
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
 import jax.numpy as jnp


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def __get__(      self		:       str				,       _snake_case		:       Tuple				,       _snake_case		:       Tuple=None				):
          # See docs.python.org/3/howto/descriptor.html#properties
          if obj is None:
              return self
          if self.fget is None:
              raise AttributeError('''unreadable attribute'''				)
          __lowercase		:			Any	    =       '''__cached_''' + self.fget.__name__
          __lowercase		:			str	    =       getattr(_snake_case				,       _snake_case				,       _snake_case				)
          if cached is None:
              __lowercase		:			Tuple	    =       self.fget(_snake_case				)
              setattr(_snake_case				,       _snake_case				,       _snake_case				)
          return cached






def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Union[str, Any]:
    __lowercase		:			int	    =       val.lower()
    if val in {"y", "yes", "t", "true", "on", "1"}:
        return 1
    if val in {"n", "no", "f", "false", "off", "0"}:
        return 0
    raise ValueError(F'invalid truth value {val!r}'	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[Any]:
    if is_torch_fx_proxy(__lowerCAmelCase	):
        return True
    if is_torch_available():
        import torch

        if isinstance(__lowerCAmelCase	,					torch.Tensor	):
            return True
    if is_tf_available():
        import tensorflow as tf

        if isinstance(__lowerCAmelCase	,					tf.Tensor	):
            return True

    if is_flax_available():
        import jax.numpy as jnp
        from jax.core import Tracer

        if isinstance(__lowerCAmelCase	,					(jnp.ndarray, Tracer)	):
            return True

    return isinstance(__lowerCAmelCase	,					np.ndarray	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Dict:
    return isinstance(__lowerCAmelCase	,					np.ndarray	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Union[str, Any]:
    return _is_numpy(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[Any]:
    import torch

    return isinstance(__lowerCAmelCase	,					torch.Tensor	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Any:
    return False if not is_torch_available() else _is_torch(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Any:
    import torch

    return isinstance(__lowerCAmelCase	,					torch.device	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Union[str, Any]:
    return False if not is_torch_available() else _is_torch_device(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Dict:
    import torch

    if isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        if hasattr(__lowerCAmelCase	,					__lowerCAmelCase	):
            __lowercase		:			Optional[Any]	    =       getattr(__lowerCAmelCase	,					__lowerCAmelCase	)
        else:
            return False
    return isinstance(__lowerCAmelCase	,					torch.dtype	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[Any]:
    return False if not is_torch_available() else _is_torch_dtype(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:
    import tensorflow as tf

    return isinstance(__lowerCAmelCase	,					tf.Tensor	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return False if not is_tf_available() else _is_tensorflow(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Any:
    import tensorflow as tf

    # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
    if hasattr(__lowerCAmelCase	,					'''is_symbolic_tensor'''	):
        return tf.is_symbolic_tensor(__lowerCAmelCase	)
    return type(__lowerCAmelCase	) == tf.Tensor


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[str]:
    return False if not is_tf_available() else _is_tf_symbolic_tensor(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:
    import jax.numpy as jnp  # noqa: F811

    return isinstance(__lowerCAmelCase	,					jnp.ndarray	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return False if not is_flax_available() else _is_jax(__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[str]:
    if isinstance(__lowerCAmelCase	,					(dict, UserDict)	):
        return {k: to_py_obj(__lowerCAmelCase	) for k, v in obj.items()}
    elif isinstance(__lowerCAmelCase	,					(list, tuple)	):
        return [to_py_obj(__lowerCAmelCase	) for o in obj]
    elif is_tf_tensor(__lowerCAmelCase	):
        return obj.numpy().tolist()
    elif is_torch_tensor(__lowerCAmelCase	):
        return obj.detach().cpu().tolist()
    elif is_jax_tensor(__lowerCAmelCase	):
        return np.asarray(__lowerCAmelCase	).tolist()
    elif isinstance(__lowerCAmelCase	,					(np.ndarray, np.number)	):  # tolist also works on 0d np arrays
        return obj.tolist()
    else:
        return obj


