





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

import datasets


_UpperCAmelCase										=					'\\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'

_UpperCAmelCase										=					'\\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'


_UpperCAmelCase										=					'\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 				_UpperCamelCase       (						datasets.Metric  ):

       def lowercase			(		self:			List[Any]		)							->			Tuple:




              """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:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			str:




              """simple docstring"""


              if self.config_name == "default":
                     UpperCamelCase_ 							=	"moussaKam/frugalscore_tiny_bert-base_bert-score"
              else:
                     UpperCamelCase_ 							=	self.config_name
              UpperCamelCase_ 							=	AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Dict=32       , _SCREAMING_SNAKE_CASE:			str=128       , _SCREAMING_SNAKE_CASE:			Any=None       , )							->			Any:




              """simple docstring"""


              assert len(_SCREAMING_SNAKE_CASE		) == len(
                  _SCREAMING_SNAKE_CASE		), "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=_SCREAMING_SNAKE_CASE       , report_to="all"       , no_cuda=(device == "cpu")       , log_level="warning"       , )
              UpperCamelCase_ 							=	{"sentence1": predictions, "sentence2": references}
              UpperCamelCase_ 							=	datasets.Dataset.from_dict(_SCREAMING_SNAKE_CASE		)

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

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






'''simple docstring'''



import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_lowercase       :						Dict						=logging.get_logger(__name__)

if is_vision_available():
				import PIL.Image
				from PIL import Image
				from PIL.Image import Image as ImageType
else:
				_lowercase       :						List[Any]						=object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    value



			def __str__(     self		:						int			)      ->      str:
							"""simple docstring"""





							return self.to_string()



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[str]:
							"""simple docstring"""





							logger.error(
							    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
							return self._value







			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      str:
							"""simple docstring"""





							logger.error(
							    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
							return str(self._value			)






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return self._value







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





							return self._value






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

							if not is_vision_available():
											raise ImportError('''PIL must be installed in order to handle images.'''			)

							snake_case__      :   str      =    None
							snake_case__      :   List[Any]      =    None
							snake_case__      :   Tuple      =    None

							if isinstance(__UpperCamelCase    ,			__UpperCamelCase			):
											snake_case__      :   List[Any]      =    value
							elif isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   Union[str, Any]      =    value
							else:
											raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Any=None			)      ->      List[Any]:
							"""simple docstring"""





							from IPython.display import Image, display

							display(Image(self.to_string()			)			)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





							if self._raw is not None:
											return self._raw

							if self._path is not None:
											snake_case__      :   str      =    Image.open(self._path			)
											return self._raw







			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._raw is not None:
											snake_case__      :   Any      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)
											self._raw.save(self._path			)

											return self._path

							if self._tensor is not None:
											snake_case__      :   List[str]      =    self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											snake_case__      :   List[Any]      =    Image.fromarray((array * 255).astype(np.uinta			)			)

											snake_case__      :   str      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)

											img.save(self._path			)

											return self._path






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str=16000			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

							if not is_soundfile_availble():
											raise ImportError('''soundfile must be installed in order to handle audio.'''			)

							snake_case__      :   List[str]      =    None
							snake_case__      :   Optional[int]      =    None

							snake_case__      :   Any      =    samplerate

							if isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   int      =    value
							else:
											raise ValueError(F'''Unsupported audio type: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Any:
							"""simple docstring"""





							from IPython.display import Audio, display

							display(Audio(self.to_string()    ,			rate=self.samplerate			)			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							if self._tensor is not None:
											return self._tensor

							if self._path is not None:
											snake_case__  ,      snake_case__      :   Tuple      =    sf.read(self._path			)
											snake_case__      :   Tuple      =    torch.tensor(__UpperCamelCase			)
											return self._tensor







			def 				lowerCAmelCase (     self		:						Any			)      ->      Dict:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._tensor is not None:
											snake_case__      :   Tuple      =    tempfile.mkdtemp()
											snake_case__      :   int      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.wav'''			)
											sf.write(self._path    ,			self._tensor    ,			samplerate=self.samplerate			)
											return self._path



_lowercase       :						Union[str, Any]						={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase       :						List[Any]						={str: AgentText}

if is_vision_available():
				_lowercase       :						Optional[Any]						=AgentImage


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Optional[int]   ,				**UpperCamelCase__				:List[Any]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    [(arg.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else arg) for arg in args]
				snake_case__      :   Any      =    {k: (v.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else v) for k, v in kwargs.items()}
				return args, kwargs



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple=None			)		->						Dict:
				if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								snake_case__      :   Any      =    {}
								for i, (k, v) in enumerate(outputs.items()			):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				snake_case__      :   List[Any]      =    AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)
																else:
																				snake_case__      :   Optional[int]      =    AgentType(UpperCamelCase__			)

												else:
																# If the class does not have defined output, then we map according to the type
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								snake_case__      :   str      =    _v(UpperCamelCase__			)
																if k not in decoded_outputs:
																				snake_case__      :   Any      =    AgentType[v]

				elif isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    type(UpperCamelCase__			)()
								for i, v in enumerate(UpperCamelCase__			):
												if output_types is not None:
																# If the class has defined outputs, we can map directly according to the class definition
																if output_types[i] in AGENT_TYPE_MAPPING:
																				decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)			)
																else:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)
												else:
																# If the class does not have defined output, then we map according to the type
																snake_case__      :   Any      =    False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								decoded_outputs.append(_v(UpperCamelCase__			)			)
																								snake_case__      :   str      =    True

																if not found:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)

				else:
								if output_types[0] in AGENT_TYPE_MAPPING:
												# If the class has defined outputs, we can map directly according to the class definition
												snake_case__      :   Tuple      =    AGENT_TYPE_MAPPING[output_types[0]](UpperCamelCase__			)

								else:
												# If the class does not have defined output, then we map according to the type
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																if isinstance(UpperCamelCase__   ,				_k			):
																				return _v(UpperCamelCase__			)
												return AgentType(UpperCamelCase__			)

				return decoded_outputs




