


import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase	=							logging.getLogger(__name__)


@dataclass
class 	_a							:
							_lowercase     :      str  				=			field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
							_lowercase     :      Optional[str]  				=			field(
							    default=__SCREAMING_SNAKE_CASE					,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
							_lowercase     :      Optional[str]  				=			field(
							    default='''NER'''					,     metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}      )
							_lowercase     :      Optional[str]  				=			field(
							    default=__SCREAMING_SNAKE_CASE					,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
							_lowercase     :      bool  				=			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.
							_lowercase     :      Optional[str]  				=			field(
							    default=__SCREAMING_SNAKE_CASE					,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,     )


@dataclass
class 	_a							:
							_lowercase     :      str  				=			field(
							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}      )
							_lowercase     :      Optional[str]  				=			field(
							    default=__SCREAMING_SNAKE_CASE					,     metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					,     )
							_lowercase     :      int  				=			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.'''
							        )
							    }					,     )
							_lowercase     :      bool  				=			field(
							    default=__SCREAMING_SNAKE_CASE					,     metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )
def       _a			(				):


		"""simple docstring"""




		lowercase__						=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
		if len(sys.argv					) == 2 and sys.argv[1].endswith('''.json'''					):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowercase__						=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
		else:
				lowercase__						=				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.'''					)

		lowercase__						=				import_module('''tasks'''					)
		try:
				lowercase__						=				getattr(_SCREAMING_SNAKE_CASE						,    model_args.task_type					)
				lowercase__						=				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 if training_args.local_rank in [-1, 0] else logging.WARN						,    )
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''						,    training_args.local_rank						,    training_args.device						,    training_args.n_gpu						,    bool(training_args.local_rank != -1					)						,    training_args.fpaa						,    )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank					):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('''Training/evaluation parameters %s'''						,    _SCREAMING_SNAKE_CASE					)

		# Set seed
		set_seed(training_args.seed					)

		# Prepare CONLL-2003 task
		lowercase__						=				token_classification_task.get_labels(data_args.labels					)
		lowercase__						=				dict(enumerate(_SCREAMING_SNAKE_CASE					)					)
		lowercase__						=				len(_SCREAMING_SNAKE_CASE					)

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

		lowercase__						=				AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,    num_labels=_SCREAMING_SNAKE_CASE						,    idalabel=_SCREAMING_SNAKE_CASE						,    labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE					)}						,    cache_dir=model_args.cache_dir						,    )
		lowercase__						=				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						,    )
		lowercase__						=				AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path						,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path					)						,    config=_SCREAMING_SNAKE_CASE						,    cache_dir=model_args.cache_dir						,    )

		# Get datasets
		lowercase__						=				(
		    TokenClassificationDataset(
		        token_classification_task=_SCREAMING_SNAKE_CASE						,    data_dir=data_args.data_dir						,    tokenizer=_SCREAMING_SNAKE_CASE						,    labels=_SCREAMING_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
		)
		lowercase__						=				(
		    TokenClassificationDataset(
		        token_classification_task=_SCREAMING_SNAKE_CASE						,    data_dir=data_args.data_dir						,    tokenizer=_SCREAMING_SNAKE_CASE						,    labels=_SCREAMING_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(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					) -> Tuple[List[int], List[int]]:
				lowercase__						=				np.argmax(_SCREAMING_SNAKE_CASE						,    axis=2					)

				lowercase__						=				preds.shape

				lowercase__						=				[[] for _ in range(_SCREAMING_SNAKE_CASE					)]
				lowercase__						=				[[] for _ in range(_SCREAMING_SNAKE_CASE					)]

				for i in range(_SCREAMING_SNAKE_CASE					):
						for j in range(_SCREAMING_SNAKE_CASE					):
								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
										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(SCREAMING_SNAKE_CASE					) -> Dict:
				lowercase__						=				align_predictions(p.predictions						,    p.label_ids					)
				return {
				    "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					),
				    "precision": precision_score(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					),
				    "recall": recall_score(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					),
				    "f1": fa_score(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					),
				}

		# Data collator
		lowercase__						=				DataCollatorWithPadding(_SCREAMING_SNAKE_CASE						,    pad_to_multiple_of=8					) if training_args.fpaa else None

		# Initialize our Trainer
		lowercase__						=				Trainer(
		    model=_SCREAMING_SNAKE_CASE						,    args=_SCREAMING_SNAKE_CASE						,    train_dataset=_SCREAMING_SNAKE_CASE						,    eval_dataset=_SCREAMING_SNAKE_CASE						,    compute_metrics=_SCREAMING_SNAKE_CASE						,    data_collator=_SCREAMING_SNAKE_CASE						,    )

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_process_zero():
						tokenizer.save_pretrained(training_args.output_dir					)

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

				lowercase__						=				trainer.evaluate()

				lowercase__						=				os.path.join(training_args.output_dir						,    '''eval_results.txt'''					)
				if trainer.is_world_process_zero():
						with open(_SCREAMING_SNAKE_CASE						,    '''w'''					) as writer:
								logger.info('''***** Eval results *****'''					)
								for key, value in result.items():
										logger.info('''  %s = %s'''						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					)
										writer.write('''%s = %s\n''' % (key, value)					)

						results.update(_SCREAMING_SNAKE_CASE					)