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    if isinstance(__lowerCAmelCase	,					(dict, UserDict)	):
        return {k: to_numpy(__lowerCAmelCase	) for k, v in obj.items()}
    elif isinstance(__lowerCAmelCase	,					(list, tuple)	):
        return np.array(__lowerCAmelCase	)
    elif is_tf_tensor(__lowerCAmelCase	):
        return obj.numpy()
    elif is_torch_tensor(__lowerCAmelCase	):
        return obj.detach().cpu().numpy()
    elif is_jax_tensor(__lowerCAmelCase	):
        return np.asarray(__lowerCAmelCase	)
    else:
        return obj


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       fields(self				)

          # Safety and consistency checks
          if not len(_snake_case				):
              raise ValueError(F'{self.__class__.__name__} has no fields.'				)
          if not all(field.default is None for field in class_fields[1:]				):
              raise ValueError(F'{self.__class__.__name__} should not have more than one required field.'				)

          __lowercase		:			int	    =       getattr(self				,       class_fields[0].name				)
          __lowercase		:			Union[str, Any]	    =       all(getattr(self				,       field.name				) is None for field in class_fields[1:]				)

          if other_fields_are_none and not is_tensor(_snake_case				):
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			Tuple	    =       first_field.items()
                  __lowercase		:			List[str]	    =       True
              else:
                  try:
                      __lowercase		:			Optional[int]	    =       iter(_snake_case				)
                      __lowercase		:			Any	    =       True
                  except TypeError:
                      __lowercase		:			Any	    =       False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
              if first_field_iterator:
                  for idx, element in enumerate(_snake_case				):
                      if (
                          not isinstance(_snake_case				,       (list, tuple)				)
                          or not len(_snake_case				) == 2
                          or not isinstance(element[0]				,       _snake_case				)
                      ):
                          if idx == 0:
                              # If we do not have an iterator of key/values, set it as attribute
                              __lowercase		:			str	    =       first_field
                          else:
                              # If we have a mixed iterator, raise an error
                              raise ValueError(
                                  F'Cannot set key/value for {element}. It needs to be a tuple (key, value).'				)
                          break
                      setattr(self				,       element[0]				,       element[1]				)
                      if element[1] is not None:
                          __lowercase		:			Any	    =       element[1]
              elif first_field is not None:
                  __lowercase		:			Any	    =       first_field
          else:
              for field in class_fields:
                  __lowercase		:			Any	    =       getattr(self				,       field.name				)
                  if v is not None:
                      __lowercase		:			Dict	    =       v




      def __delitem__(      self		:       List[str]				,       *_snake_case		:       List[Any]				,       **_snake_case		:       Union[str, Any]				):
          raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'				)




      def 		snake_case_			(      self		:       List[str]				,       *_snake_case		:       Optional[Any]				,       **_snake_case		:       Union[str, Any]				):
          raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'				)




      def 		snake_case_			(      self		:       List[Any]				,       *_snake_case		:       List[str]				,       **_snake_case		:       Optional[Any]				):
          raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.'				)




      def 		snake_case_			(      self		:       Optional[int]				,       *_snake_case		:       Union[str, Any]				,       **_snake_case		:       Optional[int]				):
          raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.'				)




      def __getitem__(      self		:       Optional[int]				,       _snake_case		:       int				):
          if isinstance(_snake_case				,       _snake_case				):
              __lowercase		:			List[Any]	    =       dict(self.items()				)
              return inner_dict[k]
          else:
              return self.to_tuple()[k]




      def __setattr__(      self		:       int				,       _snake_case		:       Any				,       _snake_case		:       Optional[Any]				):
          if name in self.keys() and value is not None:
              # Don't call self.__setitem__ to avoid recursion errors
              super().__setitem__(_snake_case				,       _snake_case				)
          super().__setattr__(_snake_case				,       _snake_case				)




      def __setitem__(      self		:       Dict				,       _snake_case		:       int				,       _snake_case		:       Union[str, Any]				):
          # Will raise a KeyException if needed
          super().__setitem__(_snake_case				,       _snake_case				)
          # Don't call self.__setattr__ to avoid recursion errors
          super().__setattr__(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          return tuple(self[k] for k in self.keys()				)






class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_      ):



      """simple docstring"""







