



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()


UpperCamelCase__	:     Any    =			logging.getLogger(__name__)






@dataclass
class 						_UpperCamelCase  :


			'''simple docstring'''

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






@dataclass
class 						_UpperCamelCase  :


			'''simple docstring'''

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


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		str   	=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		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.""" )

		__SCREAMING_SNAKE_CASE	:		Any   	=		import_module("""tasks""" )

		try:
				__SCREAMING_SNAKE_CASE	:		str   	=		getattr(_lowerCamelCase	,				model_args.task_type )
				__SCREAMING_SNAKE_CASE	:		TokenClassificationTask   	=		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"""	,				_lowerCamelCase )

		# Prepare Token Classification task
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		token_classification_task.get_labels(data_args.labels )
		__SCREAMING_SNAKE_CASE	:		Dict[int, str]   	=		dict(enumerate(_lowerCamelCase ) )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		len(_lowerCamelCase )

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

		__SCREAMING_SNAKE_CASE	:		Tuple   	=		AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=_lowerCamelCase	,				idalabel=_lowerCamelCase	,				labelaid={label: i for i, label in enumerate(_lowerCamelCase )}	,				cache_dir=model_args.cache_dir	,				)
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		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():
				__SCREAMING_SNAKE_CASE	:		Dict   	=		TFAutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path	,				from_pt=bool(""".bin""" in model_args.model_name_or_path )	,				config=_lowerCamelCase	,				cache_dir=model_args.cache_dir	,				)

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

				for i in range(_lowerCamelCase ):
						for j in range(_lowerCamelCase ):
								if label_ids[i, j] != -1_00:
										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(_lowerCamelCase:       Optional[int] ) -> Dict:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		align_predictions(p.predictions	,				p.label_ids )

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

		# Initialize our Trainer
		__SCREAMING_SNAKE_CASE	:		Dict   	=		TFTrainer(
		    model=_lowerCamelCase	,				args=_lowerCamelCase	,				train_dataset=train_dataset.get_dataset() if train_dataset else None	,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None	,				compute_metrics=_lowerCamelCase	,				)

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

		# Evaluation
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***""" )

				__SCREAMING_SNAKE_CASE	:		str   	=		trainer.evaluate()
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		os.path.join(training_args.output_dir	,				"""eval_results.txt""" )

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

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

						results.update(_lowerCamelCase )

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

				__SCREAMING_SNAKE_CASE	:		List[str]   	=		trainer.predict(test_dataset.get_dataset() )
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		align_predictions(_lowerCamelCase	,				_lowerCamelCase )
				__SCREAMING_SNAKE_CASE	:		str   	=		classification_report(_lowerCamelCase	,				_lowerCamelCase )

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

				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(training_args.output_dir	,				"""test_results.txt""" )

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

				# Save predictions
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		os.path.join(training_args.output_dir	,				"""test_predictions.txt""" )

				with open(_lowerCamelCase	,				"""w""" ) as writer:
						with open(os.path.join(data_args.data_dir	,				"""test.txt""" )	,				"""r""" ) as f:
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		0

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

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

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

		return results


if __name__ == "__main__":
						main()




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass