


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


SCREAMING_SNAKE_CASE__     					=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			List[Any]       			=  """albert"""






		def __init__(						self				:     int   ,  UpperCAmelCase				:     Tuple=3_00_00   ,  UpperCAmelCase				:     int=1_28   ,  UpperCAmelCase				:     Optional[Any]=40_96   ,  UpperCAmelCase				:     List[str]=12   ,  UpperCAmelCase				:     Optional[Any]=1   ,  UpperCAmelCase				:     Union[str, Any]=64   ,  UpperCAmelCase				:     str=1_63_84   ,  UpperCAmelCase				:     List[str]=1   ,  UpperCAmelCase				:     Dict="gelu_new"   ,  UpperCAmelCase				:     Optional[Any]=0   ,  UpperCAmelCase				:     Dict=0   ,  UpperCAmelCase				:     int=5_12   ,  UpperCAmelCase				:     Optional[Any]=2   ,  UpperCAmelCase				:     int=0.02   ,  UpperCAmelCase				:     int=1E-12   ,  UpperCAmelCase				:     Dict=0.1   ,  UpperCAmelCase				:     Tuple="absolute"   ,  UpperCAmelCase				:     Any=0   ,  UpperCAmelCase				:     int=2   ,  UpperCAmelCase				:     List[Any]=3   ,  **UpperCAmelCase				:     Optional[int]   ,  ):
				super().__init__(pad_token_id=UpperCAmelCase   ,  bos_token_id=UpperCAmelCase   ,  eos_token_id=UpperCAmelCase   ,  **UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						vocab_size
				SCREAMING_SNAKE_CASE_				:str				     =						embedding_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						hidden_size
				SCREAMING_SNAKE_CASE_				:Dict				     =						num_hidden_layers
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						num_hidden_groups
				SCREAMING_SNAKE_CASE_				:Tuple				     =						num_attention_heads
				SCREAMING_SNAKE_CASE_				:str				     =						inner_group_num
				SCREAMING_SNAKE_CASE_				:List[str]				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						intermediate_size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_				:Tuple				     =						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						max_position_embeddings
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						type_vocab_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						layer_norm_eps
				SCREAMING_SNAKE_CASE_				:List[str]				     =						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						position_embedding_type




class 						_UpperCAmelCase      (     lowercase							):






		@property
		def _snake_case		(						self				:     List[Any]):
				if self.task == "multiple-choice":
						SCREAMING_SNAKE_CASE_				:Tuple				     =						{0: "batch", 1: "choice", 2: "sequence"}
				else:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ])










import os

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

import datasets


SCREAMING_SNAKE_CASE__     					=			datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     					=			"\\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"

SCREAMING_SNAKE_CASE__     					=			"\\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"

SCREAMING_SNAKE_CASE__     					=			"\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"

SCREAMING_SNAKE_CASE__     					=			{
    "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 						_UpperCAmelCase      (     datasets.Metric							):






		def _snake_case		(						self				:     List[str]):
				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 _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Any):
				# 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_				:str				     =						"bleurt-base-128"

				if self.config_name.lower() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						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_				:Optional[Any]				     =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
				SCREAMING_SNAKE_CASE_				:Dict				     =						score.BleurtScorer(os.path.join(UpperCAmelCase   ,  UpperCAmelCase))







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.scorer.score(references=UpperCAmelCase   ,  candidates=UpperCAmelCase)
				return {"scores": scores}