      @classmethod
      def 		snake_case_			(      cls		:       Tuple				,       _snake_case		:       Optional[int]				):
          raise ValueError(
              F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()				)}'				)






class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    '''longest'''
      A__	:			Dict        =    '''max_length'''
      A__	:			Any        =    '''do_not_pad'''






class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Any        =    '''pt'''
      A__	:			Tuple        =    '''tf'''
      A__	:			Dict        =    '''np'''
      A__	:			List[Any]        =    '''jax'''






class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       Union[str, Any]				,       _snake_case		:       List[ContextManager]				):
          __lowercase		:			List[str]	    =       context_managers
          __lowercase		:			Optional[int]	    =       ExitStack()




      def __enter__(      self		:       Optional[int]				):
          for context_manager in self.context_managers:
              self.stack.enter_context(_snake_case				)




      def __exit__(      self		:       str				,       *_snake_case		:       Optional[int]				,       **_snake_case		:       int				):
          self.stack.__exit__(*_snake_case				,       **_snake_case				)






def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[Any]:
    __lowercase		:			Union[str, Any]	    =       infer_framework(__lowerCAmelCase	)
    if framework == "tf":
        __lowercase		:			int	    =       inspect.signature(model_class.call	)  # TensorFlow models
    elif framework == "pt":
        __lowercase		:			Optional[int]	    =       inspect.signature(model_class.forward	)  # PyTorch models
    else:
        __lowercase		:			List[str]	    =       inspect.signature(model_class.__call__	)  # Flax models

    for p in signature.parameters:
        if p == "return_loss" and signature.parameters[p].default is True:
            return True

    return False


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			str	    =       model_class.__name__
    __lowercase		:			Any	    =       infer_framework(__lowerCAmelCase	)
    if framework == "tf":
        __lowercase		:			Union[str, Any]	    =       inspect.signature(model_class.call	)  # TensorFlow models
    elif framework == "pt":
        __lowercase		:			Union[str, Any]	    =       inspect.signature(model_class.forward	)  # PyTorch models
    else:
        __lowercase		:			Optional[Any]	    =       inspect.signature(model_class.__call__	)  # Flax models

    if "QuestionAnswering" in model_name:
        return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
    else:
        return [p for p in signature.parameters if "label" in p]


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = ""	,					__lowerCAmelCase = "."	) ->						Optional[int]:

    def _flatten_dict(__lowerCAmelCase	,					__lowerCAmelCase=""	,					__lowerCAmelCase="."	):
        for k, v in d.items():
            __lowercase		:			List[Any]	    =       str(__lowerCAmelCase	) + delimiter + str(__lowerCAmelCase	) if parent_key else k
            if v and isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
                yield from flatten_dict(__lowerCAmelCase	,					__lowerCAmelCase	,					delimiter=__lowerCAmelCase	).items()
            else:
                yield key, v

    return dict(_flatten_dict(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)	)


@contextmanager
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = False	) ->						Union[str, Any]:
    if use_temp_dir:
        with tempfile.TemporaryDirectory() as tmp_dir:
            yield tmp_dir
    else:
        yield working_dir


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase=None	) ->						Tuple:
    if is_numpy_array(__lowerCAmelCase	):
        return np.transpose(__lowerCAmelCase	,					axes=__lowerCAmelCase	)
    elif is_torch_tensor(__lowerCAmelCase	):
        return array.T if axes is None else array.permute(*__lowerCAmelCase	)
    elif is_tf_tensor(__lowerCAmelCase	):
        import tensorflow as tf

        return tf.transpose(__lowerCAmelCase	,					perm=__lowerCAmelCase	)
    elif is_jax_tensor(__lowerCAmelCase	):
        return jnp.transpose(__lowerCAmelCase	,					axes=__lowerCAmelCase	)
    else:
        raise ValueError(F'Type not supported for transpose: {type(__lowerCAmelCase	)}.'	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    if is_numpy_array(__lowerCAmelCase	):
        return np.reshape(__lowerCAmelCase	,					__lowerCAmelCase	)
    elif is_torch_tensor(__lowerCAmelCase	):
        return array.reshape(*__lowerCAmelCase	)
    elif is_tf_tensor(__lowerCAmelCase	):
        import tensorflow as tf

