





import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


_snake_case  :   List[str]						   =  logging.getLogger(__name__)



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""token-classification"""






  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[int]:
     if type(lowerCAmelCase_					) == dict:
        __lowerCAmelCase							     =     Namespace(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     import_module('tasks'					)
     try:
        __lowerCAmelCase							     =     getattr(lowerCAmelCase_ ,  hparams.task_type					)
        __lowerCAmelCase							     =     token_classification_task_clazz()
     except AttributeError:
        raise ValueError(
            f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
            f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""					)
     __lowerCAmelCase							     =     self.token_classification_task.get_labels(hparams.labels					)
     __lowerCAmelCase							     =     CrossEntropyLoss().ignore_index
     super().__init__(lowerCAmelCase_ ,  len(self.labels					) ,  self.mode					)






  def     lowercase		(    self			:		str ,  **lowerCAmelCase_			:		Any					)							->						Union[str, Any]:
     return self.model(**lowerCAmelCase_					)






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Tuple:
     __lowerCAmelCase							     =     {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
     if self.config.model_type != "distilbert":
        __lowerCAmelCase							     =     (
            batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
        )  # XLM and RoBERTa don"t use token_type_ids

     __lowerCAmelCase							     =     self(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     outputs[0]
     # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
     return {"loss": loss}






  def     lowercase		(    self			:		Optional[int]					)							->						List[Any]:
     __lowerCAmelCase							     =     self.hparams
     for mode in ["train", "dev", "test"]:
        __lowerCAmelCase							     =     self._feature_file(lowerCAmelCase_					)
        if os.path.exists(lowerCAmelCase_					) and not args.overwrite_cache:
           logger.info('Loading features from cached file %s' ,  lowerCAmelCase_					)
           __lowerCAmelCase							     =     torch.load(lowerCAmelCase_					)
        else:
           logger.info('Creating features from dataset file at %s' ,  args.data_dir					)
           __lowerCAmelCase							     =     self.token_classification_task.read_examples_from_file(args.data_dir ,  lowerCAmelCase_					)
           __lowerCAmelCase							     =     self.token_classification_task.convert_examples_to_features(
               lowerCAmelCase_ ,  self.labels ,  args.max_seq_length ,  self.tokenizer ,  cls_token_at_end=bool(self.config.model_type in ['xlnet']					) ,  cls_token=self.tokenizer.cls_token ,  cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0 ,  sep_token=self.tokenizer.sep_token ,  sep_token_extra=lowerCAmelCase_ ,  pad_on_left=bool(self.config.model_type in ['xlnet']					) ,  pad_token=self.tokenizer.pad_token_id ,  pad_token_segment_id=self.tokenizer.pad_token_type_id ,  pad_token_label_id=self.pad_token_label_id ,  )
           logger.info('Saving features into cached file %s' ,  lowerCAmelCase_					)
           torch.save(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		bool = False					)							->						DataLoader:
     __lowerCAmelCase							     =     self._feature_file(lowerCAmelCase_					)
     logger.info('Loading features from cached file %s' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.load(lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.tensor([f.input_ids for f in features] ,  dtype=torch.long					)
     __lowerCAmelCase							     =     torch.tensor([f.attention_mask for f in features] ,  dtype=torch.long					)
     if features[0].token_type_ids is not None:
        __lowerCAmelCase							     =     torch.tensor([f.token_type_ids for f in features] ,  dtype=torch.long					)
     else:
        __lowerCAmelCase							     =     torch.tensor([0 for f in features] ,  dtype=torch.long					)
        # HACK(we will not use this anymore soon)
     __lowerCAmelCase							     =     torch.tensor([f.label_ids for f in features] ,  dtype=torch.long					)
     return DataLoader(
         TensorDataset(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					) ,  batch_size=lowerCAmelCase_					)






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Dict:
     """Compute validation""" ""
     __lowerCAmelCase							     =     {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
     if self.config.model_type != "distilbert":
        __lowerCAmelCase							     =     (
            batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
        )  # XLM and RoBERTa don"t use token_type_ids
     __lowerCAmelCase							     =     self(**lowerCAmelCase_					)
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     outputs[:2]
     __lowerCAmelCase							     =     logits.detach().cpu().numpy()
     __lowerCAmelCase							     =     inputs['labels'].detach().cpu().numpy()
     return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple					)							->						str:
     __lowerCAmelCase							     =     torch.stack([x['val_loss'] for x in outputs]					).mean()
     __lowerCAmelCase							     =     np.concatenate([x['pred'] for x in outputs] ,  axis=0					)
     __lowerCAmelCase							     =     np.argmax(lowerCAmelCase_ ,  axis=2					)
     __lowerCAmelCase							     =     np.concatenate([x['target'] for x in outputs] ,  axis=0					)

