






'''simple docstring'''







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


__A   :							int     	=						logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	42
     _snake_case							   =	None







def     _UpperCamelCase				(  snake_case__				,      snake_case__=0.999				,      snake_case__="cosine"				,      ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(snake_case__				):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(snake_case__				):
                  return math.exp(t * -12.0				)

      else:
            raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

      __A		 =   []
      for i in range(snake_case__				):
            __A		 =   i / num_diffusion_timesteps
            __A		 =   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(snake_case__				) / alpha_bar_fn(snake_case__				)				,      snake_case__				)				)
      return torch.tensor(snake_case__				,      dtype=torch.floataa				)






class        a__     (      lowerCamelCase			,    lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	[e.name for e in KarrasDiffusionSchedulers]
     _snake_case							   =	1






     @register_to_config
     def __init__(self     :						Optional[int]					,					lowercase__     :						int = 1000					,					lowercase__     :						float = 0.00_01					,					lowercase__     :						float = 0.02					,					lowercase__     :						str = "linear"					,					lowercase__     :						Optional[Union[np.ndarray, List[float]]] = None					,					lowercase__     :						str = "epsilon"					,					lowercase__     :						str = "linear"					,					lowercase__     :						Optional[bool] = False					,					lowercase__     :						str = "linspace"					,					lowercase__     :						int = 0					,					)	->							List[Any]:
           if trained_betas is not None:
                 __A		 =   torch.tensor(lowercase__					,					dtype=torch.floataa       )
           elif beta_schedule == "linear":
                 __A		 =   torch.linspace(lowercase__					,					lowercase__					,					lowercase__					,					dtype=torch.floataa       )
           elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 __A		 =   (
                     torch.linspace(beta_start**0.5					,					beta_end**0.5					,					lowercase__					,					dtype=torch.floataa       ) ** 2
                 )
           elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 __A		 =   betas_for_alpha_bar(lowercase__       )
           else:
                 raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"       )

           __A		 =   1.0 - self.betas
           __A		 =   torch.cumprod(self.alphas					,					dim=0       )

           __A		 =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
           __A		 =   np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
           __A		 =   torch.from_numpy(lowercase__       )

           # setable values
           __A		 =   None
           __A		 =   np.linspace(0					,					num_train_timesteps - 1					,					lowercase__					,					dtype=lowercase__       )[::-1].copy()
           __A		 =   torch.from_numpy(lowercase__       )
           __A		 =   False
           __A		 =   use_karras_sigmas






     @property
     def      _UpperCamelCase (self     :						Tuple       )	->							Dict:
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                 return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						Union[float, torch.FloatTensor]       )	->							torch.FloatTensor:
           if isinstance(lowercase__					,					torch.Tensor       ):
                 __A		 =   timestep.to(self.timesteps.device       )
           __A		 =   (self.timesteps == timestep).nonzero().item()
           __A		 =   self.sigmas[step_index]

           __A		 =   sample / ((sigma**2 + 1) ** 0.5)

           __A		 =   True
           return sample






     def      _UpperCamelCase (self     :						List[str]					,					lowercase__     :						int					,					lowercase__     :						Union[str, torch.device] = None       )	->							Optional[Any]:
           __A		 =   num_inference_steps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                 __A		 =   np.linspace(0					,					self.config.num_train_timesteps - 1					,					lowercase__					,					dtype=lowercase__       )[
                     ::-1
                 ].copy()
           elif self.config.timestep_spacing == "leading":
                 __A		 =   self.config.num_train_timesteps // self.num_inference_steps
                 # creates integer timesteps by multiplying by ratio
                 # casting to int to avoid issues when num_inference_step is power of 3
                 __A		 =   (np.arange(0					,					lowercase__       ) * step_ratio).round()[::-1].copy().astype(lowercase__       )
                 timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                 __A		 =   self.config.num_train_timesteps / self.num_inference_steps
                 # creates integer timesteps by multiplying by ratio
                 # casting to int to avoid issues when num_inference_step is power of 3
                 __A		 =   (np.arange(self.config.num_train_timesteps					,					0					,					-step_ratio       )).round().copy().astype(lowercase__       )
                 timesteps -= 1
           else:
                 raise ValueError(
                     f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."       )

           __A		 =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
           __A		 =   np.log(lowercase__       )

           if self.config.interpolation_type == "linear":
                 __A		 =   np.interp(lowercase__					,					np.arange(0					,					len(lowercase__       )       )					,					lowercase__       )
           elif self.config.interpolation_type == "log_linear":
                 __A		 =   torch.linspace(np.log(sigmas[-1]       )					,					np.log(sigmas[0]       )					,					num_inference_steps + 1       ).exp()
           else:
                 raise ValueError(
                     f"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
                     " 'linear' or 'log_linear'"       )

           if self.use_karras_sigmas:
                 __A		 =   self._convert_to_karras(in_sigmas=lowercase__					,					num_inference_steps=self.num_inference_steps       )
                 __A		 =   np.array([self._sigma_to_t(lowercase__					,					lowercase__       ) for sigma in sigmas]       )

           __A		 =   np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
           __A		 =   torch.from_numpy(lowercase__       ).to(device=lowercase__       )
           if str(lowercase__       ).startswith("mps"       ):
                 # mps does not support float64
                 __A		 =   torch.from_numpy(lowercase__       ).to(lowercase__					,					dtype=torch.floataa       )
           else:
                 __A		 =   torch.from_numpy(lowercase__       ).to(device=lowercase__       )






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						Tuple					,					lowercase__     :						List[str]       )	->							Optional[int]:
           # get log sigma
           __A		 =   np.log(lowercase__       )

           # get distribution
           __A		 =   log_sigma - log_sigmas[:, np.newaxis]

           # get sigmas range
           __A		 =   np.cumsum((dists >= 0)					,					axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
           __A		 =   low_idx + 1

           __A		 =   log_sigmas[low_idx]
           __A		 =   log_sigmas[high_idx]

           # interpolate sigmas
           __A		 =   (low - log_sigma) / (low - high)
           __A		 =   np.clip(lowercase__					,					0					,					1       )

           # transform interpolation to time range
           __A		 =   (1 - w) * low_idx + w * high_idx
           __A		 =   t.reshape(sigma.shape       )
           return t






     def      _UpperCamelCase (self     :						str					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						List[Any]       )	->							torch.FloatTensor:
           __A		 =   in_sigmas[-1].item()
           __A		 =   in_sigmas[0].item()

           __A		 =   7.0  # 7.0 is the value used in the paper
           __A		 =   np.linspace(0					,					1					,					lowercase__       )
           __A		 =   sigma_min ** (1 / rho)
           __A		 =   sigma_max ** (1 / rho)
           __A		 =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						Union[float, torch.FloatTensor]					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						float = 0.0					,					lowercase__     :						float = 0.0					,					lowercase__     :						float = float("inf"       )					,					lowercase__     :						float = 1.0					,					lowercase__     :						Optional[torch.Generator] = None					,					lowercase__     :						bool = True					,					)	->							Union[EulerDiscreteSchedulerOutput, Tuple]:

           if (
               isinstance(lowercase__					,					lowercase__       )
               or isinstance(lowercase__					,					torch.IntTensor       )
               or isinstance(lowercase__					,					torch.LongTensor       )
           ):
                 raise ValueError(
                     (
                         "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                         " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                         " one of the `scheduler.timesteps` as a timestep."
                     )					,					)

           if not self.is_scale_input_called:
                 logger.warning(
                     "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                     "See `StableDiffusionPipeline` for a usage example."       )

           if isinstance(lowercase__					,					torch.Tensor       ):
                 __A		 =   timestep.to(self.timesteps.device       )

           __A		 =   (self.timesteps == timestep).nonzero().item()
           __A		 =   self.sigmas[step_index]

           __A		 =   min(s_churn / (len(self.sigmas       ) - 1)					,					2**0.5 - 1       ) if s_tmin <= sigma <= s_tmax else 0.0

           __A		 =   randn_tensor(
               model_output.shape					,					dtype=model_output.dtype					,					device=model_output.device					,					generator=lowercase__       )

           __A		 =   noise * s_noise
           __A		 =   sigma * (gamma + 1)

           if gamma > 0:
                 __A		 =   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           # NOTE: "original_sample" should not be an expected prediction_type but is left in for
           # backwards compatibility
           if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                 __A		 =   model_output
           elif self.config.prediction_type == "epsilon":
                 __A		 =   sample - sigma_hat * model_output
           elif self.config.prediction_type == "v_prediction":
                 # * c_out + input * c_skip
                 __A		 =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           else:
                 raise ValueError(
                     f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"       )

           # 2. Convert to an ODE derivative
           __A		 =   (sample - pred_original_sample) / sigma_hat

           __A		 =   self.sigmas[step_index + 1] - sigma_hat

           __A		 =   sample + derivative * dt

           if not return_dict:
                 return (prev_sample,)

           return EulerDiscreteSchedulerOutput(prev_sample=lowercase__					,					pred_original_sample=lowercase__       )






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						torch.FloatTensor					,					)	->							torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           __A		 =   self.sigmas.to(device=original_samples.device					,					dtype=original_samples.dtype       )
           if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__       ):
                 # mps does not support float64
                 __A		 =   self.timesteps.to(original_samples.device					,					dtype=torch.floataa       )
                 __A		 =   timesteps.to(original_samples.device					,					dtype=torch.floataa       )
           else:
                 __A		 =   self.timesteps.to(original_samples.device       )
                 __A		 =   timesteps.to(original_samples.device       )

           __A		 =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

           __A		 =   sigmas[step_indices].flatten()
           while len(sigma.shape       ) < len(original_samples.shape       ):
                 __A		 =   sigma.unsqueeze(-1       )

