


"""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


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					"poolformer"



				def __init__(						self       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=16       ,		UpperCAmelCase__=16       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=4.0       ,		UpperCAmelCase__=[2, 2, 6, 2]       ,		UpperCAmelCase__=[64, 128, 320, 512]       ,		UpperCAmelCase__=[7, 3, 3, 3]       ,		UpperCAmelCase__=[4, 2, 2, 2]       ,		UpperCAmelCase__=[2, 1, 1, 1]       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=1e-5       ,		UpperCAmelCase__=0.02       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				num_channels
								SCREAMING_SNAKE_CASE__     							=				patch_size
								SCREAMING_SNAKE_CASE__     							=				stride
								SCREAMING_SNAKE_CASE__     							=				padding
								SCREAMING_SNAKE_CASE__     							=				pool_size
								SCREAMING_SNAKE_CASE__     							=				hidden_sizes
								SCREAMING_SNAKE_CASE__     							=				mlp_ratio
								SCREAMING_SNAKE_CASE__     							=				depths
								SCREAMING_SNAKE_CASE__     							=				patch_sizes
								SCREAMING_SNAKE_CASE__     							=				strides
								SCREAMING_SNAKE_CASE__     							=				num_encoder_blocks
								SCREAMING_SNAKE_CASE__     							=				drop_path_rate
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				use_layer_scale
								SCREAMING_SNAKE_CASE__     							=				layer_scale_init_value
								SCREAMING_SNAKE_CASE__     							=				initializer_range
								super().__init__(**UpperCAmelCase__     )


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					version.parse("1.11"   )



				@property
				def   lowerCAmelCase__		(						self     ):
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ]     )






				@property
				def   lowerCAmelCase__		(						self     ):
								return 2e-3




"""simple docstring"""






import os

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

import datasets


_lowerCamelCase     =  datasets.logging.get_logger(__name__)


_lowerCamelCase     =  '\\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     =  '\\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     =  '\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     =  {
    '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 			lowerCamelCase_		(			datasets.Metric   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# 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')."     )
												SCREAMING_SNAKE_CASE__     							=				"bleurt-base-128"

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

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






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.scorer.score(references=UpperCAmelCase__       ,		candidates=UpperCAmelCase__     )
								return {"scores": scores}
