
"""simple docstring"""





import os

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

import datasets


_a								=						datasets.logging.get_logger(__name__)


_a								=						"""\
@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}
}
"""

_a								=						"""\
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.
"""

_a								=						"""
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]
"""

_a								=						{
    """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        _UpperCAmelCase(       datasets.Metric      ):



		def 		UpperCAmelCase				(   self)			->			str:






							'''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 		UpperCAmelCase				(   self							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							# 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\').''')
												_UpperCamelCase									=    '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
												_UpperCamelCase									=    self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
												_UpperCamelCase									=    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
							_UpperCamelCase									=    dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
							_UpperCamelCase									=    score.BleurtScorer(os.path.join(__a							,						__a))




		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scorer.score(references=__a							,						candidates=__a)
							return {"scores": scores}










'''simple docstring'''





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 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"OwlViTImageProcessor"
 UpperCAmelCase				=					("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	: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			,       )
       UpperCAmelCase__	:Union[str, Any]      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:List[str]      =      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							:	int			,       __lowerCamelCase							:	str=None			,       __lowerCamelCase							:	List[Any]=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	str="max_length"			,       __lowerCamelCase							:	Any="np"			,       **__lowerCamelCase							:	Tuple ):

    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 )):
          UpperCAmelCase__	:Any      =      [self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )]

       elif isinstance(__lowerCamelCase			,       __lowerCamelCase ) and isinstance(text[0]			,       __lowerCamelCase ):
          UpperCAmelCase__	:Tuple      =      []

          # Maximum number of queries across batch
          UpperCAmelCase__	:List[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:
                UpperCAmelCase__	:str      =      t + [''' '''] * (max_num_queries - len(__lowerCamelCase ))

             UpperCAmelCase__	:Tuple      =      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":
          UpperCAmelCase__	:List[Any]      =      np.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	: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

          UpperCAmelCase__	:List[Any]      =      jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Union[str, Any]      =      jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

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

          UpperCAmelCase__	:Any      =      torch.cat([encoding['''input_ids'''] for encoding in encodings]			,       dim=0 )
          UpperCAmelCase__	:List[str]      =      torch.cat([encoding['''attention_mask'''] for encoding in encodings]			,       dim=0 )

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

          UpperCAmelCase__	:Optional[Any]      =      tf.stack([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:int      =      tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

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

       UpperCAmelCase__	:List[Any]      =      BatchEncoding()
       UpperCAmelCase__	:Union[str, Any]      =      input_ids
       UpperCAmelCase__	:Dict      =      attention_mask

    if query_images is not None:
       UpperCAmelCase__	:Tuple      =      BatchEncoding()
       UpperCAmelCase__	:int      =      self.image_processor(
           __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase ).pixel_values
       UpperCAmelCase__	:Optional[int]      =      query_pixel_values

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

    if text is not None and images is not None:
       UpperCAmelCase__	:Dict      =      image_features.pixel_values
       return encoding
    elif query_images is not None and images is not None:
       UpperCAmelCase__	:Tuple      =      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        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	int ):
    return self.image_processor.post_process(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Tuple ):
    return self.image_processor.post_process_object_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       *__lowerCamelCase							:	Tuple			,       **__lowerCamelCase							:	str ):
    return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[Any]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       *__lowerCamelCase							:	Union[str, Any]			,       **__lowerCamelCase							:	Dict ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    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        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor



