





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def 			lowerCAmelCase_			(				)  ->		Optional[Any]:
       def filter_log(UpperCamelCase_	):
              return False if "This IS expected if you are initializing" in record.msg else True

       UpperCamelCase_ 							=	datasets.utils.logging.get_logger("transformers.modeling_utils"	)
       logger.addFilter(UpperCamelCase_	)
       try:
              yield
       finally:
              logger.removeFilter(UpperCamelCase_	)


_UpperCAmelCase										=					'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

_UpperCAmelCase										=					'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

_UpperCAmelCase										=					'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'







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

       def lowercase			(		self:			Union[str, Any]		)							->			Dict:




              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION       , citation=_CITATION       , homepage="https://github.com/Tiiiger/bert_score"       , inputs_description=_KWARGS_DESCRIPTION       , features=datasets.Features(
                      {
                          "predictions": datasets.Value("string"       , id="sequence"		),
                          "references": datasets.Sequence(datasets.Value("string"       , id="sequence"		)       , id="references"		),
                      }		)       , codebase_urls=["https://github.com/Tiiiger/bert_score"]       , reference_urls=[
                      "https://github.com/Tiiiger/bert_score",
                      "https://arxiv.org/abs/1904.09675",
                  ]       , )

       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			List[Any]=None       , _SCREAMING_SNAKE_CASE:			str=False       , _SCREAMING_SNAKE_CASE:			int=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Tuple=4       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , _SCREAMING_SNAKE_CASE:			List[str]=False       , )							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	bert_score.utils.get_hash
              UpperCamelCase_ 							=	bert_score.BERTScorer

              if version.parse(bert_score.__version__		) >= version.parse("0.3.10"		):
                     UpperCamelCase_ 							=	functools.partial(_SCREAMING_SNAKE_CASE       , use_fast_tokenizer=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	functools.partial(_SCREAMING_SNAKE_CASE       , use_fast_tokenizer=_SCREAMING_SNAKE_CASE		)
              elif use_fast_tokenizer:
                     raise ImportWarning(
                         "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
                         "You can install it with `pip install \"bert-score>=0.3.10\"`."		)

              if model_type is None:
                     assert lang is not None, "either lang or model_type should be specified"
                     UpperCamelCase_ 							=	bert_score.utils.langamodel[lang.lower()]

              if num_layers is None:
                     UpperCamelCase_ 							=	bert_score.utils.modelalayers[model_type]

              UpperCamelCase_ 							=	get_hash(
                  model=_SCREAMING_SNAKE_CASE       , num_layers=_SCREAMING_SNAKE_CASE       , idf=_SCREAMING_SNAKE_CASE       , rescale_with_baseline=_SCREAMING_SNAKE_CASE       , use_custom_baseline=baseline_path is not None       , )

              with filter_logging_context():
                     if not hasattr(self       , "cached_bertscorer"		) or self.cached_bertscorer.hash != hashcode:
                            UpperCamelCase_ 							=	scorer(
                                model_type=_SCREAMING_SNAKE_CASE       , num_layers=_SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , nthreads=_SCREAMING_SNAKE_CASE       , all_layers=_SCREAMING_SNAKE_CASE       , idf=_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE       , lang=_SCREAMING_SNAKE_CASE       , rescale_with_baseline=_SCREAMING_SNAKE_CASE       , baseline_path=_SCREAMING_SNAKE_CASE       , )

              ((UpperCamelCase_)							,			(UpperCamelCase_)							,			(UpperCamelCase_)) 							=	self.cached_bertscorer.score(
                  cands=_SCREAMING_SNAKE_CASE       , refs=_SCREAMING_SNAKE_CASE       , verbose=_SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , )
              UpperCamelCase_ 							=	{
                  "precision": P.tolist(),
                  "recall": R.tolist(),
                  "f1": F.tolist(),
                  "hashcode": hashcode,
              }
              return output_dict

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			int=None       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Any:




              """simple docstring"""


              if references is not None:
                     UpperCamelCase_ 							=	[[ref] if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		) else ref for ref in references]
              super().add_batch(predictions=_SCREAMING_SNAKE_CASE       , references=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int=None       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , **_SCREAMING_SNAKE_CASE:			int		)							->			Any:




              """simple docstring"""


              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	[reference]
              super().add(prediction=_SCREAMING_SNAKE_CASE       , reference=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)



"""simple docstring"""







from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = (None, None)			,						_SCREAMING_SNAKE_CASE = None			,						):
			super().__init__(
			    model=_SCREAMING_SNAKE_CASE			,						args=_SCREAMING_SNAKE_CASE			,						data_collator=_SCREAMING_SNAKE_CASE			,						train_dataset=_SCREAMING_SNAKE_CASE			,						eval_dataset=_SCREAMING_SNAKE_CASE			,						tokenizer=_SCREAMING_SNAKE_CASE			,						model_init=_SCREAMING_SNAKE_CASE			,						compute_metrics=_SCREAMING_SNAKE_CASE			,						callbacks=_SCREAMING_SNAKE_CASE			,						optimizers=_SCREAMING_SNAKE_CASE			,						preprocess_logits_for_metrics=_SCREAMING_SNAKE_CASE			,						)

