )
          }, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=F'''inference_time_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving time results to csv."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=F'''inference_memory_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving memory results to csv."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=F'''train_time_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving time results to csv for training."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=F'''train_memory_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=F'''env_info_{round(time()  )}.csv''', metadata={"""help""": """CSV filename used if saving environment information."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=F'''log_{round(time()  )}.csv''', metadata={"""help""": """Log filename used if print statements are saved in log."""}, )
      _SCREAMING_SNAKE_CASE      =					field(default=3, metadata={"""help""": """Times an experiment will be run."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={
              """help""": (
                  """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                  """ model weights."""
              )
          }, )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        warnings.warn(
            F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
            ' are deprecated in general and it is advised to use external Benchmarking libraries '
            ' to benchmark Transformer models.'   ,      SCREAMING_SNAKE_CASE_   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return json.dumps(dataclasses.asdict(self	)   ,      indent=2	)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        if len(self.models	) <= 0:
          raise ValueError(
              'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
              ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'	)
        return self.models





      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        if not self.multi_process:
          return False
        elif self.is_tpu:
          logger.info('Multiprocessing is currently not possible on TPU.'	)
          return False
        else:
          return True










"""simple docstring"""







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowercase__		:    Tuple				   =     datasets.logging.get_logger(__name__)


lowercase__		:    List[Any]				   =     """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowercase__		:    Tuple				   =     """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowercase__		:    List[Any]				   =     """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowercase__		:    List[Any]				   =     {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION  )
class 				UpperCamelCase__       (     datasets.Metric  ):




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        return datasets.MetricInfo(
            description=_DESCRIPTION   ,      citation=_CITATION   ,      homepage='https://github.com/google-research/bleurt'   ,      inputs_description=_KWARGS_DESCRIPTION   ,      features=datasets.Features(
                {
                    'predictions': datasets.Value('string'   ,      id='sequence'	),
                    'references': datasets.Value('string'   ,      id='sequence'	),
                }	)   ,      codebase_urls=['https://github.com/google-research/bleurt']   ,      reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				str	):
        # check that config name specifies a valid BLEURT model
        if self.config_name == "default":
          logger.warning(
              'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
              'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'	)
          lowerCAmelCase_       :    List[Any]								=			'bleurt-base-128'

        if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase_       :    List[Any]								=			self.config_name.lower()

        elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase_       :    Tuple								=			self.config_name.upper()

        else:
          raise KeyError(
              F"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"	)

        # download the model checkpoint specified by self.config_name and set up the scorer
        lowerCAmelCase_       :    List[Any]								=			dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]	)
        lowerCAmelCase_       :    List[str]								=			score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    Tuple								=			self.scorer.score(references=SCREAMING_SNAKE_CASE_   ,      candidates=SCREAMING_SNAKE_CASE_	)
        return {"scores": scores}





