


import os

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

import datasets


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


_lowerCAmelCase:						int   				=       '\\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:						Tuple   				=       '\\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:						List[Any]   				=       '\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:						List[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       lowercase_					(datasets.Metric   ):





				def 						__UpperCamelCase    (				self)  -> Any:
											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       ,  lowercase_)  -> int:
											# 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__      ='bleurt-base-128'

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

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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =self.scorer.score(references=lowercase_       ,  candidates=lowercase_)
											return {"scores": scores}





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =tempfile.mkdtemp()

											a__      =BlipImageProcessor()
											a__      =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

											a__      =BlipProcessor(lowercase_       ,  lowercase_)

											processor.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> str:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).tokenizer





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).image_processor





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta)]

											a__      =[Image.fromarray(np.moveaxis(lowercase_       ,  0       ,  -1)) for x in image_inputs]

											return image_inputs





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =BlipProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor())
											processor.save_pretrained(self.tmpdirname)

											a__      =self.get_tokenizer(bos_token='(BOS)'       ,  eos_token='(EOS)')
											a__      =self.get_image_processor(do_normalize=lowercase_       ,  padding_value=1.0)

											a__      =BlipProcessor.from_pretrained(
											    self.tmpdirname       ,  bos_token='(BOS)'       ,  eos_token='(EOS)'       ,  do_normalize=lowercase_       ,  padding_value=1.0)

											self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer_add_kwargs.get_vocab())
											self.assertIsInstance(processor.tokenizer       ,  lowercase_)

											self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string())
											self.assertIsInstance(processor.image_processor       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =self.prepare_image_inputs()

											a__      =image_processor(lowercase_       ,  return_tensors='np')
											a__      =processor(images=lowercase_       ,  return_tensors='np')

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1e-2)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'

											a__      =processor(text=lowercase_)

											a__      =tokenizer(lowercase_       ,  return_token_type_ids=lowercase_)

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]       ,  encoded_processor[key])





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'
											a__      =self.prepare_image_inputs()

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

											# test if it raises when no input is passed
											with pytest.raises(lowercase_):
																		processor()





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__      =processor.batch_decode(lowercase_)
											a__      =tokenizer.batch_decode(lowercase_)

											self.assertListEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'
											a__      =self.prepare_image_inputs()

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

