




'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class     _a						(			unittest.TestCase				):
							def 					A   (			self		:			List[Any]       ):



											'''simple docstring'''

											UpperCAmelCase     =	tempfile.mkdtemp()

											UpperCAmelCase     =	[
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    'want',
											    '##want',
											    '##ed',
											    'wa',
											    'un',
											    'runn',
											    '##ing',
											    ',',
											    'low',
											    'lowest',
											]
											UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
											with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as vocab_writer:
															vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

											UpperCAmelCase     =	{
											    'do_resize': True,
											    'size': 20,
											    'do_center_crop': True,
											    'crop_size': 18,
											    'do_normalize': True,
											    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
											    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
											}
											UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       UpperCamelCase__       )
											with open(self.image_processor_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
															json.dump(UpperCamelCase__     ,       UpperCamelCase__       )
							def 					A   (			self		:			Dict     ,       **lowercase		:			int       ):



											'''simple docstring'''

											return BertTokenizer.from_pretrained(self.tmpdirname     ,       **UpperCamelCase__       )
							def 					A   (			self		:			List[str]     ,       **lowercase		:			Optional[int]       ):



											'''simple docstring'''

											return BertTokenizerFast.from_pretrained(self.tmpdirname     ,       **UpperCamelCase__       )
							def 					A   (			self		:			Union[str, Any]     ,       **lowercase		:			List[str]       ):



											'''simple docstring'''

											return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,       **UpperCamelCase__       )
							def 					A   (			self		:			Tuple       ):



											'''simple docstring'''

											shutil.rmtree(self.tmpdirname       )
							def 					A   (			self		:			Dict       ):



											'''simple docstring'''

											UpperCAmelCase     =	[np.random.randint(255     ,       size=(3, 30, 400)     ,       dtype=np.uinta       )]
											UpperCAmelCase     =	[Image.fromarray(np.moveaxis(UpperCamelCase__     ,       0     ,       -1       )       ) for x in image_inputs]
											return image_inputs
							def 					A   (			self		:			Optional[Any]       ):



											'''simple docstring'''

											UpperCAmelCase     =	self.get_tokenizer()
											UpperCAmelCase     =	self.get_rust_tokenizer()
											UpperCAmelCase     =	self.get_image_processor()

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )
											processor_slow.save_pretrained(self.tmpdirname       )
											UpperCAmelCase     =	AlignProcessor.from_pretrained(self.tmpdirname     ,       use_fast=UpperCamelCase__       )

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )
											processor_fast.save_pretrained(self.tmpdirname       )
											UpperCAmelCase     =	AlignProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()     ,       tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()     ,       tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()     ,       tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer     ,       UpperCamelCase__       )
											self.assertIsInstance(processor_fast.tokenizer     ,       UpperCamelCase__       )

											self.assertEqual(processor_slow.image_processor.to_json_string()     ,       image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()     ,       image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor     ,       UpperCamelCase__       )
											self.assertIsInstance(processor_fast.image_processor     ,       UpperCamelCase__       )
							def 					A   (			self		:			List[Any]       ):



											'''simple docstring'''

											UpperCAmelCase     =	AlignProcessor(tokenizer=self.get_tokenizer()     ,       image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

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

											UpperCAmelCase     =	AlignProcessor.from_pretrained(
											    self.tmpdirname     ,       bos_token='''(BOS)'''     ,       eos_token='''(EOS)'''     ,       do_normalize=UpperCamelCase__     ,       padding_value=1.0       )

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

											self.assertEqual(processor.image_processor.to_json_string()     ,       image_processor_add_kwargs.to_json_string()       )
											self.assertIsInstance(processor.image_processor     ,       UpperCamelCase__       )
							def 					A   (			self		:			Optional[Any]       ):



											'''simple docstring'''

											UpperCAmelCase     =	self.get_image_processor()
											UpperCAmelCase     =	self.get_tokenizer()

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )

											UpperCAmelCase     =	self.prepare_image_inputs()

											UpperCAmelCase     =	image_processor(UpperCamelCase__     ,       return_tensors='''np'''       )
											UpperCAmelCase     =	processor(images=UpperCamelCase__     ,       return_tensors='''np'''       )

											for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()     ,       input_processor[key].sum()     ,       delta=1E-2       )
							def 					A   (			self		:			Optional[int]       ):



											'''simple docstring'''

											UpperCAmelCase     =	self.get_image_processor()
											UpperCAmelCase     =	self.get_tokenizer()

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )

											UpperCAmelCase     =	'lower newer'

											UpperCAmelCase     =	processor(text=UpperCamelCase__       )

											UpperCAmelCase     =	tokenizer(UpperCamelCase__     ,       padding='''max_length'''     ,       max_length=64       )

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



											'''simple docstring'''

											UpperCAmelCase     =	self.get_image_processor()
											UpperCAmelCase     =	self.get_tokenizer()

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )

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

											UpperCAmelCase     =	processor(text=UpperCamelCase__     ,       images=UpperCamelCase__       )

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

											# test if it raises when no input is passed
											with pytest.raises(UpperCamelCase__       ):
															processor()
							def 					A   (			self		:			List[str]       ):



											'''simple docstring'''

											UpperCAmelCase     =	self.get_image_processor()
											UpperCAmelCase     =	self.get_tokenizer()

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )

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

											UpperCAmelCase     =	processor.batch_decode(UpperCamelCase__       )
											UpperCAmelCase     =	tokenizer.batch_decode(UpperCamelCase__       )

											self.assertListEqual(UpperCamelCase__     ,       UpperCamelCase__       )







							def 					A   (			self		:			Dict       ):



											'''simple docstring'''

											UpperCAmelCase     =	self.get_image_processor()
											UpperCAmelCase     =	self.get_tokenizer()

											UpperCAmelCase     =	AlignProcessor(tokenizer=UpperCamelCase__     ,       image_processor=UpperCamelCase__       )

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

											UpperCAmelCase     =	processor(text=UpperCamelCase__     ,       images=UpperCamelCase__       )

											self.assertListEqual(list(inputs.keys()       )     ,       processor.model_input_names       )










'''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									='\\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'

A									='\\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'

A									='\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'

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     _a						(			datasets.Metric				):
       def 					A   (			self		:			Dict       ):



           '''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 					A   (			self		:			List[Any]     ,       lowercase		:			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\').'''       )
               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(lowercase     ,       lowercase       )       )







       def 					A   (			self		:			str     ,       lowercase		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.scorer.score(references=lowercase     ,       candidates=lowercase       )
           return {"scores": scores}




