




'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A									=logging.get_logger(__name__)

A									={
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}


class     _a						(			__a				):
							__a       :  List[Any]            =      """beit"""
							def __init__(			self		:			Dict     ,       lowercase		:			Optional[int]=8_192     ,       lowercase		:			Union[str, Any]=768     ,       lowercase		:			Any=12     ,       lowercase		:			int=12     ,       lowercase		:			str=3_072     ,       lowercase		:			Optional[int]="gelu"     ,       lowercase		:			int=0.0     ,       lowercase		:			int=0.0     ,       lowercase		:			Optional[int]=0.02     ,       lowercase		:			Optional[Any]=1E-12     ,       lowercase		:			Union[str, Any]=224     ,       lowercase		:			List[Any]=16     ,       lowercase		:			Optional[int]=3     ,       lowercase		:			Any=False     ,       lowercase		:			Dict=False     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False     ,       lowercase		:			str=0.1     ,       lowercase		:			List[Any]=0.1     ,       lowercase		:			str=True     ,       lowercase		:			Union[str, Any]=[3, 5, 7, 11]     ,       lowercase		:			Optional[int]=[1, 2, 3, 6]     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			Tuple=0.4     ,       lowercase		:			List[Any]=256     ,       lowercase		:			Optional[Any]=1     ,       lowercase		:			List[Any]=False     ,       lowercase		:			str=255     ,       **lowercase		:			Union[str, Any]     ,       ):



											'''simple docstring'''

											super().__init__(**lowercase       )

											UpperCAmelCase     =	vocab_size
											UpperCAmelCase     =	hidden_size
											UpperCAmelCase     =	num_hidden_layers
											UpperCAmelCase     =	num_attention_heads
											UpperCAmelCase     =	intermediate_size
											UpperCAmelCase     =	hidden_act
											UpperCAmelCase     =	hidden_dropout_prob
											UpperCAmelCase     =	attention_probs_dropout_prob
											UpperCAmelCase     =	initializer_range
											UpperCAmelCase     =	layer_norm_eps

											UpperCAmelCase     =	image_size
											UpperCAmelCase     =	patch_size
											UpperCAmelCase     =	num_channels
											UpperCAmelCase     =	use_mask_token
											UpperCAmelCase     =	use_absolute_position_embeddings
											UpperCAmelCase     =	use_relative_position_bias
											UpperCAmelCase     =	use_shared_relative_position_bias
											UpperCAmelCase     =	layer_scale_init_value
											UpperCAmelCase     =	drop_path_rate
											UpperCAmelCase     =	use_mean_pooling
											# decode head attributes (semantic segmentation)
											UpperCAmelCase     =	out_indices
											UpperCAmelCase     =	pool_scales
											# auxiliary head attributes (semantic segmentation)
											UpperCAmelCase     =	use_auxiliary_head
											UpperCAmelCase     =	auxiliary_loss_weight
											UpperCAmelCase     =	auxiliary_channels
											UpperCAmelCase     =	auxiliary_num_convs
											UpperCAmelCase     =	auxiliary_concat_input
											UpperCAmelCase     =	semantic_loss_ignore_index






class     _a						(			__a				):
							__a       :  Any            =      version.parse("""1.11"""				)
							@property
							def 					A   (			self		:			Dict       ):



											'''simple docstring'''

											return OrderedDict(
											    [
											        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
											    ]       )







							@property
							def 					A   (			self		:			Any       ):



											'''simple docstring'''

											return 1E-4










'''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}