			# Override self.model.generation_config if a GenerationConfig is specified in args.
			# Priority: args.generation_config > model.generation_config > default GenerationConfig.
			if self.args.generation_config is not None:
				__lowerCAmelCase     :		Tuple		     = self.load_generation_config(self.args.generation_config					)
				__lowerCAmelCase     :		int		     = gen_config

		@staticmethod
		def 				__lowerCamelCase				( _SCREAMING_SNAKE_CASE					):

			# GenerationConfig provided, nothing to do
			if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
				return deepcopy(_SCREAMING_SNAKE_CASE					)

			# str or Path
			__lowerCAmelCase     :		Dict		     = Path(_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else gen_config_arg
			__lowerCAmelCase     :		Any		     = None

			# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
			# This step is required in order to determine config_file_name
			if pretrained_model_name.is_file():
				__lowerCAmelCase     :		List[Any]		     = pretrained_model_name.name
				__lowerCAmelCase     :		Tuple		     = pretrained_model_name.parent
			# dir path
			elif pretrained_model_name.is_dir():
				pass
			# model id or URL
			else:
				__lowerCAmelCase     :		List[Any]		     = gen_config_arg

			__lowerCAmelCase     :		Optional[int]		     = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			return gen_config

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "eval"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Any		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		Tuple		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Optional[int]		     = gen_kwargs

			return super().evaluate(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "test"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Dict		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Tuple		     = gen_kwargs

			return super().predict(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):

			if not self.args.predict_with_generate or prediction_loss_only:
				return super().prediction_step(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						prediction_loss_only=_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = 'labels' in inputs
			__lowerCAmelCase     :		List[str]		     = self._prepare_inputs(_SCREAMING_SNAKE_CASE					)

			# XXX: adapt synced_gpus for fairscale as well
			# Priority (handled in generate):
			# gen_kwargs > model.generation_config > default GenerationConfig()

			if len(_SCREAMING_SNAKE_CASE					) == 0 and hasattr(self			,						'_gen_kwargs'					):
				__lowerCAmelCase     :		Dict		     = self._gen_kwargs.copy()

			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Tuple		     = self.model.config.max_length
			__lowerCAmelCase     :		Optional[Any]		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.model.config.num_beams
			)
			__lowerCAmelCase     :		Union[str, Any]		     = True if is_deepspeed_zeroa_enabled() else False
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'					) is not None else default_synced_gpus
			)

			# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
			# (otherwise, it would continue generating from the padded `decoder_input_ids`)
			if (
			    "labels" in inputs
			    and "decoder_input_ids" in inputs
			    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
			):
				__lowerCAmelCase     :		Optional[int]		     = {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
			__lowerCAmelCase     :		Union[str, Any]		     = self.model.generate(**_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

			# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
			# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
			# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
			if self.model.generation_config._from_model_config:
				__lowerCAmelCase     :		str		     = False

			# Retrieves GenerationConfig from model.generation_config
			__lowerCAmelCase     :		List[Any]		     = self.model.generation_config
			# in case the batch is shorter than max length, the output should be padded
			if generated_tokens.shape[-1] < gen_config.max_length:
				__lowerCAmelCase     :		Any		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
			elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
				__lowerCAmelCase     :		Optional[Any]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)

			with torch.no_grad():
				if has_labels:
					with self.compute_loss_context_manager():
						__lowerCAmelCase     :		List[Any]		     = model(**_SCREAMING_SNAKE_CASE					)
					if self.label_smoother is not None:
						__lowerCAmelCase     :		Optional[int]		     = self.label_smoother(_SCREAMING_SNAKE_CASE			,						inputs['labels']					).mean().detach()
					else:
						__lowerCAmelCase     :		Optional[int]		     = (outputs['loss'] if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else outputs[0]).mean().detach()
				else:
					__lowerCAmelCase     :		Optional[int]		     = None

			if self.args.prediction_loss_only:
				return loss, None, None

			if has_labels:
				__lowerCAmelCase     :		Tuple		     = inputs['labels']
				if labels.shape[-1] < gen_config.max_length:
					__lowerCAmelCase     :		List[str]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
				elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
					__lowerCAmelCase     :		int		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)
			else:
				__lowerCAmelCase     :		Optional[int]		     = None

			return loss, generated_tokens, labels







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if self.tokenizer is not None and hasattr(self.tokenizer			,						'pad_token_id'					):
				# If PAD token is not defined at least EOS token has to be defined
				__lowerCAmelCase     :		Any		     = (
				    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
				)
			else:
				if self.model.config.pad_token_id is not None:
					__lowerCAmelCase     :		List[str]		     = self.model.config.pad_token_id
				else:
					raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'					)

			__lowerCAmelCase     :		Dict		     = pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)			,						dtype=tensor.dtype			,						device=tensor.device					)
			__lowerCAmelCase     :		Tuple		     = tensor
			return padded_tensor