     __lowerCAmelCase							     =     dict(enumerate(self.labels					)					)
     __lowerCAmelCase							     =     [[] for _ in range(out_label_ids.shape[0]					)]
     __lowerCAmelCase							     =     [[] for _ in range(out_label_ids.shape[0]					)]

     for i in range(out_label_ids.shape[0]					):
        for j in range(out_label_ids.shape[1]					):
           if out_label_ids[i, j] != self.pad_token_label_id:
              out_label_list[i].append(label_map[out_label_ids[i][j]]					)
              preds_list[i].append(label_map[preds[i][j]]					)

     __lowerCAmelCase							     =     {
         'val_loss': val_loss_mean,
         'accuracy_score': accuracy_score(lowerCAmelCase_ ,  lowerCAmelCase_					),
         'precision': precision_score(lowerCAmelCase_ ,  lowerCAmelCase_					),
         'recall': recall_score(lowerCAmelCase_ ,  lowerCAmelCase_					),
         'f1': fa_score(lowerCAmelCase_ ,  lowerCAmelCase_					),
     }

     __lowerCAmelCase							     =     dict(results.items()					)
     __lowerCAmelCase							     =     results
     return ret, preds_list, out_label_list






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						Union[str, Any]:
     # when stable
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self._eval_end(lowerCAmelCase_					)
     __lowerCAmelCase							     =     ret['log']
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		Optional[int]					)							->						Optional[Any]:
     # updating to test_epoch_end instead of deprecated test_end
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self._eval_end(lowerCAmelCase_					)

     # Converting to the dict required by pl
     # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
     # pytorch_lightning/trainer/logging.py#L139
     __lowerCAmelCase							     =     ret['log']
     # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
     return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  @staticmethod
  def     lowercase		(    lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Optional[Any]:
     # Add NER specific options
     BaseTransformer.add_model_specific_args(lowerCAmelCase_ ,  lowerCAmelCase_					)
     parser.add_argument(
         '--task_type' ,  default='NER' ,  type=lowerCAmelCase_ ,  help='Task type to fine tune in training (e.g. NER, POS, etc)'					)
     parser.add_argument(
         '--max_seq_length' ,  default=1_2_8 ,  type=lowerCAmelCase_ ,  help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         ) ,  )

     parser.add_argument(
         '--labels' ,  default='' ,  type=lowerCAmelCase_ ,  help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.' ,  )
     parser.add_argument(
         '--gpus' ,  default=0 ,  type=lowerCAmelCase_ ,  help='The number of GPUs allocated for this, it is by default 0 meaning none' ,  )

     parser.add_argument(
         '--overwrite_cache' ,  action='store_true' ,  help='Overwrite the cached training and evaluation sets'					)

     return parser


if __name__ == "__main__":
 _snake_case  :   List[Any]						   =  argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 _snake_case  :   Optional[Any]						   =  NERTransformer.add_model_specific_args(parser, os.getcwd())
 _snake_case  :   Dict						   =  parser.parse_args()
 _snake_case  :   Optional[Any]						   =  NERTransformer(args)
 _snake_case  :   Union[str, Any]						   =  generic_train(model, args)

 if args.do_predict:
  # See https://github.com/huggingface/transformers/issues/3159
  # pl use this default format to create a checkpoint:
  # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
  # /pytorch_lightning/callbacks/model_checkpoint.py#L322
  _snake_case  :   List[str]						   =  sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
  _snake_case  :   Union[str, Any]						   =  model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)








import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class     _UpperCAmelCase   (   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			DanceDiffusionPipeline
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  a_      =			PipelineTesterMixin.required_optional_params - {
      """callback""",
      """latents""",
      """callback_steps""",
      """output_type""",
      """num_images_per_prompt""",
  }
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         block_out_channels=(3_2, 3_2, 6_4) ,  extra_in_channels=1_6 ,  sample_size=5_1_2 ,  sample_rate=1_6_0_0_0 ,  in_channels=2 ,  out_channels=2 ,  flip_sin_to_cos=lowerCAmelCase_ ,  use_timestep_embedding=lowerCAmelCase_ ,  time_embedding_type='fourier' ,  mid_block_type='UNetMidBlock1D' ,  down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') ,  up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') ,  )
     __lowerCAmelCase							     =     IPNDMScheduler()

     __lowerCAmelCase							     =     {
         'unet': unet,
         'scheduler': scheduler,
     }
     return components






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[str]=0					)							->						Any:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 4,
     }
     return inputs






  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     self.get_dummy_components()
     __lowerCAmelCase							     =     DanceDiffusionPipeline(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.get_dummy_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, components["unet"].sample_size)
     __lowerCAmelCase							     =     np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]					)
     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  @skip_mps
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return super().test_save_load_local()






  @skip_mps
  def     lowercase		(    self			:		List[str]					)							->						Dict:
     return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






  @skip_mps
  def     lowercase		(    self			:		str					)							->						List[str]:
     return super().test_save_load_optional_components()






  @skip_mps
  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     return super().test_attention_slicing_forward_pass()






  def     lowercase		(    self			:		str					)							->						int:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		List[str]					)							->						List[str]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]					)

     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  def     lowercase		(    self			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' ,  torch_dtype=torch.floataa					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]					)

     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2