    # Predict
		if training_args.do_predict:
				lowercase__						=				TokenClassificationDataset(
				    token_classification_task=_SCREAMING_SNAKE_CASE						,    data_dir=data_args.data_dir						,    tokenizer=_SCREAMING_SNAKE_CASE						,    labels=_SCREAMING_SNAKE_CASE						,    model_type=config.model_type						,    max_seq_length=data_args.max_seq_length						,    overwrite_cache=data_args.overwrite_cache						,    mode=Split.test						,    )

				lowercase__						=				trainer.predict(_SCREAMING_SNAKE_CASE					)
				lowercase__						=				align_predictions(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					)

				lowercase__						=				os.path.join(training_args.output_dir						,    '''test_results.txt'''					)
				if trainer.is_world_process_zero():
						with open(_SCREAMING_SNAKE_CASE						,    '''w'''					) as writer:
								for key, value in metrics.items():
										logger.info('''  %s = %s'''						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					)
										writer.write('''%s = %s\n''' % (key, value)					)

        # Save predictions
				lowercase__						=				os.path.join(training_args.output_dir						,    '''test_predictions.txt'''					)
				if trainer.is_world_process_zero():
						with open(_SCREAMING_SNAKE_CASE						,    '''w'''					) as writer:
								with open(os.path.join(data_args.data_dir						,    '''test.txt'''					)						,    '''r'''					) as f:
										token_classification_task.write_predictions_to_file(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE					)

		return results
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		main()


if __name__ == "__main__":
			main()





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class 	_a							:
							_lowercase     :      CommonSchedulerState

							# setable values
							_lowercase     :      jnp.ndarray
							_lowercase     :      jnp.ndarray
							_lowercase     :      Optional[int]  				=			None

							@classmethod
							def 				lowerCamelCase_			(							cls:     Dict   ,						UpperCamelCase_:     CommonSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray							)      ->       List[str]:





									"""simple docstring"""







									return cls(common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_							)


@dataclass
class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      DDPMSchedulerState


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__      ):
							_lowercase     :      Tuple  				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowercase     :      jnp.dtype

							@property
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return True

							@register_to_config
							def __init__(							self:     Any   ,						UpperCamelCase_:     int = 1_000   ,						UpperCamelCase_:     float = 0.0001   ,						UpperCamelCase_:     float = 0.02   ,						UpperCamelCase_:     str = "linear"   ,						UpperCamelCase_:     Optional[jnp.ndarray] = None   ,						UpperCamelCase_:     str = "fixed_small"   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     str = "epsilon"   ,						UpperCamelCase_:     jnp.dtype = jnp.floataa   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				dtype

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									if common is None:
											lowercase__						=				CommonSchedulerState.create(self							)

									# standard deviation of the initial noise distribution
									lowercase__						=				jnp.array(1.0   ,						dtype=self.dtype							)

									lowercase__						=				jnp.arange(0   ,						self.config.num_train_timesteps							).round()[::-1]

									return DDPMSchedulerState.create(
									    common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[int] = None							)      ->       jnp.ndarray:





									"""simple docstring"""







									return sample

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Tuple = ()							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									lowercase__						=				self.config.num_train_timesteps // num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# rounding to avoid issues when num_inference_step is power of 3
									lowercase__						=				(jnp.arange(0   ,						UpperCamelCase_							) * step_ratio).round()[::-1]

									return state.replace(
									    num_inference_steps=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     str=None							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)

									# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
									# and sample from it to get previous sample
									# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
									lowercase__						=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
											lowercase__						=				self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											lowercase__						=				jnp.log(jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)							)
									elif variance_type == "fixed_large":
											lowercase__						=				state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											lowercase__						=				jnp.log(state.common.betas[t]							)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											lowercase__						=				variance
											lowercase__						=				state.common.betas[t]
											lowercase__						=				(predicted_variance + 1) / 2
											lowercase__						=				frac * max_log + (1 - frac) * min_log

									return variance

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[jax.random.KeyArray] = None   ,						UpperCamelCase_:     bool = True   ,						)      ->       Union[FlaxDDPMSchedulerOutput, Tuple]:





									"""simple docstring"""







									lowercase__						=				timestep

									if key is None:
											lowercase__						=				jax.random.PRNGKey(0							)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											lowercase__					,		lowercase__						=				jnp.split(UpperCamelCase_   ,						sample.shape[1]   ,						axis=1							)
									else:
											lowercase__						=				None

									# 1. compute alphas, betas
									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)
									lowercase__						=				1 - alpha_prod_t
									lowercase__						=				1 - alpha_prod_t_prev

									# 2. compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
									if self.config.prediction_type == "epsilon":
											lowercase__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											lowercase__						=				model_output
									elif self.config.prediction_type == "v_prediction":
											lowercase__						=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
											    ''' for the FlaxDDPMScheduler.'''							)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						-1   ,						1							)

									# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									lowercase__						=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									lowercase__						=				state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

									# 5. Compute predicted previous sample µ_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									lowercase__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
											lowercase__						=				jax.random.split(UpperCamelCase_   ,						num=1							)
											lowercase__						=				jax.random.normal(UpperCamelCase_   ,						shape=model_output.shape   ,						dtype=self.dtype							)
											return (self._get_variance(UpperCamelCase_   ,						UpperCamelCase_   ,						predicted_variance=UpperCamelCase_							) ** 0.5) * noise

									lowercase__						=				jnp.where(t > 0   ,						random_variance()   ,						jnp.zeros(model_output.shape   ,						dtype=self.dtype							)							)

									lowercase__						=				pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_   ,						state=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return add_noise_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return get_velocity_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)


							def __len__(							self:     str							)      ->       List[Any]:





									"""simple docstring"""







									return self.config.num_train_timesteps


