


'''simple docstring'''







import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


a__    :   Optional[int]								=			'\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

a__    :   str								=			'\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


a__    :   Optional[Any]								=			'\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric(\"frugalscore\")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results[\"scores\"]])\n    [0.631, 0.645]\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,		_KWARGS_DESCRIPTION  )
class    lowercase_	(       datasets.Metric  ):


    def   __a				(	self       ):
          return datasets.MetricInfo(
              description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"       ),
                      "references": datasets.Value("string"       ),
                  }       )    ,   homepage="https://github.com/moussaKam/FrugalScore"    ,   )


    def   __a				(	self    ,   a       ):
          if self.config_name == "default":
                UpperCamelCase__      =							"moussaKam/frugalscore_tiny_bert-base_bert-score"
          else:
                UpperCamelCase__      =							self.config_name
          UpperCamelCase__      =							AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase       )
          UpperCamelCase__      =							AutoTokenizer.from_pretrained(__UpperCAmelCase       )


    def   __a				(	self    ,   a    ,   a    ,   a=32    ,   a=1_28    ,   a=None    ,   ):
          assert len(__UpperCAmelCase       ) == len(
              __UpperCAmelCase       ), "predictions and references should have the same number of sentences."
          if device is not None:
                assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
          else:
                UpperCamelCase__      =							"gpu" if torch.cuda.is_available() else "cpu"
          UpperCamelCase__      =							TrainingArguments(
              "trainer"    ,   fpaa=(device == "gpu")    ,   per_device_eval_batch_size=__UpperCAmelCase    ,   report_to="all"    ,   no_cuda=(device == "cpu")    ,   log_level="warning"    ,   )
          UpperCamelCase__      =							{"sentence1": predictions, "sentence2": references}
          UpperCamelCase__      =							datasets.Dataset.from_dict(__UpperCAmelCase       )

          def tokenize_function(a       ):
                return self.tokenizer(
                    data["sentence1"]    ,   data["sentence2"]    ,   max_length=__UpperCAmelCase    ,   truncation=__UpperCAmelCase    ,   padding=__UpperCAmelCase       )

          UpperCamelCase__      =							raw_datasets.map(__UpperCAmelCase    ,   batched=__UpperCAmelCase       )
          tokenized_datasets.remove_columns(["sentence1", "sentence2"]       )
          UpperCamelCase__      =							Trainer(self.model    ,   __UpperCAmelCase    ,   tokenizer=self.tokenizer       )
          UpperCamelCase__      =							trainer.predict(__UpperCAmelCase       )
          return {"scores": list(predictions.predictions.squeeze(-1       )       )}






"""simple docstring"""




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    List[str]    				=		'blenderbot'
						lowerCAmelCase__	:    Union[str, Any]    				=		['past_key_values']
						lowerCAmelCase__	:    Optional[Any]    				=		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





						def __init__(							self		,__UpperCAmelCase=80_08		,__UpperCAmelCase=1_28		,__UpperCAmelCase=2		,__UpperCAmelCase=1_02_40		,__UpperCAmelCase=32		,__UpperCAmelCase=24		,__UpperCAmelCase=1_02_40		,__UpperCAmelCase=32		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=25_60		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1		,__UpperCAmelCase=False		,__UpperCAmelCase=0		,__UpperCAmelCase=1		,__UpperCAmelCase=2		,__UpperCAmelCase=3		,__UpperCAmelCase=2		,**__UpperCAmelCase		,)       ->					Any:
													A__						  =				vocab_size
													A__						  =				max_position_embeddings
													A__						  =				d_model
													A__						  =				encoder_ffn_dim
													A__						  =				encoder_layers
													A__						  =				encoder_attention_heads
													A__						  =				decoder_ffn_dim
													A__						  =				decoder_layers
													A__						  =				decoder_attention_heads
													A__						  =				dropout
													A__						  =				attention_dropout
													A__						  =				activation_dropout
													A__						  =				activation_function
													A__						  =				init_std
													A__						  =				encoder_layerdrop
													A__						  =				decoder_layerdrop
													A__						  =				use_cache
													A__						  =				encoder_layers
													A__						  =				scale_embedding  # scale factor will be sqrt(d_model) if True

