
from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
   import importlib_metadata
else:
   import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
_A       =					"\\n"

_A       =					"\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n"

_A       =					"\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.\n        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',\n        'tokenize', 'use_effective_order'.\n    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.\n        Keywords include 'concatenate_texts'.\nReturns: depending on the XTREME-S task, one or several of:\n    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'\n    \"f1\": F1 score - for 'minds14'\n    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'\n    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]\n    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'wer': 0.56, 'cer': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')\n    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]\n    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'bleu': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'accuracy': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'f1': 0.58, 'accuracy': 0.6}\n"

_A       =					["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
_A       =					""

try:
   from jiwer import transforms as tr

   _A       =					True
except ImportError:
   _A       =					False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):





   class      _lowerCAmelCase (     tr.AbstractTransform	):




       def __init__( self    ,    _UpperCamelCase = " "					)    ->				List[Any]:
          lowerCAmelCase_      =     sentence_delimiter




       def 					__a						( self    ,    _UpperCamelCase					)    ->				Any:
          return list(_UpperCamelCase					)




       def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
          lowerCAmelCase_      =     []
          for sent_idx, sentence in enumerate(_UpperCamelCase					):
             chars.extend(self.process_string(_UpperCamelCase					)					)
             if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_UpperCamelCase					) - 1:
                chars.append(self.sentence_delimiter					)
          return chars


   _A       =					tr.Compose(
       [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
   )
elif _jiwer_available:
   _A       =					tr.Compose(
       [
           tr.RemoveMultipleSpaces(),
           tr.Strip(),
           tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
           tr.ReduceToListOfListOfChars(),
       ]
   )
else:
   _A       =					None






def        lowerCamelCase__    (    __lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Any    ):




   """simple docstring"""


   return float((preds == labels).mean()    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Tuple    ):




   """simple docstring"""


   return {
       "f1": float(fa_score(y_true=__lowerCAmelCase   ,							y_pred=__lowerCAmelCase   ,							average="macro"    )    ),
       "accuracy": simple_accuracy(__lowerCAmelCase   ,							__lowerCAmelCase    ),
   }






def        lowerCamelCase__    (    __lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  List[Any]="exp"   ,							__lowerCAmelCase  :  List[str]=None   ,							__lowerCAmelCase  :  Dict=False   ,							__lowerCAmelCase  :  List[str]=False   ,							__lowerCAmelCase  :  Optional[int]=None   ,							__lowerCAmelCase  :  Any=False   ,							):




   """simple docstring"""


   lowerCAmelCase_      =     [[label] for label in labels]
   lowerCAmelCase_      =     list(__lowerCAmelCase    )
   try:
      import sacrebleu as scb
   except ImportError:
      raise ValueError(
          "sacrebleu has to be installed in order to apply the bleu metric for covost2."
          "You can install it via `pip install sacrebleu`."    )

   if version.parse(scb.__version__    ) < version.parse("1.4.12"    ):
      raise ImportWarning(
          "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
          "You can install it with `pip install \"sacrebleu>=1.4.12\"`."    )

   lowerCAmelCase_      =     len(labels[0]    )
   if any(len(__lowerCAmelCase    ) != references_per_prediction for refs in labels    ):
      raise ValueError("Sacrebleu requires the same number of references for each prediction"    )
   lowerCAmelCase_      =     [[refs[i] for refs in labels] for i in range(__lowerCAmelCase    )]
   lowerCAmelCase_      =     scb.corpus_bleu(
       __lowerCAmelCase   ,							__lowerCAmelCase   ,							smooth_method=__lowerCAmelCase   ,							smooth_value=__lowerCAmelCase   ,							force=__lowerCAmelCase   ,							lowercase=__lowerCAmelCase   ,							use_effective_order=__lowerCAmelCase   ,							**({"tokenize": tokenize} if tokenize else {})   ,							)
   return {"bleu": output.score}






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  List[Any]    ):




   """simple docstring"""


   try:
      from jiwer import compute_measures
   except ImportError:
      raise ValueError(
          F"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
          "You can install it via `pip install jiwer`."    )

   if concatenate_texts:
      lowerCAmelCase_      =     compute_measures(__lowerCAmelCase   ,							__lowerCAmelCase    )["wer"]

      lowerCAmelCase_      =     compute_measures(__lowerCAmelCase   ,							__lowerCAmelCase   ,							truth_transform=__lowerCAmelCase   ,							hypothesis_transform=__lowerCAmelCase    )["wer"]
      return {"wer": wer, "cer": cer}
   else:

      def compute_score(__lowerCAmelCase  :  Union[str, Any]   ,							__lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  Any="wer"    ):
         lowerCAmelCase_      =     0
         lowerCAmelCase_      =     0
         for prediction, reference in zip(__lowerCAmelCase   ,							__lowerCAmelCase    ):
            if score_type == "wer":
               lowerCAmelCase_      =     compute_measures(__lowerCAmelCase   ,							__lowerCAmelCase    )
            elif score_type == "cer":
               lowerCAmelCase_      =     compute_measures(
                   __lowerCAmelCase   ,							__lowerCAmelCase   ,							truth_transform=__lowerCAmelCase   ,							hypothesis_transform=__lowerCAmelCase    )
            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
            total += measures["substitutions"] + measures["deletions"] + measures["hits"]
         return incorrect / total

      return {"wer": compute_score(__lowerCAmelCase   ,							__lowerCAmelCase   ,							"wer"    ), "cer": compute_score(__lowerCAmelCase   ,							__lowerCAmelCase   ,							"cer"    )}





