


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
	from ...onnx.config import PatchingSpec
	from ...tokenization_utils_base import PreTrainedTokenizerBase


a : int		=				logging.get_logger(__name__)

a : List[Any]		=				{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"longformer"






					def __init__(				self ,       snake_case_ = 5_1_2 ,       snake_case_ = 2 ,       snake_case_ = 1 ,       snake_case_ = 0 ,       snake_case_ = 2 ,       snake_case_ = 3_0_5_2_2 ,       snake_case_ = 7_6_8 ,       snake_case_ = 1_2 ,       snake_case_ = 1_2 ,       snake_case_ = 3_0_7_2 ,       snake_case_ = "gelu" ,       snake_case_ = 0.1 ,       snake_case_ = 0.1 ,       snake_case_ = 5_1_2 ,       snake_case_ = 2 ,       snake_case_ = 0.0_2 ,       snake_case_ = 1e-1_2 ,       snake_case_ = False ,       **snake_case_ ,       )   ->			int:







								'''simple docstring'''

								super().__init__(pad_token_id=snake_case_ ,       **snake_case_						)

								__lowercase 					=   attention_window
								__lowercase 					=   sep_token_id
								__lowercase 					=   bos_token_id
								__lowercase 					=   eos_token_id
								__lowercase 					=   vocab_size
								__lowercase 					=   hidden_size
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   hidden_act
								__lowercase 					=   intermediate_size
								__lowercase 					=   hidden_dropout_prob
								__lowercase 					=   attention_probs_dropout_prob
								__lowercase 					=   max_position_embeddings
								__lowercase 					=   type_vocab_size
								__lowercase 					=   initializer_range
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   onnx_export




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ = "default" ,       snake_case_ = None						)   ->			Union[str, Any]:







								'''simple docstring'''

								super().__init__(snake_case_ ,       snake_case_ ,       snake_case_						)
								__lowercase 					=   True






					@property
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								if self.task == "multiple-choice":
											__lowercase 					=   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											__lowercase 					=   {0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								        ('''global_attention_mask''', dynamic_axis),
								    ]						)






					@property
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								__lowercase 					=   super().outputs
								if self.task == "default":
											__lowercase 					=   {0: '''batch'''}
								return outputs






					@property
					def A      (				self						)   ->			float:







								'''simple docstring'''

								return 1e-4






					@property
					def A      (				self						)   ->			int:







								'''simple docstring'''

								return max(super().default_onnx_opset ,       1_4						)






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = False ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

								__lowercase 					=   super().generate_dummy_inputs(
								    preprocessor=snake_case_ ,       batch_size=snake_case_ ,       seq_length=snake_case_ ,       is_pair=snake_case_ ,       framework=snake_case_						)
								import torch

								# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
								# makes the export fail randomly
								__lowercase 					=   torch.zeros_like(inputs['''input_ids''']						)
								# make every second token global
								__lowercase 					=   1

								return inputs






import os

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

import datasets


a : Optional[int]		=				datasets.logging.get_logger(__name__)


a : Tuple		=				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

a : Union[str, Any]		=				'''\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

a : Union[str, Any]		=				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

a : Tuple		=				{
    '''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 A      (				self						)   ->			Union[str, Any]:







								'''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 ,       snake_case_						)   ->			List[str]:







								'''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\').'''						)
											__lowercase 					=   '''bleurt-base-128'''

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

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






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

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