													super().__init__(
													    pad_token_id=__UpperCAmelCase		,bos_token_id=__UpperCAmelCase		,eos_token_id=__UpperCAmelCase		,is_encoder_decoder=__UpperCAmelCase		,decoder_start_token_id=__UpperCAmelCase		,encoder_no_repeat_ngram_size=__UpperCAmelCase		,forced_eos_token_id=__UpperCAmelCase		,**__UpperCAmelCase		,)






class      UpperCamelCase__(      __A			):





						@property
						def 					snake_case__						(							self  )       ->					Mapping[str, Mapping[int, str]]:
													if self.task in ["default", "seq2seq-lm"]:
																				A__						  =				OrderedDict(
																				    [
																				        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																				        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																				    ]  )
																				if self.use_past:
																											A__						  =				{0: 'batch'}
																											A__						  =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
																				else:
																											A__						  =				{0: 'batch', 1: 'decoder_sequence'}
																											A__						  =				{0: 'batch', 1: 'decoder_sequence'}
																				if self.use_past:
																											self.fill_with_past_key_values_(__UpperCAmelCase		,direction='inputs'  )
													elif self.task == "causal-lm":
																				A__						  =				OrderedDict(
																				    [
																				        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																				        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																				    ]  )
																				if self.use_past:
																											A__  ,	A__						  =				self.num_layers
																											for i in range(__UpperCAmelCase  ):
																																		A__						  =				{0: 'batch', 2: 'past_sequence + sequence'}
																																		A__						  =				{0: 'batch', 2: 'past_sequence + sequence'}
													else:
																				A__						  =				OrderedDict(
																				    [
																				        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																				        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																				        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
																				        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
																				    ]  )

													return common_inputs





						@property
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
						def 					snake_case__						(							self  )       ->					Mapping[str, Mapping[int, str]]:
													if self.task in ["default", "seq2seq-lm"]:
																				A__						  =				super().outputs
													else:
																				A__						  =				super(__UpperCAmelCase		,self  ).outputs
																				if self.use_past:
																											A__  ,	A__						  =				self.num_layers
																											for i in range(__UpperCAmelCase  ):
																																		A__						  =				{0: 'batch', 2: 'past_sequence + sequence'}
																																		A__						  =				{0: 'batch', 2: 'past_sequence + sequence'}
													return common_outputs





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = -1		,__UpperCAmelCase = -1		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,)       ->					Mapping[str, Any]:
													A__						  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )
													# Generate decoder inputs
													A__						  =				seq_length if not self.use_past else 1
													A__						  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )
													A__						  =				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
													A__						  =				dict(**__UpperCAmelCase		,**__UpperCAmelCase  )

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
																				else:
																											import torch
																				A__  ,	A__						  =				common_inputs['input_ids'].shape
																				A__						  =				common_inputs['decoder_input_ids'].shape[1]
																				A__  ,	A__						  =				self.num_attention_heads
																				A__						  =				(
																				    batch,
																				    num_encoder_attention_heads,
																				    encoder_seq_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)
																				A__						  =				decoder_seq_length
																				A__						  =				(
																				    batch,
																				    num_decoder_attention_heads,
																				    decoder_past_length,
																				    self._config.hidden_size // num_decoder_attention_heads,
																				)
																				A__						  =				torch.cat(
																				    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCAmelCase		,__UpperCAmelCase  )]		,dim=1  )
																				A__						  =				[]
																				A__  ,	A__						  =				self.num_layers

																				for _ in range(__UpperCAmelCase  ):
																											common_inputs["past_key_values"].append(
																											    (
																											        torch.zeros(__UpperCAmelCase  ),
																											        torch.zeros(__UpperCAmelCase  ),
																											        torch.zeros(__UpperCAmelCase  ),
																											        torch.zeros(__UpperCAmelCase  ),
																											    )  )
													return common_inputs





