


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			 =   logging.getLogger(__name__)
@dataclass
class 							lowerCamelCase       :


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
							SCREAMING_SNAKE_CASE										=      field(
							    default=__SCREAMING_SNAKE_CASE				,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
							SCREAMING_SNAKE_CASE										=      field(
							    default="""NER"""				,					metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""} )
							SCREAMING_SNAKE_CASE										=      field(
							    default=__SCREAMING_SNAKE_CASE				,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
							SCREAMING_SNAKE_CASE										=      field(default=__SCREAMING_SNAKE_CASE				,					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.
							SCREAMING_SNAKE_CASE										=      field(
							    default=__SCREAMING_SNAKE_CASE				,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,					)




@dataclass
class 							lowerCamelCase       :


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      field(
							    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""} )
							SCREAMING_SNAKE_CASE										=      field(
							    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""} )
							SCREAMING_SNAKE_CASE										=      field(
							    default=1_28				,					metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }				,					)
							SCREAMING_SNAKE_CASE										=      field(
							    default=__SCREAMING_SNAKE_CASE				,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
						UpperCAmelCase__		=    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.''' )

						UpperCAmelCase__		=    import_module('''tasks''' )

						try:
												UpperCAmelCase__		=    getattr(_UpperCAmelCase	,			model_args.task_type )
												UpperCAmelCase__		=    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'''	,			_UpperCAmelCase )

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

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

						UpperCAmelCase__		=    AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,			num_labels=_UpperCAmelCase	,			idalabel=_UpperCAmelCase	,			labelaid={label: i for i, label in enumerate(_UpperCAmelCase )}	,			cache_dir=model_args.cache_dir	,			)
						UpperCAmelCase__		=    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():
												UpperCAmelCase__		=    TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path	,			from_pt=bool('''.bin''' in model_args.model_name_or_path )	,			config=_UpperCAmelCase	,			cache_dir=model_args.cache_dir	,			)

						# Get datasets
						UpperCAmelCase__		=    (
						    TFTokenClassificationDataset(
						        token_classification_task=_UpperCAmelCase	,			data_dir=data_args.data_dir	,			tokenizer=_UpperCAmelCase	,			labels=_UpperCAmelCase	,			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
						)
						UpperCAmelCase__		=    (
						    TFTokenClassificationDataset(
						        token_classification_task=_UpperCAmelCase	,			data_dir=data_args.data_dir	,			tokenizer=_UpperCAmelCase	,			labels=_UpperCAmelCase	,			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(_UpperCamelCase	,			_UpperCamelCase ) -> Tuple[List[int], List[int]]:
												UpperCAmelCase__		=    np.argmax(_UpperCAmelCase	,			axis=2 )
												UpperCAmelCase__		=    preds.shape
												UpperCAmelCase__		=    [[] for _ in range(_UpperCAmelCase )]
												UpperCAmelCase__		=    [[] for _ in range(_UpperCAmelCase )]

												for i in range(_UpperCAmelCase ):
																		for j in range(_UpperCAmelCase ):
																								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(_UpperCamelCase ) -> Dict:
												UpperCAmelCase__		=    align_predictions(p.predictions	,			p.label_ids )

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

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

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

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

												UpperCAmelCase__		=    trainer.evaluate()
												UpperCAmelCase__		=    os.path.join(training_args.output_dir	,			'''eval_results.txt''' )

												with open(_UpperCAmelCase	,			'''w''' ) as writer:
																		logger.info('''***** Eval results *****''' )

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

																		results.update(_UpperCAmelCase )

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

												UpperCAmelCase__		=    trainer.predict(test_dataset.get_dataset() )
												UpperCAmelCase__		=    align_predictions(_UpperCAmelCase	,			_UpperCAmelCase )
												UpperCAmelCase__		=    classification_report(_UpperCAmelCase	,			_UpperCAmelCase )

												logger.info('''\n%s'''	,			_UpperCAmelCase )

												UpperCAmelCase__		=    os.path.join(training_args.output_dir	,			'''test_results.txt''' )

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

												# Save predictions
												UpperCAmelCase__		=    os.path.join(training_args.output_dir	,			'''test_predictions.txt''' )

												with open(_UpperCAmelCase	,			'''w''' ) as writer:
																		with open(os.path.join(data_args.data_dir	,			'''test.txt''' )	,			'''r''' ) as f:
																								UpperCAmelCase__		=    0

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

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

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

						return results


if __name__ == "__main__":
						main()



"""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			 =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      42
							SCREAMING_SNAKE_CASE										=      None






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=0.999	,			_UpperCamelCase="cosine"	,			):





