



"""simple docstring"""






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowercase : Any      						=						version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
		from nltk import word_tokenize


_lowercase : 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"

_lowercase : List[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"

_lowercase : Union[str, Any]      						=						"\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        __SCREAMING_SNAKE_CASE	(      datasets.Metric   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  List[Any]     )->					Dict:
								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 		snake_case       (       self							:  Any,  lowerCamelCase							:  List[str]     )->					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 		snake_case       (       self							:  int,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Tuple=0.9,  lowerCamelCase							:  int=3,  lowerCamelCase							:  List[Any]=0.5     )->					Any:
								if NLTK_VERSION >= version.Version('''3.6.5'''     ):
											lowerCamelCase__		:		List[str]			=[
											    meteor_score.single_meteor_score(
											        word_tokenize(lowerCamelCase     ),  word_tokenize(lowerCamelCase     ),  alpha=lowerCamelCase,  beta=lowerCamelCase,  gamma=lowerCamelCase     )
											    for ref, pred in zip(lowerCamelCase,  lowerCamelCase     )
											]
								else:
											lowerCamelCase__		:		Optional[Any]			=[
											    meteor_score.single_meteor_score(lowerCamelCase,  lowerCamelCase,  alpha=lowerCamelCase,  beta=lowerCamelCase,  gamma=lowerCamelCase     )
											    for ref, pred in zip(lowerCamelCase,  lowerCamelCase     )
											]

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









"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowercase : Tuple      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['pixel_values']



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  int = 8,  **lowerCamelCase							:  Tuple,  )->					None:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		int			=do_rescale
								lowerCamelCase__		:		Dict			=rescale_factor
								lowerCamelCase__		:		Union[str, Any]			=do_pad
								lowerCamelCase__		:		Union[str, Any]			=pad_size



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  float,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  int     )->					np.ndarray:
								return rescale(lowerCamelCase,  scale=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None     )->					List[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=get_image_size(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=(old_height // size + 1) * size - old_height
								lowerCamelCase__		:		List[str]			=(old_width // size + 1) * size - old_width

								return pad(lowerCamelCase,  ((0, pad_height), (0, pad_width)),  mode='''symmetric''',  data_format=lowerCamelCase     )




					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  ImageInput,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Union[str, ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase							:  Union[str, Any],  )->					Dict:
								lowerCamelCase__		:		List[str]			=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__		:		Tuple			=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__		:		str			=do_pad if do_pad is not None else self.do_pad
								lowerCamelCase__		:		int			=pad_size if pad_size is not None else self.pad_size

								lowerCamelCase__		:		Optional[int]			=make_list_of_images(lowerCamelCase     )

								if not valid_images(lowerCamelCase     ):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

								if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

								# All transformations expect numpy arrays.
								lowerCamelCase__		:		Tuple			=[to_numpy_array(lowerCamelCase     ) for image in images]

								if do_rescale:
											lowerCamelCase__		:		Tuple			=[self.rescale(image=lowerCamelCase,  scale=lowerCamelCase     ) for image in images]

								if do_pad:
											lowerCamelCase__		:		Tuple			=[self.pad(lowerCamelCase,  size=lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		int			=[to_channel_dimension_format(lowerCamelCase,  lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		Dict			={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,  tensor_type=lowerCamelCase     )




