





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      str         =    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}







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





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      homepage="https://github.com/google-research/bleurt"       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
              {
                  "predictions": datasets.Value("string"       ,      id="sequence"   ),
                  "references": datasets.Value("string"       ,      id="sequence"   ),
              }   )       ,      codebase_urls=["https://github.com/google-research/bleurt"]       ,      reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      if self.config_name == "default":
         logger.warning(
             "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
             "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."   )
         a__						:    int   		=	"bleurt-base-128"

      if self.config_name.lower() in CHECKPOINT_URLS:
         a__						:    Optional[int]   		=	self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
         a__						:    Dict   		=	self.config_name.upper()

      else:
         raise KeyError(
             F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""   )

      # download the model checkpoint specified by self.config_name and set up the scorer
      a__						:    List[str]   		=	dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]   )
      a__						:    Optional[Any]   		=	score.BleurtScorer(os.path.join(snake_case       ,      snake_case   )   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      a__						:    List[str]   		=	self.scorer.score(references=snake_case       ,      candidates=snake_case   )
      return {"scores": scores}








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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    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


SCREAMING_SNAKE_CASE__							:      List[str]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					["""pixel_values"""]





   def __init__(			self       ,      snake_case = True       ,      snake_case = None       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = True       ,      snake_case = 1 / 255       ,      snake_case = True       ,      snake_case = None       ,      snake_case = None       ,      snake_case = True       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      super().__init__(**snake_case   )
      a__						:    List[str]   		=	size if size is not None else {"height": 384, "width": 384}
      a__						:    List[str]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    Any   		=	do_resize
      a__						:    Optional[int]   		=	size
      a__						:    int   		=	resample
      a__						:    Optional[Any]   		=	do_rescale
      a__						:    Dict   		=	rescale_factor
      a__						:    Dict   		=	do_normalize
      a__						:    Any   		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a__						:    List[str]   		=	image_std if image_std is not None else OPENAI_CLIP_STD
      a__						:    str   		=	do_convert_rgb





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    Optional[int]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )
      if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""   )
      a__						:    Optional[int]   		=	(size["height"], size["width"])
      return resize(snake_case       ,      size=snake_case       ,      resample=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	List[str]:
      """simple docstring"""





      return rescale(snake_case       ,      scale=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      return normalize(snake_case       ,      mean=snake_case       ,      std=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = ChannelDimension.FIRST       ,      **snake_case       ,      )					->	PIL.Image.Image:
      """simple docstring"""





      a__						:    List[str]   		=	do_resize if do_resize is not None else self.do_resize
      a__						:    Optional[Any]   		=	resample if resample is not None else self.resample
      a__						:    int   		=	do_rescale if do_rescale is not None else self.do_rescale
      a__						:    int   		=	rescale_factor if rescale_factor is not None else self.rescale_factor
      a__						:    Union[str, Any]   		=	do_normalize if do_normalize is not None else self.do_normalize
      a__						:    List[Any]   		=	image_mean if image_mean is not None else self.image_mean
      a__						:    int   		=	image_std if image_std is not None else self.image_std
      a__						:    Tuple   		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a__						:    Tuple   		=	size if size is not None else self.size
      a__						:    Any   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    str   		=	make_list_of_images(snake_case   )

      if not valid_images(snake_case   ):
         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_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."   )

      if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."   )

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
         a__						:    List[str]   		=	[convert_to_rgb(snake_case   ) for image in images]

      # All transformations expect numpy arrays.
      a__						:    Dict   		=	[to_numpy_array(snake_case   ) for image in images]

      if do_resize:
         a__						:    Union[str, Any]   		=	[self.resize(image=snake_case       ,      size=snake_case       ,      resample=snake_case   ) for image in images]

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

      if do_normalize:
         a__						:    List[str]   		=	[self.normalize(image=snake_case       ,      mean=snake_case       ,      std=snake_case   ) for image in images]

      a__						:    Union[str, Any]   		=	[to_channel_dimension_format(snake_case       ,      snake_case   ) for image in images]

      a__						:    List[Any]   		=	BatchFeature(data={"pixel_values": images}       ,      tensor_type=snake_case   )

      return encoded_outputs