						"""simple docstring"""

						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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


class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      [e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE										=      1






							@register_to_config
							def __init__(						self :     Dict		, lowerCamelCase_ :     int = 1000		, lowerCamelCase_ :     float = 0.0001		, lowerCamelCase_ :     float = 0.02		, lowerCamelCase_ :     str = "linear"		, lowerCamelCase_ :     Optional[Union[np.ndarray, List[float]]] = None		, lowerCamelCase_ :     str = "epsilon"		, lowerCamelCase_ :     str = "linspace"		, lowerCamelCase_ :     int = 0		, ):
													if trained_betas is not None:
																			UpperCAmelCase__		=    torch.tensor(lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "linear":
																			UpperCAmelCase__		=    torch.linspace(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			UpperCAmelCase__		=    (
																			    torch.linspace(beta_start**0.5		, beta_end**0.5		, lowerCamelCase_		, dtype=torch.floataa				) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			UpperCAmelCase__		=    betas_for_alpha_bar(lowerCamelCase_				)
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

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

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

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






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													# 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 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]				):
													if isinstance(lowerCamelCase_		, torch.Tensor				):
																			UpperCAmelCase__		=    timestep.to(self.timesteps.device				)
													UpperCAmelCase__		=    (self.timesteps == timestep).nonzero().item()
													UpperCAmelCase__		=    self.sigmas[step_index]
													UpperCAmelCase__		=    sample / ((sigma**2 + 1) ** 0.5)
													UpperCAmelCase__		=    True
													return sample






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     int		, lowerCamelCase_ :     Union[str, torch.device] = None				):
													UpperCAmelCase__		=    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":
																			UpperCAmelCase__		=    np.linspace(0		, self.config.num_train_timesteps - 1		, lowerCamelCase_		, dtype=lowerCamelCase_				)[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			UpperCAmelCase__		=    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
																			UpperCAmelCase__		=    (np.arange(0		, lowerCamelCase_				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_				)
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			UpperCAmelCase__		=    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
																			UpperCAmelCase__		=    (np.arange(self.config.num_train_timesteps		, 0		, -step_ratio				)).round().copy().astype(lowerCamelCase_				)
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

													UpperCAmelCase__		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase__		=    np.interp(lowerCamelCase_		, np.arange(0		, len(lowerCamelCase_				)				)		, lowerCamelCase_				)
													UpperCAmelCase__		=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)
													if str(lowerCamelCase_				).startswith('''mps'''				):
																			# mps does not support float64
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(lowerCamelCase_		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Optional[torch.Generator] = None		, lowerCamelCase_ :     bool = True		, ):

													if (
													    isinstance(lowerCamelCase_		, lowerCamelCase_				)
													    or isinstance(lowerCamelCase_		, torch.IntTensor				)
													    or isinstance(lowerCamelCase_		, 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(lowerCamelCase_		, torch.Tensor				):
																			UpperCAmelCase__		=    timestep.to(self.timesteps.device				)

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

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			UpperCAmelCase__		=    sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			UpperCAmelCase__		=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError('''prediction_type not implemented yet: sample'''				)
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

													UpperCAmelCase__		=    self.sigmas[step_index]
													UpperCAmelCase__		=    self.sigmas[step_index + 1]
													UpperCAmelCase__		=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
													UpperCAmelCase__		=    (sigma_to**2 - sigma_up**2) ** 0.5

													# 2. Convert to an ODE derivative
													UpperCAmelCase__		=    (sample - pred_original_sample) / sigma

													UpperCAmelCase__		=    sigma_down - sigma

													UpperCAmelCase__		=    sample + derivative * dt

													UpperCAmelCase__		=    model_output.device
													UpperCAmelCase__		=    randn_tensor(model_output.shape		, dtype=model_output.dtype		, device=lowerCamelCase_		, generator=lowerCamelCase_				)

													UpperCAmelCase__		=    prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=lowerCamelCase_		, pred_original_sample=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, ):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													UpperCAmelCase__		=    self.sigmas.to(device=original_samples.device		, dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_				):
																			# mps does not support float64
																			UpperCAmelCase__		=    self.timesteps.to(original_samples.device		, dtype=torch.floataa				)
																			UpperCAmelCase__		=    timesteps.to(original_samples.device		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    self.timesteps.to(original_samples.device				)
																			UpperCAmelCase__		=    timesteps.to(original_samples.device				)

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

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

													UpperCAmelCase__		=    original_samples + noise * sigma
													return noisy_samples






							def __len__(						self :     Optional[int]				):
													return self.config.num_train_timesteps