
"""simple docstring"""


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


_lowercase				 =					logging.getLogger(__name__)







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


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







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


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






def    _snake_case					(		):
	# 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, 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.
		A ,   A ,   A     = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
	else:
		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(snake_case__						,   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 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'						,   snake_case__  )

	# Set seed
	set_seed(training_args.seed  )

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

	# 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=snake_case__						,   idalabel=snake_case__						,   labelaid={label: i for i, label in enumerate(snake_case__  )}						,   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						,   )
	A     = AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path						,   from_tf=bool('.ckpt' in model_args.model_name_or_path  )						,   config=snake_case__						,   cache_dir=model_args.cache_dir						,   )

	# Get datasets
	A     = (
	    TokenClassificationDataset(
	        token_classification_task=snake_case__						,   data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   labels=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
	)
	A     = (
	    TokenClassificationDataset(
	        token_classification_task=snake_case__						,   data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   labels=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(snake_case__ :  np.ndarray						,   snake_case__ :  np.ndarray  ) -> Tuple[List[int], List[int]]:
		A     = np.argmax(snake_case__						,   axis=2  )

		A ,   A     = preds.shape

		A     = [[] for _ in range(snake_case__  )]
		A     = [[] for _ in range(snake_case__  )]

		for i in range(snake_case__  ):
			for j in range(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(snake_case__ :  EvalPrediction  ) -> Dict:
		A ,   A     = align_predictions(p.predictions						,   p.label_ids  )
		return {
		    "accuracy_score": accuracy_score(snake_case__						,   snake_case__  ),
		    "precision": precision_score(snake_case__						,   snake_case__  ),
		    "recall": recall_score(snake_case__						,   snake_case__  ),
		    "f1": fa_score(snake_case__						,   snake_case__  ),
		}

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

	# Initialize our Trainer
	A     = Trainer(
	    model=snake_case__						,   args=snake_case__						,   train_dataset=snake_case__						,   eval_dataset=snake_case__						,   compute_metrics=snake_case__						,   data_collator=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
	A     = {}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'  )

		A     = trainer.evaluate()

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

			results.update(snake_case__  )

    # Predict
	if training_args.do_predict:
		A     = TokenClassificationDataset(
		    token_classification_task=snake_case__						,   data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   labels=snake_case__						,   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(snake_case__  )
		A ,   A     = align_predictions(snake_case__						,   snake_case__  )

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

        # Save predictions
		A     = os.path.join(training_args.output_dir						,   'test_predictions.txt'  )
		if trainer.is_world_process_zero():
			with open(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(snake_case__						,   snake_case__						,   snake_case__  )

	return results






def    _snake_case					(		snake_case__ :  Any  ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()




'''simple docstring'''





from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=1E-12			) -> List[Any]:
  __lowerCamelCase			:			int       			=					jnp.divide(emb_a.T			,jnp.clip(jnp.linalg.norm(_lowerCAmelCase			,axis=1			)			,a_min=_lowerCAmelCase			)			).T
  __lowerCamelCase			:			Tuple       			=					jnp.divide(emb_a.T			,jnp.clip(jnp.linalg.norm(_lowerCAmelCase			,axis=1			)			,a_min=_lowerCAmelCase			)			).T
  return jnp.matmul(_lowerCAmelCase			,norm_emb_a.T			)






class    lowerCamelCase_       (							nn.Module	):
  """simple docstring"""




  a_				   =42
  a_				   =jnp.floataa




  def 		_lowercase	(					self :      Optional[Any]  )   ->   Tuple:
    __lowerCamelCase			:			Optional[int]       			=					FlaxCLIPVisionModule(self.config.vision_config  )
    __lowerCamelCase			:			Any       			=					nn.Dense(self.config.projection_dim     ,      use_bias=_a     ,      dtype=self.dtype  )

    __lowerCamelCase			:			str       			=					self.param('concept_embeds'     ,      jax.nn.initializers.ones     ,      (17, self.config.projection_dim)  )
    __lowerCamelCase			:			str       			=					self.param(
        'special_care_embeds'     ,      jax.nn.initializers.ones     ,      (3, self.config.projection_dim)  )

    __lowerCamelCase			:			Optional[int]       			=					self.param('concept_embeds_weights'     ,      jax.nn.initializers.ones     ,      (17,)  )
    __lowerCamelCase			:			Dict       			=					self.param('special_care_embeds_weights'     ,      jax.nn.initializers.ones     ,      (3,)  )




  def __call__(					self :      Tuple     ,      _a :      Any  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					self.vision_model(_a  )[1]
    __lowerCamelCase			:			Optional[int]       			=					self.visual_projection(_a  )

    __lowerCamelCase			:			Optional[Any]       			=					jax_cosine_distance(_a     ,      self.special_care_embeds  )
    __lowerCamelCase			:			Tuple       			=					jax_cosine_distance(_a     ,      self.concept_embeds  )

    # increase this value to create a stronger `nfsw` filter
    # at the cost of increasing the possibility of filtering benign image inputs
    __lowerCamelCase			:			Dict       			=					0.0

    __lowerCamelCase			:			str       			=					special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
    __lowerCamelCase			:			Optional[int]       			=					jnp.round(_a     ,      3  )
    __lowerCamelCase			:			Dict       			=					jnp.any(special_scores > 0     ,      axis=1     ,      keepdims=_a  )
    # Use a lower threshold if an image has any special care concept
    __lowerCamelCase			:			Dict       			=					is_special_care * 0.01

    __lowerCamelCase			:			Optional[int]       			=					cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
    __lowerCamelCase			:			List[str]       			=					jnp.round(_a     ,      3  )
    __lowerCamelCase			:			List[Any]       			=					jnp.any(concept_scores > 0     ,      axis=1  )

    return has_nsfw_concepts






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =CLIPConfig
  a_				   ="""clip_input"""
  a_				   =FlaxStableDiffusionSafetyCheckerModule




  def __init__(					self :      List[str]     ,      _a :      CLIPConfig     ,      _a :      Optional[Tuple] = None     ,      _a :      int = 0     ,      _a :      jnp.dtype = jnp.floataa     ,      _a :      bool = True     ,      **_a :      List[Any]     ,      )   ->   List[Any]:
    if input_shape is None:
      __lowerCamelCase			:			str       			=					(1, 224, 224, 3)
    __lowerCamelCase			:			List[str]       			=					self.module_class(config=_a     ,      dtype=_a     ,      **_a  )
    super().__init__(_a     ,      _a     ,      input_shape=_a     ,      seed=_a     ,      dtype=_a     ,      _do_init=_do_init  )




  def 		_lowercase	(					self :      int     ,      _a :      jax.random.KeyArray     ,      _a :      Tuple     ,      _a :      FrozenDict = None  )   ->   FrozenDict:
    # init input tensor
    __lowerCamelCase			:			Optional[int]       			=					jax.random.normal(_a     ,      _a  )

    __lowerCamelCase			,__lowerCamelCase			:			Optional[Any]       			=					jax.random.split(_a  )
    __lowerCamelCase			:			int       			=					{'params': params_rng, 'dropout': dropout_rng}

    __lowerCamelCase			:			Tuple       			=					self.module.init(_a     ,      _a  )['params']

    return random_params




  def __call__(					self :      Optional[int]     ,      _a :      int     ,      _a :      dict = None     ,      )   ->   Dict:
    __lowerCamelCase			:			str       			=					jnp.transpose(_a     ,      (0, 2, 3, 1)  )

    return self.module.apply(
        {'params': params or self.params}     ,      jnp.array(_a     ,      dtype=jnp.floataa  )     ,      rngs={}     ,      )