           __A		 =   original_samples + noise * sigma
           return noisy_samples






     def __len__(self     :						Tuple       )	->							Tuple:
           return self.config.num_train_timesteps










'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


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






@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
     _snake_case							   =	field(
         default='NER'			,    metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
     _snake_case							   =	field(default=lowerCamelCase			,    metadata={'help': 'Set this flag to use fast tokenization.'}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,    )







@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}    )
     _snake_case							   =	field(
         metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    )
     _snake_case							   =	field(
         default=128			,    metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }			,    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







def     _UpperCamelCase				(  ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
      __A		 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __A ,					__A ,					__A		 =   parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir				)
          and os.listdir(training_args.output_dir				)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                " --overwrite_output_dir to overcome."				)

      __A		 =   import_module("tasks"				)

      try:
            __A		 =   getattr(snake_case__				,      model_args.task_type				)
            __A		 =   token_classification_task_clazz()
      except AttributeError:
            raise ValueError(
                f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"				)

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,      datefmt="%m/%d/%Y %H:%M:%S"				,      level=logging.INFO				,      )
      logger.info(
          "n_replicas: %s, distributed training: %s, 16-bits training: %s"				,      training_args.n_replicas				,      bool(training_args.n_replicas > 1				)				,      training_args.fpaa				,      )
      logger.info("Training/evaluation parameters %s"				,      snake_case__				)

      # Prepare Token Classification task
      __A		 =   token_classification_task.get_labels(data_args.labels				)
      __A		 =   dict(enumerate(snake_case__				)				)
      __A		 =   len(snake_case__				)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      __A		 =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,      num_labels=snake_case__				,      idalabel=snake_case__				,      labelaid={label: i for i, label in enumerate(snake_case__				)}				,      cache_dir=model_args.cache_dir				,      )
      __A		 =   AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,      cache_dir=model_args.cache_dir				,      use_fast=model_args.use_fast				,      )

      with training_args.strategy.scope():
            __A		 =   TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path				,      from_pt=bool(".bin" in model_args.model_name_or_path				)				,      config=snake_case__				,      cache_dir=model_args.cache_dir				,      )

      # Get datasets
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.train				,      )
          if training_args.do_train
          else None
      )
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.dev				,      )
          if training_args.do_eval
          else None
      )

      def align_predictions(snake_case__				,      snake_case__				) -> Tuple[List[int], List[int]]:
            __A		 =   np.argmax(snake_case__				,      axis=2				)
            __A ,					__A		 =   preds.shape
            __A		 =   [[] for _ in range(snake_case__				)]
            __A		 =   [[] for _ in range(snake_case__				)]

            for i in range(snake_case__				):
                  for j in range(snake_case__				):
                        if label_ids[i, j] != -100:
                              out_label_list[i].append(label_map[label_ids[i][j]]				)
                              preds_list[i].append(label_map[preds[i][j]]				)

            return preds_list, out_label_list

      def compute_metrics(snake_case__				) -> Dict:
            __A ,					__A		 =   align_predictions(p.predictions				,      p.label_ids				)

            return {
                "precision": precision_score(snake_case__				,      snake_case__				),
                "recall": recall_score(snake_case__				,      snake_case__				),
                "f1": fa_score(snake_case__				,      snake_case__				),
            }

      # Initialize our Trainer
      __A		 =   TFTrainer(
          model=snake_case__				,      args=snake_case__				,      train_dataset=train_dataset.get_dataset() if train_dataset else None				,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,      compute_metrics=snake_case__				,      )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir				)

      # Evaluation
      __A		 =   {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"				)

            __A		 =   trainer.evaluate()
            __A		 =   os.path.join(training_args.output_dir				,      "eval_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  logger.info("***** Eval results *****"				)

                  for key, value in result.items():
                        logger.info("  %s = %s"				,      snake_case__				,      snake_case__				)
                        writer.write("%s = %s\n" % (key, value)				)

                  results.update(snake_case__				)

    # Predict
      if training_args.do_predict:
            __A		 =   TFTokenClassificationDataset(
                token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.test				,      )

            __A ,					__A ,					__A		 =   trainer.predict(test_dataset.get_dataset()				)
            __A ,					__A		 =   align_predictions(snake_case__				,      snake_case__				)
            __A		 =   classification_report(snake_case__				,      snake_case__				)

            logger.info("\n%s"				,      snake_case__				)

            __A		 =   os.path.join(training_args.output_dir				,      "test_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  writer.write("%s\n" % report				)

            # Save predictions
            __A		 =   os.path.join(training_args.output_dir				,      "test_predictions.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  with open(os.path.join(data_args.data_dir				,      "test.txt"				)				,      "r"				) as f:
                        __A		 =   0

                        for line in f:
                              if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                                    writer.write(snake_case__				)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    __A		 =   line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"

                                    writer.write(snake_case__				)
                              else:
                                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."				,      line.split()[0]				)

      return results


if __name__ == "__main__":
       main()


