




'''simple docstring'''


from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case   :   Any     							=	logging.get_logger(__name__)

_snake_case   :   List[str]     							=	{
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}

class     A					(	_a					):
							lowercase_           =       'xlm-prophetnet'
							lowercase_           =       ['past_key_values']
							lowercase_           =       {
							    'num_attention_heads': 'num_encoder_attention_heads',
							}







							def __init__(						self :						List[str]							,    lowerCAmelCase_ :						Optional[float] = 0.1							,    lowerCAmelCase_ :						Optional[Union[str, Callable]] = "gelu"							,    lowerCAmelCase_ :						Optional[int] = 3_05_22							,    lowerCAmelCase_ :						Optional[int] = 10_24							,    lowerCAmelCase_ :						Optional[int] = 40_96							,    lowerCAmelCase_ :						Optional[int] = 12							,    lowerCAmelCase_ :						Optional[int] = 16							,    lowerCAmelCase_ :						Optional[int] = 40_96							,    lowerCAmelCase_ :						Optional[int] = 12							,    lowerCAmelCase_ :						Optional[int] = 16							,    lowerCAmelCase_ :						Optional[float] = 0.1							,    lowerCAmelCase_ :						Optional[float] = 0.1							,    lowerCAmelCase_ :						Optional[int] = 5_12							,    lowerCAmelCase_ :						Optional[float] = 0.0_2							,    lowerCAmelCase_ :						Optional[bool] = True							,    lowerCAmelCase_ :						Optional[bool] = True							,    lowerCAmelCase_ :						Optional[int] = 0							,    lowerCAmelCase_ :						Optional[int] = 2							,    lowerCAmelCase_ :						Optional[int] = 32							,    lowerCAmelCase_ :						Optional[int] = 1_28							,    lowerCAmelCase_ :						Optional[bool] = False							,    lowerCAmelCase_ :						Optional[float] = 0.0							,    lowerCAmelCase_ :						Optional[bool] = True							,    lowerCAmelCase_ :						Optional[int] = 0							,    lowerCAmelCase_ :						Optional[int] = 1							,    lowerCAmelCase_ :						Optional[int] = 2							,    **lowerCAmelCase_ :						int							,    )    ->							Any:


														"""simple docstring"""


														_a			  =       vocab_size
														_a			  =       hidden_size
														_a			  =       encoder_ffn_dim
														_a			  =       num_encoder_layers
														_a			  =       num_encoder_attention_heads
														_a			  =       decoder_ffn_dim
														_a			  =       num_decoder_layers
														_a			  =       num_decoder_attention_heads
														_a			  =       max_position_embeddings
														_a			  =       init_std  # Normal(0, this parameter)
														_a			  =       activation_function

														# parameters for xlmprophetnet
														_a			  =       ngram
														_a			  =       num_buckets
														_a			  =       relative_max_distance
														_a			  =       disable_ngram_loss
														_a			  =       eps

														# 3 Types of Dropout
														_a			  =       attention_dropout
														_a			  =       activation_dropout
														_a			  =       dropout

														_a			  =       use_cache

														super().__init__(
														    pad_token_id=lowerCAmelCase_							,    bos_token_id=lowerCAmelCase_							,    eos_token_id=lowerCAmelCase_							,    is_encoder_decoder=lowerCAmelCase_							,    add_cross_attention=lowerCAmelCase_							,    decoder_start_token_id=lowerCAmelCase_							,    **lowerCAmelCase_							,    )







							@property
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							int:


														"""simple docstring"""


														return self.num_encoder_layers + self.num_decoder_layers







							@num_hidden_layers.setter
							def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						Any      )    ->							Union[str, Any]:


														"""simple docstring"""


														raise NotImplementedError(
														    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
														    ''' `num_decoder_layers`.'''      )







'''simple docstring'''


import os

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

import datasets


_snake_case   :   Any     							=	datasets.logging.get_logger(__name__)


_snake_case   :   Tuple     							=	'\\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'

_snake_case   :   Optional[Any]     							=	'\\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'

_snake_case   :   Optional[int]     							=	'\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'

_snake_case   :   Dict     							=	{
    '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      __lowerCAmelCase    (						self :						int      )    ->							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      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str      )    ->							Tuple:


														"""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\').'''      )
																					_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(lowerCAmelCase_							,    lowerCAmelCase_      )      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[int]:


														"""simple docstring"""


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