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = -1		,__UpperCAmelCase = -1		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,)       ->					Mapping[str, Any]:
													A__						  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
																				else:
																											import torch
																				A__  ,	A__						  =				common_inputs['input_ids'].shape
																				A__						  =				seqlen
																				A__  ,	A__						  =				self.num_layers
																				A__  ,	A__						  =				self.num_attention_heads
																				A__						  =				(
																				    batch,
																				    num_encoder_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)
																				A__						  =				common_inputs['attention_mask'].dtype
																				A__						  =				torch.cat(
																				    [common_inputs['attention_mask'], torch.ones(__UpperCAmelCase		,__UpperCAmelCase		,dtype=__UpperCAmelCase  )]		,dim=1  )
																				A__						  =				[
																				    (torch.zeros(__UpperCAmelCase  ), torch.zeros(__UpperCAmelCase  )) for _ in range(__UpperCAmelCase  )
																				]
													return common_inputs





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = -1		,__UpperCAmelCase = -1		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,)       ->					Mapping[str, Any]:
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													A__						  =				compute_effective_axis_dimension(
													    __UpperCAmelCase		,fixed_dimension=OnnxConfig.default_fixed_batch		,num_token_to_add=0  )

													# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
													A__						  =				tokenizer.num_special_tokens_to_add(__UpperCAmelCase  )
													A__						  =				compute_effective_axis_dimension(
													    __UpperCAmelCase		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=__UpperCAmelCase  )

													# Generate dummy inputs according to compute batch and sequence
													A__						  =				[' '.join([tokenizer.unk_token]  ) * seq_length] * batch_size
													A__						  =				dict(tokenizer(__UpperCAmelCase		,return_tensors=__UpperCAmelCase  )  )
													return common_inputs





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = -1		,__UpperCAmelCase = -1		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,)       ->					Mapping[str, Any]:
													if self.task in ["default", "seq2seq-lm"]:
																				A__						  =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																				    __UpperCAmelCase		,batch_size=__UpperCAmelCase		,seq_length=__UpperCAmelCase		,is_pair=__UpperCAmelCase		,framework=__UpperCAmelCase  )

													elif self.task == "causal-lm":
																				A__						  =				self._generate_dummy_inputs_for_causal_lm(
																				    __UpperCAmelCase		,batch_size=__UpperCAmelCase		,seq_length=__UpperCAmelCase		,is_pair=__UpperCAmelCase		,framework=__UpperCAmelCase  )
													else:
																				A__						  =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																				    __UpperCAmelCase		,batch_size=__UpperCAmelCase		,seq_length=__UpperCAmelCase		,is_pair=__UpperCAmelCase		,framework=__UpperCAmelCase  )

													return common_inputs





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Tuple:
													if self.task in ["default", "seq2seq-lm"]:
																				A__						  =				super()._flatten_past_key_values_(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )
													else:
																				A__						  =				super(__UpperCAmelCase		,self  )._flatten_past_key_values_(
																				    __UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					int:
													if direction not in ["inputs", "outputs"]:
																				raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''  )

													A__						  =				'past_key_values' if direction == 'inputs' else 'present'
													A__  ,	A__						  =				self.num_layers

													A__						  =				'past_encoder_sequence'
													A__						  =				'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

													for i in range(__UpperCAmelCase  ):
																				A__						  =				{0: 'batch', 2: decoder_sequence}
																				A__						  =				{0: 'batch', 2: decoder_sequence}
																				A__						  =				{0: 'batch', 2: encoder_sequence}
																				A__						  =				{0: 'batch', 2: encoder_sequence}