        return tf.reshape(__lowerCAmelCase	,					__lowerCAmelCase	)
    elif is_jax_tensor(__lowerCAmelCase	):
        return jnp.reshape(__lowerCAmelCase	,					__lowerCAmelCase	)
    else:
        raise ValueError(F'Type not supported for reshape: {type(__lowerCAmelCase	)}.'	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase=None	) ->						str:
    if is_numpy_array(__lowerCAmelCase	):
        return np.squeeze(__lowerCAmelCase	,					axis=__lowerCAmelCase	)
    elif is_torch_tensor(__lowerCAmelCase	):
        return array.squeeze() if axis is None else array.squeeze(dim=__lowerCAmelCase	)
    elif is_tf_tensor(__lowerCAmelCase	):
        import tensorflow as tf

        return tf.squeeze(__lowerCAmelCase	,					axis=__lowerCAmelCase	)
    elif is_jax_tensor(__lowerCAmelCase	):
        return jnp.squeeze(__lowerCAmelCase	,					axis=__lowerCAmelCase	)
    else:
        raise ValueError(F'Type not supported for squeeze: {type(__lowerCAmelCase	)}.'	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Dict:
    if is_numpy_array(__lowerCAmelCase	):
        return np.expand_dims(__lowerCAmelCase	,					__lowerCAmelCase	)
    elif is_torch_tensor(__lowerCAmelCase	):
        return array.unsqueeze(dim=__lowerCAmelCase	)
    elif is_tf_tensor(__lowerCAmelCase	):
        import tensorflow as tf

        return tf.expand_dims(__lowerCAmelCase	,					axis=__lowerCAmelCase	)
    elif is_jax_tensor(__lowerCAmelCase	):
        return jnp.expand_dims(__lowerCAmelCase	,					axis=__lowerCAmelCase	)
    else:
        raise ValueError(F'Type not supported for expand_dims: {type(__lowerCAmelCase	)}.'	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:
    if is_numpy_array(__lowerCAmelCase	):
        return np.size(__lowerCAmelCase	)
    elif is_torch_tensor(__lowerCAmelCase	):
        return array.numel()
    elif is_tf_tensor(__lowerCAmelCase	):
        import tensorflow as tf

        return tf.size(__lowerCAmelCase	)
    elif is_jax_tensor(__lowerCAmelCase	):
        return array.size
    else:
        raise ValueError(F'Type not supported for expand_dims: {type(__lowerCAmelCase	)}.'	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						List[str]:
    for key, value in auto_map.items():
        if isinstance(__lowerCAmelCase	,					(tuple, list)	):
            __lowercase		:			Any	    =       [F'{repo_id}--{v}' if (v is not None and '''--''' not in v) else v for v in value]
        elif value is not None and "--" not in value:
            __lowercase		:			List[Any]	    =       F'{repo_id}--{value}'

    return auto_map





def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    for base_class in inspect.getmro(__lowerCAmelCase	):
        __lowercase		:			Dict	    =       base_class.__module__
        __lowercase		:			Any	    =       base_class.__name__
        if module.startswith('''tensorflow'''	) or module.startswith('''keras'''	) or name == "TFPreTrainedModel":
            return "tf"
        elif module.startswith('''torch'''	) or name == "PreTrainedModel":
            return "pt"
        elif module.startswith('''flax'''	) or module.startswith('''jax'''	) or name == "FlaxPreTrainedModel":
            return "flax"
    else:
        raise TypeError(F'Could not infer framework from class {model_class}.'	)




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase					:			Tuple			      =		"\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase					:			Dict			      =		"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase					:			Optional[int]			      =		"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:

    def remove_articles(__lowerCAmelCase	):
        __lowercase		:			int	    =       re.compile(R'''\b(a|an|the)\b'''	,					re.UNICODE	)
        return re.sub(__lowerCAmelCase	,					''' '''	,					__lowerCAmelCase	)

    def white_space_fix(__lowerCAmelCase	):
        return " ".join(text.split()	)

    def remove_punc(__lowerCAmelCase	):
        __lowercase		:			int	    =       set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(__lowerCAmelCase	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase	)	)	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						List[str]:
    return int(normalize_answer(__lowerCAmelCase	) == normalize_answer(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [any(compute_exact(__lowerCAmelCase	,					__lowerCAmelCase	) for ref in refs	) for pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	)]
    return (sum(__lowerCAmelCase	) / len(__lowerCAmelCase	)) * 100


