


import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


__a       =					logging.get_logger(__name__)




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->Tuple:







      """simple docstring"""

      lowercase	:      Union[str, Any]          =						nn.functional.normalize(__lowerCAmelCase   )
      lowercase	:      int          =						nn.functional.normalize(__lowerCAmelCase   )
      return torch.mm(__lowerCAmelCase,			normalized_text_embeds.t()   )







class  __SCREAMING_SNAKE_CASE      (	__lowerCamelCase      ):
      A :							Tuple										=   CLIPConfig

      A :							int										=   ["""CLIPEncoderLayer"""]


      def __init__(							self							,  SCREAMING_SNAKE_CASE__						):
            super().__init__(__lowercase						)

            lowercase	:      List[str]          =						CLIPVisionModel(config.vision_config						)
            lowercase	:      Optional[int]          =						nn.Linear(config.vision_config.hidden_size							,  config.projection_dim							,  bias=__lowercase						)

            lowercase	:      str          =						nn.Parameter(torch.ones(17							,  config.projection_dim						)							,  requires_grad=__lowercase						)
            lowercase	:      str          =						nn.Parameter(torch.ones(3							,  config.projection_dim						)							,  requires_grad=__lowercase						)

            lowercase	:      Tuple          =						nn.Parameter(torch.ones(17						)							,  requires_grad=__lowercase						)
            lowercase	:      List[Any]          =						nn.Parameter(torch.ones(3						)							,  requires_grad=__lowercase						)


      @torch.no_grad()
      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      Union[str, Any]          =						self.vision_model(__lowercase						)[1]  # pooled_output
            lowercase	:      Union[str, Any]          =						self.visual_projection(__lowercase						)

            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            lowercase	:      Optional[Any]          =						cosine_distance(__lowercase							,  self.special_care_embeds						).cpu().float().numpy()
            lowercase	:      Union[str, Any]          =						cosine_distance(__lowercase							,  self.concept_embeds						).cpu().float().numpy()

            lowercase	:      Dict          =						[]
            lowercase	:      Optional[int]          =						image_embeds.shape[0]
            for i in range(__lowercase						):
                  lowercase	:      Tuple          =						{'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

                  # increase this value to create a stronger `nfsw` filter
                  # at the cost of increasing the possibility of filtering benign images
                  lowercase	:      Union[str, Any]          =						0.0

                  for concept_idx in range(len(special_cos_dist[0]						)						):
                        lowercase	:      Any          =						special_cos_dist[i][concept_idx]
                        lowercase	:      Any          =						self.special_care_embeds_weights[concept_idx].item()
                        lowercase	:      int          =						round(concept_cos - concept_threshold + adjustment							,  3						)
                        if result_img["special_scores"][concept_idx] > 0:
                              result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]}						)
                              lowercase	:      Tuple          =						0.01

                  for concept_idx in range(len(cos_dist[0]						)						):
                        lowercase	:      str          =						cos_dist[i][concept_idx]
                        lowercase	:      List[str]          =						self.concept_embeds_weights[concept_idx].item()
                        lowercase	:      Tuple          =						round(concept_cos - concept_threshold + adjustment							,  3						)
                        if result_img["concept_scores"][concept_idx] > 0:
                              result_img["bad_concepts"].append(__lowercase						)

                  result.append(__lowercase						)

            lowercase	:      Any          =						[len(res['''bad_concepts''']						) > 0 for res in result]

            return images, has_nsfw_concepts






      @torch.no_grad()
      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      Optional[Any]          =						self.vision_model(__lowercase						)[1]  # pooled_output
            lowercase	:      Optional[Any]          =						self.visual_projection(__lowercase						)

            lowercase	:      Optional[int]          =						cosine_distance(__lowercase							,  self.special_care_embeds						)
            lowercase	:      Optional[int]          =						cosine_distance(__lowercase							,  self.concept_embeds						)

