

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


UpperCAmelCase__	:       Any			      =      [nn.LayerNorm]

UpperCAmelCase__	:       List[str]			      =      logging.get_logger(__name__)

UpperCAmelCase__	:       Dict			      =      version.parse(version.parse(torch.__version__).base_version)

UpperCAmelCase__	:       List[str]			      =      parsed_torch_version_base >= version.parse("""2.0""")
UpperCAmelCase__	:       List[Any]			      =      parsed_torch_version_base >= version.parse("""1.12""")
UpperCAmelCase__	:       str			      =      parsed_torch_version_base >= version.parse("""1.11""")
UpperCAmelCase__	:       Union[str, Any]			      =      parsed_torch_version_base < version.parse("""1.11""")






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A  ,      self     )       ->  int:
      from torch import _softmax_backward_data

      if is_torch_less_than_1_11:
            return _softmax_backward_data(_A  ,      _A  ,      parent.dim  ,      self     )
      else:
            return _softmax_backward_data(_A  ,      _A  ,      parent.dim  ,      self.dtype     )






def    __lowercase (       _A  ,      _A  ,      _A = 0     )       ->  nn.Linear:
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				index.to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				layer.weight.index_select(_A  ,      _A     ).clone().detach()
      if layer.bias is not None:
            if dim == 1:
                  SCREAMING_SNAKE_CASE	: Tuple					       =				layer.bias.clone().detach()
            else:
                  SCREAMING_SNAKE_CASE	: Dict					       =				layer.bias[index].clone().detach()
      SCREAMING_SNAKE_CASE	: Tuple					       =				list(layer.weight.size()     )
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				nn.Linear(new_size[1]  ,      new_size[0]  ,      bias=layer.bias is not None     ).to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: str					       =				False
      new_layer.weight.copy_(W.contiguous()     )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				True
      if layer.bias is not None:
            SCREAMING_SNAKE_CASE	: Dict					       =				False
            new_layer.bias.copy_(b.contiguous()     )
            SCREAMING_SNAKE_CASE	: Tuple					       =				True
      return new_layer







class     a__    (				nn.Module    ):







  """simple docstring"""




  def __init__(     self      :	str			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Union[str, Any]						)   ->Optional[int]:
        """simple docstring"""







        super().__init__()
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				nf
        SCREAMING_SNAKE_CASE	: Dict					       =				nn.Parameter(torch.empty(UpperCAmelCase__			,			UpperCAmelCase__						)						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				nn.Parameter(torch.zeros(UpperCAmelCase__						)						)
        nn.init.normal_(self.weight			,			std=0.02						)




  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Optional[int]						)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				x.size()[:-1] + (self.nf,)
        SCREAMING_SNAKE_CASE	: List[str]					       =				torch.addmm(self.bias			,			x.view(-1			,			x.size(-1						)						)			,			self.weight						)
        SCREAMING_SNAKE_CASE	: str					       =				x.view(UpperCAmelCase__						)
        return x






def    __lowercase (       _A  ,      _A  ,      _A = 1     )       ->  ConvaD:
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				index.to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: List[str]					       =				layer.weight.index_select(_A  ,      _A     ).clone().detach()
      if dim == 0:
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				layer.bias.clone().detach()
      else:
            SCREAMING_SNAKE_CASE	: Dict					       =				layer.bias[index].clone().detach()
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				list(layer.weight.size()     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: Dict					       =				ConvaD(new_size[1]  ,      new_size[0]     ).to(layer.weight.device     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				False
      new_layer.weight.copy_(W.contiguous()     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				True
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				False
      new_layer.bias.copy_(b.contiguous()     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				True
      return new_layer






def    __lowercase (       _A  ,      _A  ,      _A = None     )       ->  Union[nn.Linear, ConvaD]:
      if isinstance(_A  ,      nn.Linear     ):
            return prune_linear_layer(_A  ,      _A  ,      dim=0 if dim is None else dim     )
      elif isinstance(_A  ,      _A     ):
            return prune_convad_layer(_A  ,      _A  ,      dim=1 if dim is None else dim     )
      else:
            raise ValueError(F"Can't prune layer of class {layer.__class__}"     )






def    __lowercase (       _A  ,      _A  ,      _A  ,      *_A     )       ->  torch.Tensor:
      assert len(_A     ) > 0, F"{input_tensors} has to be a tuple/list of tensors"

      # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				len(inspect.signature(_A     ).parameters     )
      if num_args_in_forward_chunk_fn != len(_A     ):
            raise ValueError(
                F"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_A     )} input "
                """tensors are given"""     )

      if chunk_size > 0:
            SCREAMING_SNAKE_CASE	: str					       =				input_tensors[0].shape[chunk_dim]
            for input_tensor in input_tensors:
                  if input_tensor.shape[chunk_dim] != tensor_shape:
                        raise ValueError(
                            F"All input tenors have to be of the same shape: {tensor_shape}, "
                            F"found shape {input_tensor.shape[chunk_dim]}"     )

            if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
                  raise ValueError(
                      F"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
                      F"size {chunk_size}"     )

            SCREAMING_SNAKE_CASE	: Optional[Any]					       =				input_tensors[0].shape[chunk_dim] // chunk_size

            # chunk input tensor into tuples
            SCREAMING_SNAKE_CASE	: Any					       =				tuple(input_tensor.chunk(_A  ,      dim=_A     ) for input_tensor in input_tensors     )
            # apply forward fn to every tuple
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				tuple(forward_fn(*_A     ) for input_tensors_chunk in zip(*_A     )     )
            # concatenate output at same dimension
            return torch.cat(_A  ,      dim=_A     )

      return forward_fn(*_A     )






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  Tuple[Set[int], torch.LongTensor]:
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				torch.ones(_A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				set(_A     ) - already_pruned_heads  # Convert to set and remove already pruned heads
      for head in heads:
            # Compute how many pruned heads are before the head and move the index accordingly
            SCREAMING_SNAKE_CASE	: List[str]					       =				head - sum(1 if h < head else 0 for h in already_pruned_heads     )
            SCREAMING_SNAKE_CASE	: Tuple					       =				0
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				mask.view(-1     ).contiguous().eq(1     )
      SCREAMING_SNAKE_CASE	: torch.LongTensor					       =				torch.arange(len(_A     )     )[mask].long()
      return heads, index






def    __lowercase (       *_A  ,      _A = None     )       ->  Tuple[torch.Tensor, ...]:
      return torch.meshgrid(*_A  ,      indexing=_A     )







def    __lowercase (       _A     )       ->  Tuple[torch.device, int, int]:
      return tensor.device, storage_ptr(_A     ), storage_size(_A     )







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

import datasets


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

UpperCAmelCase__	:       Union[str, Any]			      =      """\
FrugalScore 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.
"""


UpperCAmelCase__	:       str			      =      """
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION    )
class     a__    (				datasets.Metric    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        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 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







        if self.config_name == "default":
              SCREAMING_SNAKE_CASE	: int					       =				"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.config_name
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        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:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

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

        SCREAMING_SNAKE_CASE	: Dict					       =				raw_datasets.map(UpperCAmelCase__			,			batched=UpperCAmelCase__						)
        tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Trainer(self.model			,			UpperCAmelCase__			,			tokenizer=self.tokenizer						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(UpperCAmelCase__						)
        return {"scores": list(predictions.predictions.squeeze(-1						)						)}