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




    def 					__a						( self					)    ->				List[str]:
       if self.config_name not in _CONFIG_NAMES:
          raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)

       lowerCAmelCase_      =     "int64" if self.config_name in ["fleurs-lang_id", "minds14"] else "string"

       return datasets.MetricInfo(
           description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
               {"predictions": datasets.Value(_UpperCamelCase					), "references": datasets.Value(_UpperCamelCase					)}					)    ,    codebase_urls=[]    ,    reference_urls=[]    ,    format="numpy"    ,    )




    def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase=None    ,    _UpperCamelCase=None					)    ->				Optional[Any]:
       lowerCAmelCase_      =     bleu_kwargs if bleu_kwargs is not None else {}
       lowerCAmelCase_      =     wer_kwargs if wer_kwargs is not None else {}

       if self.config_name == "fleurs-lang_id":
          return {"accuracy": simple_accuracy(_UpperCamelCase    ,    _UpperCamelCase					)}
       elif self.config_name == "minds14":
          return fa_and_simple_accuracy(_UpperCamelCase    ,    _UpperCamelCase					)
       elif self.config_name == "covost2":
          lowerCAmelCase_      =     bleu_kwargs.pop("smooth_method"    ,    "exp"					)
          lowerCAmelCase_      =     bleu_kwargs.pop("smooth_value"    ,    _UpperCamelCase					)
          lowerCAmelCase_      =     bleu_kwargs.pop("force"    ,    _UpperCamelCase					)
          lowerCAmelCase_      =     bleu_kwargs.pop("lowercase"    ,    _UpperCamelCase					)
          lowerCAmelCase_      =     bleu_kwargs.pop("tokenize"    ,    _UpperCamelCase					)
          lowerCAmelCase_      =     bleu_kwargs.pop("use_effective_order"    ,    _UpperCamelCase					)
          return bleu(
              preds=_UpperCamelCase    ,    labels=_UpperCamelCase    ,    smooth_method=_UpperCamelCase    ,    smooth_value=_UpperCamelCase    ,    force=_UpperCamelCase    ,    lowercase=_UpperCamelCase    ,    tokenize=_UpperCamelCase    ,    use_effective_order=_UpperCamelCase    ,    )
       elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
          lowerCAmelCase_      =     wer_kwargs.pop("concatenate_texts"    ,    _UpperCamelCase					)
          return wer_and_cer(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    self.config_name					)
       else:
          raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     key
							lowerCAmelCase_      =     freq




				def __str__( self					)    ->				Any:
							return f"""Node(key={self.key}, freq={self.freq})"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			if i > j or i < 0 or j > len(__lowerCAmelCase    ) - 1:
						return

			lowerCAmelCase_      =     root[i][j]
			if parent == -1:  # root does not have a parent
						print(F"""{key[node]} is the root of the binary search tree."""    )
			elif is_left:
						print(F"""{key[node]} is the left child of key {parent}."""    )
			else:
						print(F"""{key[node]} is the right child of key {parent}."""    )

			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							node - 1   ,							key[node]   ,							__lowerCAmelCase    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							node + 1   ,							__lowerCAmelCase   ,							key[node]   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			nodes.sort(key=lambda __lowerCAmelCase    : node.key    )

			lowerCAmelCase_      =     len(__lowerCAmelCase    )

			lowerCAmelCase_      =     [nodes[i].key for i in range(__lowerCAmelCase    )]
			lowerCAmelCase_      =     [nodes[i].freq for i in range(__lowerCAmelCase    )]

			# This 2D array stores the overall tree cost (which's as minimized as possible);
			# for a single key, cost is equal to frequency of the key.
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
			# array
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# stores tree roots that will be used later for constructing binary search tree
			lowerCAmelCase_      =     [[i if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]

			for interval_length in range(2   ,							n + 1    ):
						for i in range(n - interval_length + 1    ):
									lowerCAmelCase_      =     i + interval_length - 1

									lowerCAmelCase_      =     sys.maxsize  # set the value to "infinity"
									lowerCAmelCase_      =     total[i][j - 1] + freqs[j]

									# Apply Knuth's optimization
									# Loop without optimization: for r in range(i, j + 1):
									for r in range(root[i][j - 1]   ,							root[i + 1][j] + 1    ):  # r is a temporal root
												lowerCAmelCase_      =     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
												lowerCAmelCase_      =     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
												lowerCAmelCase_      =     left + total[i][j] + right

												if dp[i][j] > cost:
															lowerCAmelCase_      =     cost
															lowerCAmelCase_      =     r

			print("Binary search tree nodes:"    )
			for node in nodes:
						print(__lowerCAmelCase    )

			print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							0   ,							n - 1   ,							-1   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     [Node(__lowerCAmelCase   ,							randint(1   ,							50    )    ) for i in range(10   ,							0   ,							-1    )]
			find_optimal_binary_search_tree(__lowerCAmelCase    )


if __name__ == "__main__":
			main()