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [rgram for rgrams in rgramslist for rgram in rgrams]
    __lowercase		:			Dict	    =       Counter(__lowerCAmelCase	)

    __lowercase		:			Union[str, Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			str	    =       Counter()
    for sgram, scount in sgramcounter.items():
        __lowercase		:			List[Any]	    =       scount * numref

    __lowercase		:			Optional[Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       Counter()
    for cgram, ccount in cgramcounter.items():
        __lowercase		:			Union[str, Any]	    =       ccount * numref

    # KEEP
    __lowercase		:			Optional[Any]	    =       sgramcounter_rep & cgramcounter_rep
    __lowercase		:			Dict	    =       keepgramcounter_rep & rgramcounter
    __lowercase		:			str	    =       sgramcounter_rep & rgramcounter

    __lowercase		:			Tuple	    =       0
    __lowercase		:			int	    =       0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    __lowercase		:			Any	    =       1
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			List[str]	    =       keeptmpscorea / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        __lowercase		:			Dict	    =       keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    __lowercase		:			Dict	    =       0
    if keepscore_precision > 0 or keepscore_recall > 0:
        __lowercase		:			str	    =       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    __lowercase		:			Tuple	    =       sgramcounter_rep - cgramcounter_rep
    __lowercase		:			str	    =       delgramcounter_rep - rgramcounter
    __lowercase		:			int	    =       sgramcounter_rep - rgramcounter
    __lowercase		:			Dict	    =       0
    __lowercase		:			List[str]	    =       0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Union[str, Any]	    =       deltmpscorea / len(__lowerCAmelCase	)

    # ADDITION
    __lowercase		:			int	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       set(__lowerCAmelCase	) & set(__lowerCAmelCase	)
    __lowercase		:			Any	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)

    __lowercase		:			Tuple	    =       0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    __lowercase		:			int	    =       1
    __lowercase		:			Any	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Any	    =       addtmpscore / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			str	    =       addtmpscore / len(__lowerCAmelCase	)
    __lowercase		:			int	    =       0
    if addscore_precision > 0 or addscore_recall > 0:
        __lowercase		:			Any	    =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    __lowercase		:			Tuple	    =       len(__lowerCAmelCase	)

    __lowercase		:			str	    =       ssent.split(''' '''	)
    __lowercase		:			List[str]	    =       csent.split(''' '''	)
    __lowercase		:			Tuple	    =       []
    __lowercase		:			Any	    =       []
    __lowercase		:			Optional[Any]	    =       []
    __lowercase		:			Optional[int]	    =       []
    __lowercase		:			Dict	    =       []
    __lowercase		:			Optional[int]	    =       []

    __lowercase		:			Dict	    =       []
    __lowercase		:			str	    =       []
    __lowercase		:			Dict	    =       []
    __lowercase		:			List[Any]	    =       []
    for rsent in rsents:
        __lowercase		:			Any	    =       rsent.split(''' '''	)
        __lowercase		:			Optional[int]	    =       []
        __lowercase		:			List[Any]	    =       []
        __lowercase		:			Tuple	    =       []
        ragramslist.append(__lowerCAmelCase	)
        for i in range(0	,					len(__lowerCAmelCase	) - 1	):
            if i < len(__lowerCAmelCase	) - 1:
                __lowercase		:			List[str]	    =       ragrams[i] + ''' ''' + ragrams[i + 1]
                ragrams.append(__lowerCAmelCase	)
            if i < len(__lowerCAmelCase	) - 2:
                __lowercase		:			Union[str, Any]	    =       ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                ragrams.append(__lowerCAmelCase	)
            if i < len(__lowerCAmelCase	) - 3:
                __lowercase		:			int	    =       ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                ragrams.append(__lowerCAmelCase	)
        ragramslist.append(__lowerCAmelCase	)
        ragramslist.append(__lowerCAmelCase	)
        ragramslist.append(__lowerCAmelCase	)

