






import os

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

import datasets


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


__snake_case    :    Optional[Any]         =     """\
@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}
}
"""

__snake_case    :    Dict         =     """\
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.
"""

__snake_case    :    List[str]         =     """
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]
"""

__snake_case    :    List[str]         =     {
    """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     __SCREAMING_SNAKE_CASE					(   datasets.Metric):






						def       UpperCamelCase__      (	self   ):






												"""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     ,						_UpperCamelCase   ):






												"""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\').'   )
																		lowerCAmelCase__    						=						'bleurt-base-128'

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

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







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






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













import numpy as np
from PIL import Image







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

						# compute the shape of the output matrix
						lowerCAmelCase__    						=						(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape maxpool_shape
						lowerCAmelCase__    						=						np.zeros((maxpool_shape, maxpool_shape)		)

						while i < arr.shape[0]:
												if i + size > arr.shape[0]:
																		# if the end of the matrix is reached, break
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the maximum of the pooling matrix
																		lowerCAmelCase__    						=						np.max(arr[i : i + size, j : j + size]		)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1

												# reset the column index to 0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0

						return updated_arr







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

						# compute the shape of the output matrix
						lowerCAmelCase__    						=						(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape avgpool_shape
						lowerCAmelCase__    						=						np.zeros((avgpool_shape, avgpool_shape)		)

						while i < arr.shape[0]:
												# if the end of the matrix is reached, break
												if i + size > arr.shape[0]:
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the average of the pooling matrix
																		lowerCAmelCase__    						=						int(np.average(arr[i : i + size, j : j + size]		)		)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1
												# reset the column index to 0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0

						return updated_arr


# Main Function
if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""avgpooling""", verbose=True)

					# Loading the image
					__snake_case    :    List[str]         =     Image.open("""path_to_image""")

					# Converting the image to numpy array and maxpooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

					# Converting the image to numpy array and averagepooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