            # increase this value to create a stronger `nsfw` filter
            # at the cost of increasing the possibility of filtering benign images
            lowercase	:      List[Any]          =						0.0

            lowercase	:      Optional[int]          =						special_cos_dist - self.special_care_embeds_weights + adjustment
            # special_scores = special_scores.round(decimals=3)
            lowercase	:      Any          =						torch.any(special_scores > 0							,  dim=1						)
            lowercase	:      Any          =						special_care * 0.01
            lowercase	:      List[Any]          =						special_adjustment.unsqueeze(1						).expand(-1							,  cos_dist.shape[1]						)

            lowercase	:      Any          =						(cos_dist - self.concept_embeds_weights) + special_adjustment
            # concept_scores = concept_scores.round(decimals=3)
            lowercase	:      Dict          =						torch.any(concept_scores > 0							,  dim=1						)

            return images, has_nsfw_concepts






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


A__								=						logging.getLogger(__name__)


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							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=__lowerCamelCase  ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
		__lowerCAmelCase	:    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.
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,							)


@dataclass
class       a							:
		__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=__lowerCamelCase  ,							metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,							)
		__lowerCAmelCase	:    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."""
		        )
		    }  ,							)
		__lowerCAmelCase	:    bool											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      Any     		=	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.
								snake_case__		, snake_case__		, snake_case__	:      str     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								snake_case__		, snake_case__		, snake_case__	:      List[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.'''      )

				snake_case__	:      int     		=	import_module('''tasks'''      )
				try:
								snake_case__	:      Optional[int]     		=	getattr(__lowerCAmelCase  ,  model_args.task_type      )
								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 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'''  ,  __lowerCAmelCase      )

				# Set seed
				set_seed(training_args.seed      )

				# Prepare CONLL-2003 task
				snake_case__	:      Optional[int]     		=	token_classification_task.get_labels(data_args.labels      )
				snake_case__	:      Dict[int, str]     		=	dict(enumerate(__lowerCAmelCase      )      )
				snake_case__	:      Optional[Any]     		=	len(__lowerCAmelCase      )

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

				snake_case__	:      List[str]     		=	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  ,  )
				snake_case__	:      List[str]     		=	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  ,  )
				snake_case__	:      Tuple     		=	AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path  ,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )  ,  config=__lowerCAmelCase  ,  cache_dir=model_args.cache_dir  ,  )

				# Get datasets
				snake_case__	:      Dict     		=	(
				    TokenClassificationDataset(
				        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
				)
				snake_case__	:      Optional[Any]     		=	(
				    TokenClassificationDataset(
				        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  ,  __lowerCAmelCase      ) -> Tuple[List[int], List[int]]:
								snake_case__	:      Any     		=	np.argmax(__lowerCAmelCase  ,  axis=2      )

								snake_case__		, snake_case__	:      List[Any]     		=	preds.shape

								snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
								snake_case__	:      int     		=	[[] for _ in range(__lowerCAmelCase      )]

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

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

				# Initialize our Trainer
				snake_case__	:      Tuple     		=	Trainer(
				    model=__lowerCAmelCase  ,  args=__lowerCAmelCase  ,  train_dataset=__lowerCAmelCase  ,  eval_dataset=__lowerCAmelCase  ,  compute_metrics=__lowerCAmelCase  ,  data_collator=__lowerCAmelCase  ,  )

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

								snake_case__	:      Tuple     		=	trainer.evaluate()

								snake_case__	:      Optional[Any]     		=	os.path.join(training_args.output_dir  ,  '''eval_results.txt'''      )
								if trainer.is_world_process_zero():
												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:
								snake_case__	:      Optional[Any]     		=	TokenClassificationDataset(
								    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  ,  )

								snake_case__		, snake_case__		, snake_case__	:      List[str]     		=	trainer.predict(__lowerCAmelCase      )
								snake_case__		, snake_case__	:      int     		=	align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      )

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

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

				return results



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				main()


if __name__ == "__main__":
				main()

