



from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


__snake_case     :Any			       =logging.get_logger(__name__)

__snake_case     :str			       ={
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  Tuple						, __UpperCamelCase      :  Any=None						, __UpperCamelCase      :  List[Any]=None						, *__UpperCamelCase      :  Union[str, Any]						, **__UpperCamelCase      :  Optional[Any]       )		->     Tuple:
											super().__init__(*__UpperCamelCase						, **__UpperCamelCase       )

											if config is None:
																		assert isinstance(self.model						, __UpperCamelCase       ), (
																		    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																		    f''' {self.model.__class__}'''
																		)
																		A      						=    self.model.config
											else:
																		A      						=    config

											A      						=    data_args
											A      						=    self.config.tgt_vocab_size if isinstance(self.config						, __UpperCamelCase       ) else self.config.vocab_size

											if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
																		assert self.config.pad_token_id is not None, (
																		    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
																		    " calculation or doing label smoothing."
																		)

											if self.config.pad_token_id is None and self.config.eos_token_id is not None:
																		logger.warning(
																		    f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
																		    ' padding..'       )

											if self.args.label_smoothing == 0:
																		A      						=    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
											else:
																		# dynamically import label_smoothed_nll_loss
																		from utils import label_smoothed_nll_loss

																		A      						=    label_smoothed_nll_loss


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  int       )		->     str:
											if self.optimizer is None:
																		A      						=    ['bias', 'LayerNorm.weight']
																		A      						=    [
																		    {
																		        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
																		        'weight_decay': self.args.weight_decay,
																		    },
																		    {
																		        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
																		        'weight_decay': 0.0,
																		    },
																		]
																		A      						=    Adafactor if self.args.adafactor else AdamW
																		if self.args.adafactor:
																									A      						=    Adafactor
																									A      						=    {'scale_parameter': False, 'relative_step': False}
																		else:
																									A      						=    AdamW
																									A      						=    {
																									    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																									    'eps': self.args.adam_epsilon,
																									}
																		A      						=    self.args.learning_rate
																		if self.sharded_ddp:
																									A      						=    OSS(
																									    params=__UpperCamelCase						, optim=__UpperCamelCase						, **__UpperCamelCase						, )
																		else:
																									A      						=    optimizer_cls(__UpperCamelCase						, **__UpperCamelCase       )

											if self.lr_scheduler is None:
																		A      						=    self._get_lr_scheduler(__UpperCamelCase       )
											else:  # ignoring --lr_scheduler
																		logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'       )


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  List[Any]       )		->     Union[str, Any]:
											A      						=    arg_to_scheduler[self.args.lr_scheduler]
											if self.args.lr_scheduler == "constant":
																		A      						=    schedule_func(self.optimizer       )
											elif self.args.lr_scheduler == "constant_w_warmup":
																		A      						=    schedule_func(self.optimizer						, num_warmup_steps=self.args.warmup_steps       )
											else:
																		A      						=    schedule_func(
																		    self.optimizer						, num_warmup_steps=self.args.warmup_steps						, num_training_steps=__UpperCamelCase       )
											return scheduler


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[torch.utils.data.Sampler]:
											if isinstance(self.train_dataset						, torch.utils.data.IterableDataset       ):
																		return None
											elif is_torch_tpu_available():
																		return get_tpu_sampler(self.train_dataset       )
											else:
																		if self.args.sortish_sampler:
																									self.train_dataset.make_sortish_sampler(
																									    self.args.per_device_train_batch_size						, distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)						, )

																		return (
																		    RandomSampler(self.train_dataset       )
																		    if self.args.local_rank == -1
																		    else DistributedSampler(self.train_dataset       )
																		)


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Optional[int]       )		->     int:
											if self.args.label_smoothing == 0:
																		if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																									# force training to ignore pad token
																									A      						=    model(**__UpperCamelCase						, use_cache=__UpperCamelCase       )[0]
																									A      						=    self.loss_fn(logits.view(-1						, logits.shape[-1]       )						, labels.view(-1       )       )
																		else:
																									# compute usual loss via models
																									A						,   A      						=    model(**__UpperCamelCase						, labels=__UpperCamelCase						, use_cache=__UpperCamelCase       )[:2]
											else:
																		# compute label smoothed loss
																		A      						=    model(**__UpperCamelCase						, use_cache=__UpperCamelCase       )[0]
																		A      						=    torch.nn.functional.log_softmax(__UpperCamelCase						, dim=-1       )
																		A						,   A      						=    self.loss_fn(__UpperCamelCase						, __UpperCamelCase						, self.args.label_smoothing						, ignore_index=self.config.pad_token_id       )
											return loss, logits


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											A      						=    inputs.pop('labels'       )
											A						,   A      						=    self._compute_loss(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )
											return loss


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  nn.Module						, __UpperCamelCase      :  Dict[str, Union[torch.Tensor, Any]]						, __UpperCamelCase      :  bool						, __UpperCamelCase      :  Optional[List[str]] = None						, )		->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
											A      						=    self._prepare_inputs(__UpperCamelCase       )

											A      						=    {
											    'max_length': self.data_args.val_max_target_length
											    if self.data_args is not None
											    else self.config.max_length,
											    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
											}

											if self.args.predict_with_generate and not self.args.prediction_loss_only:
																		A      						=    self.model.generate(
																		    inputs['input_ids']						, attention_mask=inputs['attention_mask']						, **__UpperCamelCase						, )
																		# in case the batch is shorter than max length, the output should be padded
																		if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																									A      						=    self._pad_tensors_to_max_len(__UpperCamelCase						, gen_kwargs['max_length']       )

											A      						=    inputs.pop('labels'       )
											with torch.no_grad():
																		# compute loss on predict data
																		A						,   A      						=    self._compute_loss(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )

											A      						=    loss.mean().detach()
											if self.args.prediction_loss_only:
																		return (loss, None, None)

											A      						=    generated_tokens if self.args.predict_with_generate else logits

											if labels.shape[-1] < gen_kwargs["max_length"]:
																		A      						=    self._pad_tensors_to_max_len(__UpperCamelCase						, gen_kwargs['max_length']       )

											return (loss, logits, labels)


				def __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Dict       )		->     int:
											# If PAD token is not defined at least EOS token has to be defined
											A      						=    self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

											if pad_token_id is None:
																		raise ValueError(
																		    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
																		    f''' padded to `max_length`={max_length}'''       )

											A      						=    pad_token_id * torch.ones(
											    (tensor.shape[0], max_length)						, dtype=tensor.dtype						, device=tensor.device       )
											A      						=    tensor
											return padded_tensor





from scipy.stats import pearsonr

import datasets


__magic_name__  :  Union[str, Any]					=       """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


__magic_name__  :  Tuple					=       """
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__magic_name__  :  int					=       """
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION			)
class     SCREAMING_SNAKE_CASE__		(datasets.Metric			):
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       return datasets.MetricInfo(
           description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''float'''						),
                   '''references''': datasets.Value('''float'''						),
               }						)       ,						reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']       ,						)



     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict=False						):



       """simple docstring"""
       if return_pvalue:
         lowerCAmelCase__										=      pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)[0]						)}