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			Dict	    =       sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			int	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			str	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__lowerCAmelCase	)

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			int	    =       cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			Any	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			Optional[int]	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__lowerCAmelCase	)

    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			List[str]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Optional[int]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Optional[Any]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Any	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			List[Any]	    =       sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    __lowercase		:			Any	    =       sum([delascore, delascore, delascore, delascore]	) / 4
    __lowercase		:			int	    =       sum([addascore, addascore, addascore, addascore]	) / 4
    __lowercase		:			str	    =       (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = True	,					__lowerCAmelCase = "13a"	,					__lowerCAmelCase = True	) ->						List[str]:
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        __lowercase		:			List[Any]	    =       sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__	).major >= 2:
            __lowercase		:			Union[str, Any]	    =       sacrebleu.metrics.bleu._get_tokenizer(__lowerCAmelCase	)()(__lowerCAmelCase	)
        else:
            __lowercase		:			str	    =       sacrebleu.TOKENIZERS[tokenizer]()(__lowerCAmelCase	)
    elif tokenizer == "moses":
        __lowercase		:			Tuple	    =       sacremoses.MosesTokenizer().tokenize(__lowerCAmelCase	,					return_str=__lowerCAmelCase	,					escape=__lowerCAmelCase	)
    elif tokenizer == "penn":
        __lowercase		:			str	    =       sacremoses.MosesTokenizer().penn_tokenize(__lowerCAmelCase	,					return_str=__lowerCAmelCase	)
    else:
        __lowercase		:			int	    =       sentence

    if not return_str:
        __lowercase		:			List[Any]	    =       normalized_sent.split()

    return normalized_sent


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    if not (len(__lowerCAmelCase	) == len(__lowerCAmelCase	) == len(__lowerCAmelCase	)):
        raise ValueError('''Sources length must match predictions and references lengths.'''	)
    __lowercase		:			str	    =       0
    for src, pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	):
        sari_score += SARIsent(normalize(__lowerCAmelCase	)	,					normalize(__lowerCAmelCase	)	,					[normalize(__lowerCAmelCase	) for sent in refs]	)
    __lowercase		:			int	    =       sari_score / len(__lowerCAmelCase	)
    return 100 * sari_score


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase="exp"	,					__lowerCAmelCase=None	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					) ->						str:
    __lowercase		:			List[Any]	    =       len(references[0]	)
    if any(len(__lowerCAmelCase	) != references_per_prediction for refs in references	):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
    __lowercase		:			int	    =       [[refs[i] for refs in references] for i in range(__lowerCAmelCase	)]
    __lowercase		:			List[str]	    =       sacrebleu.corpus_bleu(
        __lowerCAmelCase	,					__lowerCAmelCase	,					smooth_method=__lowerCAmelCase	,					smooth_value=__lowerCAmelCase	,					force=__lowerCAmelCase	,					lowercase=__lowerCAmelCase	,					use_effective_order=__lowerCAmelCase	,					)
    return output.score





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



      """simple docstring"""







      def 		snake_case_			(      self		:       Dict				):
          return datasets.MetricInfo(
              description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''string'''				,       id='''sequence'''				),
                      '''references''': datasets.Sequence(datasets.Value('''string'''				,       id='''sequence'''				)				,       id='''references'''				),
                  }				)				,       codebase_urls=[
                  '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                  '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                  '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                  '''https://github.com/mjpost/sacreBLEU''',
              ]				,       reference_urls=[
                  '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                  '''https://github.com/mjpost/sacreBLEU''',
                  '''https://en.wikipedia.org/wiki/BLEU''',
                  '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
              ]				,       )




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				):
          __lowercase		:			List[Any]	    =       {}
          result.update({'''sari''': compute_sari(sources=_snake_case				,       predictions=_snake_case				,       references=_snake_case				)}				)
          result.update({'''sacrebleu''': compute_sacrebleu(predictions=_snake_case				,       references=_snake_case				)}				)
          result.update({'''exact''': compute_em(predictions=_snake_case				,       references=_snake_case				)}				)
          return result


