import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase_ : Tuple						  =      "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\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_ : List[Any]						  =      "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\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#chrf--chrf for more information.\n"

lowerCAmelCase_ : List[str]						  =      "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"





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


   def __UpperCAmelCase       (   self				:	List[Any])      ->					str:
         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="https://github.com/mjpost/sacreBLEU#chrf--chrf"   ,		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#chrf--chrf"]   ,		reference_urls=[
                 "https://github.com/m-popovic/chrF",
             ]   ,		)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	int = CHRF.CHAR_ORDER   ,		__lowerCAmelCase				:	int = CHRF.WORD_ORDER   ,		__lowerCAmelCase				:	int = CHRF.BETA   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		)      ->					Any:
         lowercase_     =      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_     =      [[refs[i] for refs in references] for i in range(__lowerCAmelCase)]

         lowercase_     =      CHRF(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      sb_chrf.corpus_score(__lowerCAmelCase   ,		__lowerCAmelCase)

         return {
             "score": output.score,
             "char_order": output.char_order,
             "word_order": output.word_order,
             "beta": output.beta,
         }







'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       import PIL.Image

       from .features import FeatureType


lowerCAmelCase_ : Optional[List[str]]						  =      None
lowerCAmelCase_ : str						  =      "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase_ : Any						  =      [
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=True
   lowerCamelCase_    					=None
   # Automatically constructed
   lowerCamelCase_    					="PIL.Image.Image"
   lowerCamelCase_    					=pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
   lowerCamelCase_    					=field(default='Image' ,				init=__lowerCamelCase ,				repr=__lowerCamelCase )


   def __call__(   self				:	List[Any])      ->					List[Any]:
         return self.pa_type


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"])      ->					dict:
         if config.PIL_AVAILABLE:
               import PIL.Image
         else:
               raise ImportError("To support encoding images, please install 'Pillow'.")

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               lowercase_     =      np.array(__lowerCAmelCase)

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               return {"path": value, "bytes": None}
         elif isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               return {"path": None, "bytes": value}
         elif isinstance(__lowerCAmelCase   ,		np.ndarray):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(__lowerCAmelCase)
         elif isinstance(__lowerCAmelCase   ,		PIL.Image.Image):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(__lowerCAmelCase)
         elif value.get("path") is not None and os.path.isfile(value["path"]):
               # we set "bytes": None to not duplicate the data if they're already available locally
               return {"bytes": None, "path": value.get("path")}
         elif value.get("bytes") is not None or value.get("path") is not None:
               # store the image bytes, and path is used to infer the image format using the file extension
               return {"bytes": value.get("bytes"), "path": value.get("path")}
         else:
               raise ValueError(
                   F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	dict   ,		__lowerCAmelCase				:	Dict=None)      ->					"PIL.Image.Image":
         if not self.decode:
               raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead.")

         if config.PIL_AVAILABLE:
               import PIL.Image
         else:
               raise ImportError("To support decoding images, please install 'Pillow'.")

         if token_per_repo_id is None:
               lowercase_     =      {}

         lowercase_ ,							lowercase_     =      value["path"], value["bytes"]
         if bytes_ is None:
               if path is None:
                     raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.')
               else:
                     if is_local_path(__lowerCAmelCase):
                           lowercase_     =      PIL.Image.open(__lowerCAmelCase)
                     else:
                           lowercase_     =      path.split("::")[-1]
                           try:
                                 lowercase_     =      string_to_dict(__lowerCAmelCase   ,		config.HUB_DATASETS_URL)["repo_id"]
                                 lowercase_     =      token_per_repo_id.get(__lowerCAmelCase)
                           except ValueError:
                                 lowercase_     =      None
                           with xopen(__lowerCAmelCase   ,		"rb"   ,		use_auth_token=__lowerCAmelCase) as f:
                                 lowercase_     =      BytesIO(f.read())
                           lowercase_     =      PIL.Image.open(bytes_)
         else:
               lowercase_     =      PIL.Image.open(BytesIO(bytes_))
         image.load()  # to avoid "Too many open files" errors
         return image


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value("binary"),
                 "path": Value("string"),
             }
         )


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	Union[pa.StringArray, pa.StructArray, pa.ListArray])      ->					pa.StructArray:
         if pa.types.is_string(storage.type):
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.binary())
               lowercase_     =      pa.StructArray.from_arrays([bytes_array, storage]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_binary(storage.type):
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays([storage, path_array]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_struct(storage.type):
               if storage.type.get_field_index("bytes") >= 0:
                     lowercase_     =      storage.field("bytes")
               else:
                     lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.binary())
               if storage.type.get_field_index("path") >= 0:
                     lowercase_     =      storage.field("path")
               else:
                     lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_list(storage.type):
               lowercase_     =      pa.array(
                   [encode_np_array(np.array(__lowerCAmelCase))["bytes"] if arr is not None else None for arr in storage.to_pylist()]   ,		type=pa.binary()   ,		)
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays(
                   [bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	pa.StructArray)      ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(__lowerCAmelCase				:	int):
               with xopen(__lowerCAmelCase   ,		"rb") as f:
                     lowercase_     =      f.read()
               return bytes_

         lowercase_     =      pa.array(
             [
                 (path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]   ,		type=pa.binary()   ,		)
         lowercase_     =      pa.array(
             [os.path.basename(__lowerCAmelCase) if path is not None else None for path in storage.field("path").to_pylist()]   ,		type=pa.string()   ,		)
         lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)




def __a (	)	->							List[str]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            lowercase_     =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
      return _IMAGE_COMPRESSION_FORMATS


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							bytes:
      '''simple docstring'''


      lowercase_     =      BytesIO()
      if image.format in list_image_compression_formats():
            lowercase_     =      image.format
      else:
            lowercase_     =      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
      image.save(__lowerCamelCase ,		format=__lowerCamelCase				)
      return buffer.getvalue()


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							dict:
      '''simple docstring'''


      if hasattr(__lowerCamelCase ,		"filename"				) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(__lowerCamelCase				)}


def __a (	__lowerCamelCase			:						np.ndarray				)	->							dict:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      lowercase_     =      array.dtype
      lowercase_     =      dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
      lowercase_     =      dtype.kind
      lowercase_     =      dtype.itemsize

      lowercase_     =      None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            lowercase_     =      np.dtype("|u1"				)
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'				)
            if dtype is not dest_dtype:
                  warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''				)
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            lowercase_     =      dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  lowercase_     =      dtype_byteorder + dtype_kind + str(__lowerCamelCase				)
                  lowercase_     =      np.dtype(__lowerCamelCase				)
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''				)
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'				)
      lowercase_     =      PIL.Image.fromarray(array.astype(__lowerCamelCase				)				)
      return {"path": None, "bytes": image_to_bytes(__lowerCamelCase				)}






def __a (	__lowerCamelCase			:						Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				)	->							List[dict]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      if objs:
            lowercase_ ,							lowercase_     =      first_non_null_value(__lowerCamelCase				)
            if isinstance(__lowerCamelCase ,		__lowerCamelCase				):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(__lowerCamelCase ,		np.ndarray				):
                  lowercase_     =      no_op_if_value_is_null(__lowerCamelCase				)
                  return [obj_to_image_dict_func(__lowerCamelCase				) for obj in objs]
            elif isinstance(__lowerCamelCase ,		PIL.Image.Image				):
                  lowercase_     =      no_op_if_value_is_null(__lowerCamelCase				)
                  return [obj_to_image_dict_func(__lowerCamelCase				) for obj in objs]
            else:
                  return objs
      else:
            return objs






