

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import jax
      import jaxlib

a_    :						Optional[Any]    		=      get_logger()

a_    :						Optional[dict]    		=      None




class      UpperCamelCase (     TensorFormatter[Mapping, "jax.Array", Mapping]   ):

    def __init__(			self :      Optional[Any]							,    snake_case__ :      List[str]=None							,    snake_case__ :      List[str]=None							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     super().__init__(features=snake_case__    )
     import jax
     from jaxlib.xla_client import Device

     if isinstance(snake_case__							,    snake_case__    ):
      raise ValueError(
          F"""Expected {device} to be a `str` not {type(snake_case__    )}, as `jaxlib.xla_extension.Device` """
          'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
          'the device with `str()` to get its string identifier that will be internally mapped '
          'to the actual `jaxlib.xla_extension.Device`.'    )
     SCREAMING_SNAKE_CASE								=			device if isinstance(snake_case__							,    snake_case__    ) else str(jax.devices()[0]    )
     # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
     # with `pickle` nor with `dill`, so we need to use a global variable instead
     global DEVICE_MAPPING
     if DEVICE_MAPPING is None:
      SCREAMING_SNAKE_CASE								=			self._map_devices_to_str()
     if self.device not in list(DEVICE_MAPPING.keys()    ):
      logger.warning(
          F"""Device with string identifier {self.device} not listed among the available """
          F"""devices: {list(DEVICE_MAPPING.keys()    )}, so falling back to the default """
          F"""device: {str(jax.devices()[0]    )}."""    )
      SCREAMING_SNAKE_CASE								=			str(jax.devices()[0]    )
     SCREAMING_SNAKE_CASE								=			jnp_array_kwargs

    @staticmethod
    def UpperCamelCase			(			):

     """simple docstring"""






     import jax

     return {str(snake_case__    ): device for device in jax.devices()}

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     import jax
     import jax.numpy as jnp

     if isinstance(snake_case__							,    snake_case__    ) and column:
      if all(
          isinstance(snake_case__							,    jax.Array    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
       return jnp.stack(snake_case__							,    axis=0    )
     return column

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     import jax
     import jax.numpy as jnp

     if isinstance(snake_case__							,    (str, bytes, type(snake_case__    ))    ):
      return value
     elif isinstance(snake_case__							,    (np.character, np.ndarray)    ) and np.issubdtype(value.dtype							,    np.character    ):
      return value.tolist()

     SCREAMING_SNAKE_CASE								=			{}

     if isinstance(snake_case__							,    (np.number, np.ndarray)    ) and np.issubdtype(value.dtype							,    np.integer    ):
      # the default int precision depends on the jax config
      # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
      if jax.config.jax_enable_xaa:
       SCREAMING_SNAKE_CASE								=			{'dtype': jnp.intaa}
      else:
       SCREAMING_SNAKE_CASE								=			{'dtype': jnp.intaa}
     elif isinstance(snake_case__							,    (np.number, np.ndarray)    ) and np.issubdtype(value.dtype							,    np.floating    ):
      SCREAMING_SNAKE_CASE								=			{'dtype': jnp.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
      import PIL.Image

      if isinstance(snake_case__							,    PIL.Image.Image    ):
       SCREAMING_SNAKE_CASE								=			np.asarray(snake_case__    )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
     global DEVICE_MAPPING
     if DEVICE_MAPPING is None:
      SCREAMING_SNAKE_CASE								=			self._map_devices_to_str()

     with jax.default_device(DEVICE_MAPPING[self.device]    ):
      # calling jnp.array on a np.ndarray does copy the data
      # see https://github.com/google/jax/issues/4486
      return jnp.array(snake_case__							,    **{**default_dtype, **self.jnp_array_kwargs}    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      Dict    ):

     """simple docstring"""






     import jax

     # support for torch, tf, jax etc.
     if config.TORCH_AVAILABLE and "torch" in sys.modules:
      import torch

      if isinstance(snake_case__							,    torch.Tensor    ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
     if hasattr(snake_case__							,    '__array__'    ) and not isinstance(snake_case__							,    jax.Array    ):
      SCREAMING_SNAKE_CASE								=			data_struct.__array__()
     # support for nested types like struct of list of struct
     if isinstance(snake_case__							,    np.ndarray    ):
      if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
       return self._consolidate([self.recursive_tensorize(snake_case__    ) for substruct in data_struct]    )
     elif isinstance(snake_case__							,    (list, tuple)    ):
      return self._consolidate([self.recursive_tensorize(snake_case__    ) for substruct in data_struct]    )
     return self._tensorize(snake_case__    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      dict    ):

     """simple docstring"""






     return map_nested(self._recursive_tensorize							,    snake_case__							,    map_list=snake_case__    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      pa.Table    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.numpy_arrow_extractor().extract_row(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.python_features_decoder.decode_row(snake_case__    )
     return self.recursive_tensorize(snake_case__    )

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      pa.Table    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.numpy_arrow_extractor().extract_column(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.python_features_decoder.decode_column(snake_case__							,    pa_table.column_names[0]    )
     SCREAMING_SNAKE_CASE								=			self.recursive_tensorize(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self._consolidate(snake_case__    )
     return column




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      pa.Table    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.numpy_arrow_extractor().extract_batch(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.python_features_decoder.decode_batch(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.recursive_tensorize(snake_case__    )
     for column_name in batch:
      SCREAMING_SNAKE_CASE								=			self._consolidate(batch[column_name]    )
     return batch




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

a_    :						str    		=      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a_    :						int    		=      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




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

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return datasets.MetricInfo(
         description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
             {
                 'predictions': datasets.Value('string'							,    id='sequence'    ),
                 'references': datasets.Value('string'							,    id='sequence'    ),
             }    )							,    codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,    reference_urls=[
             'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
             'https://en.wikipedia.org/wiki/METEOR',
         ]							,    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     import nltk

     nltk.download('wordnet'    )
     if NLTK_VERSION >= version.Version('3.6.5'    ):
      nltk.download('punkt'    )
     if NLTK_VERSION >= version.Version('3.6.6'    ):
      nltk.download('omw-1.4'    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






     if NLTK_VERSION >= version.Version('3.6.5'    ):
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(
              word_tokenize(snake_case__    )							,    word_tokenize(snake_case__    )							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]
     else:
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(snake_case__							,    snake_case__							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]

     return {"meteor": np.mean(snake_case__    )}

