





'''simple docstring'''







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

import numpy as np
import torch

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





@dataclass
class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		a   :   torch.FloatTensor
		a   :   torch.FloatTensor





def    _lowerCAmelCase						(						lowercase					,	lowercase=0.9_99					,	lowercase="cosine"					,	)					->					Any:
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(lowercase	):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

			elif alpha_transform_type == "exp":

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

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

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







class     _UpperCAmelCase							(						lowerCAmelCase_	,					lowerCAmelCase_   ):
		a   :   Optional[Any]     =1
		@register_to_config
		def __init__(  self,__SCREAMING_SNAKE_CASE = 10_00,__SCREAMING_SNAKE_CASE = 0.0001,__SCREAMING_SNAKE_CASE = 0.02,__SCREAMING_SNAKE_CASE = "linear",__SCREAMING_SNAKE_CASE = 0.0,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = True,):



					'''simple docstring'''

					if trained_betas is not None:
								__lowerCAmelCase											=				torch.from_numpy(__SCREAMING_SNAKE_CASE			)
					elif beta_schedule == "linear":
								__lowerCAmelCase											=				torch.linspace(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dtype=torch.floataa			)
					elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								__lowerCAmelCase											=				(
								    torch.linspace(beta_start**0.5,beta_end**0.5,__SCREAMING_SNAKE_CASE,dtype=torch.floataa			) ** 2
								)
					elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								__lowerCAmelCase											=				betas_for_alpha_bar(__SCREAMING_SNAKE_CASE			)
					elif beta_schedule == "sigmoid":
								# GeoDiff sigmoid schedule
								__lowerCAmelCase											=				torch.linspace(-6,6,__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				torch.sigmoid(__SCREAMING_SNAKE_CASE			) * (beta_end - beta_start) + beta_start
					else:
								raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'			)

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

					__lowerCAmelCase											=				torch.tensor(1.0			)

					# standard deviation of the initial noise distribution
					__lowerCAmelCase											=				1.0

					# setable values
					__lowerCAmelCase											=				None
					__lowerCAmelCase											=				torch.from_numpy(np.arange(0,__SCREAMING_SNAKE_CASE			)[::-1].copy()			)

					__lowerCAmelCase											=				eta
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None			):



					'''simple docstring'''

					return sample
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 10,__SCREAMING_SNAKE_CASE = 10,__SCREAMING_SNAKE_CASE = None,):



					'''simple docstring'''

					__lowerCAmelCase											=				min(self.config.num_train_timesteps,__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				num_inference_steps

					__lowerCAmelCase											=				[]

					__lowerCAmelCase											=				{}
					for j in range(0,num_inference_steps - jump_length,__SCREAMING_SNAKE_CASE			):
								__lowerCAmelCase											=				jump_n_sample - 1

					__lowerCAmelCase											=				num_inference_steps
					while t >= 1:
								__lowerCAmelCase											=				t - 1
								timesteps.append(__SCREAMING_SNAKE_CASE			)

								if jumps.get(__SCREAMING_SNAKE_CASE,0			) > 0:
											__lowerCAmelCase											=				jumps[t] - 1
											for _ in range(__SCREAMING_SNAKE_CASE			):
														__lowerCAmelCase											=				t + 1
														timesteps.append(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				np.array(__SCREAMING_SNAKE_CASE			) * (self.config.num_train_timesteps // self.num_inference_steps)
					__lowerCAmelCase											=				torch.from_numpy(__SCREAMING_SNAKE_CASE			).to(__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				t - self.config.num_train_timesteps // self.num_inference_steps

					__lowerCAmelCase											=				self.alphas_cumprod[t]
					__lowerCAmelCase											=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
					__lowerCAmelCase											=				1 - alpha_prod_t
					__lowerCAmelCase											=				1 - alpha_prod_t_prev

					# 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
					# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
					# without eta.
					# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
					__lowerCAmelCase											=				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

					return variance
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = True,):



					'''simple docstring'''

					__lowerCAmelCase											=				timestep
					__lowerCAmelCase											=				timestep - self.config.num_train_timesteps // self.num_inference_steps

					# 1. compute alphas, betas
					__lowerCAmelCase											=				self.alphas_cumprod[t]
					__lowerCAmelCase											=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
					__lowerCAmelCase											=				1 - alpha_prod_t

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					__lowerCAmelCase											=				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
								__lowerCAmelCase											=				torch.clamp(__SCREAMING_SNAKE_CASE,-1,1			)

					# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
					# substitute formula (7) in the algorithm coming from DDPM paper
					# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
					# DDIM schedule gives the same results as DDPM with eta = 1.0
					# Noise is being reused in 7. and 8., but no impact on quality has
					# been observed.

					# 5. Add noise
					__lowerCAmelCase											=				model_output.device
					__lowerCAmelCase											=				randn_tensor(model_output.shape,generator=__SCREAMING_SNAKE_CASE,device=__SCREAMING_SNAKE_CASE,dtype=model_output.dtype			)
					__lowerCAmelCase											=				self.eta * self._get_variance(__SCREAMING_SNAKE_CASE			) ** 0.5

					__lowerCAmelCase											=				0
					if t > 0 and self.eta > 0:
								__lowerCAmelCase											=				std_dev_t * noise

					# 6. compute "direction pointing to x_t" of formula (12)
					# from https://arxiv.org/pdf/2010.02502.pdf
					__lowerCAmelCase											=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

					# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					__lowerCAmelCase											=				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

					# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
					__lowerCAmelCase											=				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

					# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
					__lowerCAmelCase											=				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

					if not return_dict:
								return (
								    pred_prev_sample,
								    pred_original_sample,
								)

					return RePaintSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE,pred_original_sample=__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.config.num_train_timesteps // self.num_inference_steps

					for i in range(__SCREAMING_SNAKE_CASE			):
								__lowerCAmelCase											=				self.betas[timestep + i]
								if sample.device.type == "mps":
											# randn does not work reproducibly on mps
											__lowerCAmelCase											=				randn_tensor(sample.shape,dtype=sample.dtype,generator=__SCREAMING_SNAKE_CASE			)
											__lowerCAmelCase											=				noise.to(sample.device			)
								else:
											__lowerCAmelCase											=				randn_tensor(sample.shape,generator=__SCREAMING_SNAKE_CASE,device=sample.device,dtype=sample.dtype			)

								# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
								__lowerCAmelCase											=				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

					return sample
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""			)







		def __len__(  self			):



					'''simple docstring'''

					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					: str      =    logging.getLogger(__name__)





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





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





def    _lowerCAmelCase						(						)					->					List[str]:
			# 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.
			__lowerCAmelCase											=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
			__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				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."""	)

			__lowerCAmelCase											=				import_module("""tasks"""	)

			try:
						__lowerCAmelCase											=				getattr(lowercase					,	model_args.task_type	)
						__lowerCAmelCase											=				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"""					,	lowercase	)

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

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

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

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

						for i in range(lowercase	):
									for j in range(lowercase	):
												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(lowercase	) -> Dict:
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(p.predictions					,	p.label_ids	)

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

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

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

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

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

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

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

									results.update(lowercase	)

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

						__lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase											=				trainer.predict(test_dataset.get_dataset()	)
						__lowerCAmelCase						,       __lowerCAmelCase											=				align_predictions(lowercase					,	lowercase	)
						__lowerCAmelCase											=				classification_report(lowercase					,	lowercase	)

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

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

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

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

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

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

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

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

			return results


if __name__ == "__main__":
	main()

