
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()


SCREAMING_SNAKE_CASE   :		Any					 =       logging.getLogger(__name__)






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






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



def        lowerCamelCase_  (   ):
				# 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(__UpperCamelCase ,						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''' ,						__UpperCamelCase     )

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

				# 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=__UpperCamelCase ,						idalabel=__UpperCamelCase ,						labelaid={label: i for i, label in enumerate(__UpperCamelCase     )} ,						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=__UpperCamelCase ,						cache_dir=model_args.cache_dir ,						)

				# Get datasets
				A_       	=  (
				    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
				)
				A_       	=  (
				    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]]:
								A_       	=  np.argmax(__UpperCamelCase ,						axis=2     )
								A_	,					A_       	=  preds.shape
								A_       	=  [[] for _ in range(__UpperCamelCase     )]
								A_       	=  [[] for _ in range(__UpperCamelCase     )]

								for i in range(__UpperCamelCase     ):
												for j in range(__UpperCamelCase     ):
																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(__UpperCamelCase     ) -> Dict:
								A_	,					A_       	=  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
				A_       	=  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
				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(__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:
								A_       	=  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 ,						)

								A_	,					A_	,					A_       	=  trainer.predict(test_dataset.get_dataset()     )
								A_	,					A_       	=  align_predictions(__UpperCamelCase ,						__UpperCamelCase     )
								A_       	=  classification_report(__UpperCamelCase ,						__UpperCamelCase     )

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

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

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

								# Save predictions
								A_       	=  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:
																A_       	=  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]:
																								A_       	=  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()


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker