


import os

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

import datasets


lowerCAmelCase__      		= datasets.logging.get_logger(__name__)


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

lowerCAmelCase__      		= '\\nBLEURT 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)\nand 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\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

lowerCAmelCase__      		= '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

lowerCAmelCase__      		= {
    '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):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				Tuple:
            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  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            # check that config name specifies a valid BLEURT model
            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      :       Tuple				    = "bleurt-base-128"

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _A      :       Tuple				    = self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _A      :       str				    = 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      :       str				    = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
            _A      :       Dict				    = score.BleurtScorer(os.path.join(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[Any]:
            _A      :       Dict				    = self.scorer.score(references=__lowerCamelCase ,						candidates=__lowerCamelCase)
            return {"scores": scores}




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       int				    = None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead." ,						__lowerCamelCase ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       Dict				    = image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`.")
            if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`.")

            super().__init__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				Any:

            if text is None and query_images is None and images is None:
                   raise ValueError(
                       "You have to specify at least one text or query image or image. All three cannot be none.")

            if text is not None:
                   if isinstance(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = max([len(__lowerCamelCase) for t in text])

                          # Pad all batch samples to max number of text queries
                          for t in text:
                                 if len(__lowerCamelCase) != max_num_queries:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
                                 encodings.append(__lowerCamelCase)
                   else:
                          raise TypeError("Input text should be a string, a list of strings or a nested list of strings")

                   if return_tensors == "np":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[Any]				    = np.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "jax" and is_flax_available():
                          import jax.numpy as jnp

                          _A      :       Optional[int]				    = jnp.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[int]				    = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "pt" and is_torch_available():
                          import torch

                          _A      :       Optional[Any]				    = torch.cat([encoding["input_ids"] for encoding in encodings] ,						dim=0)
                          _A      :       Union[str, Any]				    = torch.cat([encoding["attention_mask"] for encoding in encodings] ,						dim=0)

                   elif return_tensors == "tf" and is_tf_available():
                          import tensorflow as tf

                          _A      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   else:
                          raise ValueError("Target return tensor type could not be returned")

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

            if query_images is not None:
                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       List[str]				    = self.image_processor(
                       __lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase).pixel_values
                   _A      :       Union[str, Any]				    = query_pixel_values

            if images is not None:
                   _A      :       int				    = self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)

            if text is not None and images is not None:
                   _A      :       Tuple				    = image_features.pixel_values
                   return encoding
            elif query_images is not None and images is not None:
                   _A      :       int				    = image_features.pixel_values
                   return encoding
            elif text is not None or query_images is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**__lowerCamelCase) ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				int:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,						__lowerCamelCase ,						)
            return self.image_processor_class


     @property
     def  _lowerCamelCase (						self)			->				List[str]:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__lowerCamelCase ,						)
            return self.image_processor
