






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__UpperCAmelCase								=						version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
		from nltk import word_tokenize


__UpperCAmelCase								=						'\\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'

__UpperCAmelCase								=						'\\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'

__UpperCAmelCase								=						'\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   lowerCamelCase      (datasets.Metric			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							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				,		_UpperCamelCase   )	->							List[Any]:
							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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=0.9				,		_UpperCamelCase=3				,		_UpperCamelCase=0.5   )	->							List[str]:
							if NLTK_VERSION >= version.Version('3.6.5'   ):
												UpperCAmelCase_    :      Any            =						[
												    meteor_score.single_meteor_score(
												        word_tokenize(_UpperCamelCase   )				,		word_tokenize(_UpperCamelCase   )				,		alpha=_UpperCamelCase				,		beta=_UpperCamelCase				,		gamma=_UpperCamelCase   )
												    for ref, pred in zip(_UpperCamelCase				,		_UpperCamelCase   )
												]
							else:
												UpperCAmelCase_    :      Dict            =						[
												    meteor_score.single_meteor_score(_UpperCamelCase				,		_UpperCamelCase				,		alpha=_UpperCamelCase				,		beta=_UpperCamelCase				,		gamma=_UpperCamelCase   )
												    for ref, pred in zip(_UpperCamelCase				,		_UpperCamelCase   )
												]

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














def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					if not head:
										return True
					# split the list to two parts
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						head.next, head
					while fast and fast.next:
										UpperCAmelCase_    :      str            =						fast.next.next
										UpperCAmelCase_    :      Union[str, Any]            =						slow.next
					UpperCAmelCase_    :      int            =						slow.next
					UpperCAmelCase_    :      List[Any]            =						None  # Don't forget here! But forget still works!
					# reverse the second part
					UpperCAmelCase_    :      Tuple            =						None
					while second:
										UpperCAmelCase_    :      int            =						second.next
										UpperCAmelCase_    :      Any            =						node
										UpperCAmelCase_    :      Optional[Any]            =						second
										UpperCAmelCase_    :      Tuple            =						nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										UpperCAmelCase_    :      Optional[Any]            =						node.next
										UpperCAmelCase_    :      Dict            =						head.next
					return True




def      lowercase__      (     __snake_case    :							Union[str, Any]  ):


					'''simple docstring'''


					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					UpperCAmelCase_    :      Any            =						head
					while fast and fast.next:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						fast.next.next, slow.next

					# 2. Push the second half into the stack
					UpperCAmelCase_    :      List[str]            =						[slow.val]
					while slow.next:
										UpperCAmelCase_    :      List[str]            =						slow.next
										stack.append(slow.val  )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										UpperCAmelCase_    :      int            =						cur.next

					return True






def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					if not head or not head.next:
										return True
					UpperCAmelCase_    :      Tuple            =						{}
					UpperCAmelCase_    :      int            =						0
					while head:
										if head.val in d:
															d[head.val].append(__snake_case  )
										else:
															UpperCAmelCase_    :      List[Any]            =						[pos]
										UpperCAmelCase_    :      Any            =						head.next
										pos += 1
					UpperCAmelCase_    :      Dict            =						pos - 1
					UpperCAmelCase_    :      Optional[int]            =						0
					for v in d.values():
										if len(__snake_case  ) % 2 != 0:
															middle += 1
										else:
															UpperCAmelCase_    :      int            =						0
															for i in range(0     ,       len(__snake_case  )  ):
																				if v[i] + v[len(__snake_case  ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True






