




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__a							     =					'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\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'

__a							     =					'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__a							     =					'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'

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






    """simple docstring"""





    def 				a__ ( self    )					->						Tuple:
         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"`.'''    )
         return datasets.MetricInfo(
             description=_DESCRIPTION  ,citation=_CITATION  ,homepage='''http://www.cs.umd.edu/~snover/tercom/'''  ,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/mjpost/sacreBLEU#ter''']  ,reference_urls=[
                 '''https://github.com/jhclark/tercom''',
             ]  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,)					->						Optional[Any]:
         UpperCAmelCase_     :				Tuple  =     len(references[0]    )
         if any(len(_SCREAMING_SNAKE_CASE    ) != references_per_prediction for refs in references    ):
              raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
         UpperCAmelCase_     :				str  =     [[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE    )]

         UpperCAmelCase_     :				Tuple  =     TER(
             normalized=_SCREAMING_SNAKE_CASE  ,no_punct=_SCREAMING_SNAKE_CASE  ,asian_support=_SCREAMING_SNAKE_CASE  ,case_sensitive=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				Optional[Any]  =     sb_ter.corpus_score(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    b.T
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=1					)
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=0					)
		lowerCAmelCase     =    np.matmul(_A ,  _A					)
		lowerCAmelCase     =    aa[:, None] - 2 * ab + ba[None, :]
		return d






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
					return resize(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    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_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True.""")

					if do_color_quantize and clusters is None:
							raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

					if do_color_quantize:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       ChannelDimension.LAST) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							lowerCAmelCase     =    np.array(__lowerCAmelCase)
							lowerCAmelCase     =    color_quantize(__lowerCAmelCase			,       __lowerCAmelCase).reshape(images.shape[:-1])

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							lowerCAmelCase     =    list(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




