

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


lowercase_			=  None
lowercase_			=  "<" 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
lowercase_			=  [
    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 			A						:
    lowerCamelCase		     =							True
    lowerCamelCase		     =							None
    # Automatically constructed
    lowerCamelCase		     =							"PIL.Image.Image"
    lowerCamelCase		     =							pa.struct({'bytes': pa.binary(), 'path': pa.string()}			)
    lowerCamelCase		     =							field(default='Image'   ,							init=__UpperCamelCase   ,							repr=__UpperCamelCase			)


    def __call__(				self			:	Any		)->   Dict:





        '''simple docstring'''







        return self.pa_type


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]		)->   str:





        '''simple docstring'''







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

        if isinstance(_lowerCAmelCase,_lowerCAmelCase		):
            A__   				=       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 						snake_case__					(				self			:	List[str],lowercase_			:	dict,lowercase_			:	Dict=None		)->   List[str]:





        '''simple docstring'''







        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:
            A__   				=       {}

        A__   				=       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		):
                    A__   				=       PIL.Image.open(_lowerCAmelCase		)
                else:
                    A__   				=       path.split('::'		)[-1]
                    try:
                        A__   				=       string_to_dict(_lowerCAmelCase,config.HUB_DATASETS_URL		)["""repo_id"""]
                        A__   				=       token_per_repo_id.get(_lowerCAmelCase		)
                    except ValueError:
                        A__   				=       None
                    with xopen(_lowerCAmelCase,'rb',use_auth_token=_lowerCAmelCase		) as f:
                        A__   				=       BytesIO(f.read()		)
                    A__   				=       PIL.Image.open(bytes_		)
        else:
            A__   				=       PIL.Image.open(BytesIO(bytes_		)		)
        image.load()  # to avoid "Too many open files" errors
        return image


    def 						snake_case__					(				self			:	Union[str, Any]		)->   Optional[Any]:





        '''simple docstring'''







        from .features import Value

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


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	Union[pa.StringArray, pa.StructArray, pa.ListArray]		)->   Optional[Any]:





        '''simple docstring'''







        if pa.types.is_string(storage.type		):
            A__   				=       pa.array([None] * len(_lowerCAmelCase		),type=pa.binary()		)
            A__   				=       pa.StructArray.from_arrays([bytes_array, storage],['bytes', 'path'],mask=storage.is_null()		)
        elif pa.types.is_binary(storage.type		):
            A__   				=       pa.array([None] * len(_lowerCAmelCase		),type=pa.string()		)
            A__   				=       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:
                A__   				=       storage.field('bytes'		)
            else:
                A__   				=       pa.array([None] * len(_lowerCAmelCase		),type=pa.binary()		)
            if storage.type.get_field_index('path'		) >= 0:
                A__   				=       storage.field('path'		)
            else:
                A__   				=       pa.array([None] * len(_lowerCAmelCase		),type=pa.string()		)
            A__   				=       pa.StructArray.from_arrays([bytes_array, path_array],['bytes', 'path'],mask=storage.is_null()		)
        elif pa.types.is_list(storage.type		):
            A__   				=       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(),)
            A__   				=       pa.array([None] * len(_lowerCAmelCase		),type=pa.string()		)
            A__   				=       pa.StructArray.from_arrays(
                [bytes_array, path_array],['bytes', 'path'],mask=bytes_array.is_null()		)
        return array_cast(_lowerCAmelCase,self.pa_type		)


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	pa.StructArray		)->   Any:





        '''simple docstring'''







        @no_op_if_value_is_null
        def path_to_bytes(lowercase_			:	Tuple		):
            with xopen(_lowerCAmelCase,'rb'		) as f:
                A__   				=       f.read()
            return bytes_

        A__   				=       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(),)
        A__   				=       pa.array(
            [os.path.basename(_lowerCAmelCase		) if path is not None else None for path in storage.field('path'		).to_pylist()],type=pa.string(),)
        A__   				=       pa.StructArray.from_arrays([bytes_array, path_array],['bytes', 'path'],mask=bytes_array.is_null()		)
        return array_cast(_lowerCAmelCase,self.pa_type		)




def 						_snake_case(   )							->				Tuple:





    '''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()
        A__   				=       list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
    return _IMAGE_COMPRESSION_FORMATS
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     "PIL.Image.Image"				)							->				int:





    '''simple docstring'''






    A__   				=       BytesIO()
    if image.format in list_image_compression_formats():
        A__   				=       image.format
    else:
        A__   				=       """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
    image.save(__SCREAMING_SNAKE_CASE   ,      format=__SCREAMING_SNAKE_CASE				)
    return buffer.getvalue()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     "PIL.Image.Image"				)							->				int:





    '''simple docstring'''






    if hasattr(__SCREAMING_SNAKE_CASE   ,      'filename'				) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE				)}
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     np.ndarray				)							->				int:





    '''simple docstring'''






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

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

    A__   				=       None

    # Multi-channel array case (only np.dtype("|u1") is allowed)
    if array.shape[2:]:
        A__   				=       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:
        A__   				=       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:
            A__   				=       dtype_byteorder + dtype_kind + str(__SCREAMING_SNAKE_CASE				)
            A__   				=       np.dtype(__SCREAMING_SNAKE_CASE				)
            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}'				)
    A__   				=       PIL.Image.fromarray(array.astype(__SCREAMING_SNAKE_CASE				)				)
    return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE				)}

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				)							->				Dict:





    '''simple docstring'''






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

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




import comet  # From: unbabel-comet
import torch

import datasets


lowercase_			=  datasets.logging.get_logger(__name__)

lowercase_			=  "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

lowercase_			=  "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

lowercase_			=  "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"


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







    """simple docstring"""





    def 						snake_case__					(				self			:	Optional[Any]		)->   str:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='https://unbabel.github.io/COMET/html/index.html',inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'sources': datasets.Value('string',id='sequence'		),
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/Unbabel/COMET'],reference_urls=[
                'https://github.com/Unbabel/COMET',
                'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
                'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
            ],)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict		)->   str:





        '''simple docstring'''







        if self.config_name == "default":
            A__   				=       comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'		)		)
        else:
            A__   				=       comet.load_from_checkpoint(comet.download_model(self.config_name		)		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict,lowercase_			:	int,lowercase_			:	List[Any],lowercase_			:	Tuple=None,lowercase_			:	List[str]=False		)->   int:





        '''simple docstring'''







        if gpus is None:
            A__   				=       1 if torch.cuda.is_available() else 0
        A__   				=       {'src': sources, 'mt': predictions, 'ref': references}
        A__   				=       [dict(zip(lowercase_,lowercase_		)		) for t in zip(*data.values()		)]
        A__				,						A__   				=       self.scorer.predict(lowercase_,gpus=lowercase_,progress_bar=lowercase_		)
        return {"mean_score": mean_score, "scores": scores}

