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													=		logging.getLogger(__name__)



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



@dataclass
class 						snake_case_ :
   __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=128						,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=__A						,metadata={"help": "Overwrite the cached training and evaluation sets"} )



def     lowercase_       (    ):
    lowercase__				:     Tuple					      =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    lowercase__				:     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.")

    lowercase__				:     str					      =       import_module("tasks")

    try:
        lowercase__				:     Dict					      =       getattr(_UpperCAmelCase	,			model_args.task_type)
        lowercase__				:     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"	,			_UpperCAmelCase)

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

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

    lowercase__				:     Optional[Any]					      =       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	,			)
    lowercase__				:     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():
        lowercase__				:     Optional[int]					      =       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
    lowercase__				:     str					      =       (
        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
    )
    lowercase__				:     List[str]					      =       (
        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(_lowerCamelCase							: Tuple	,			_lowerCamelCase							: Dict) -> Tuple[List[int], List[int]]:
        lowercase__				:     Optional[Any]					      =       np.argmax(_UpperCAmelCase	,			axis=2)
        lowercase__				:     Tuple					      =       preds.shape
        lowercase__				:     Tuple					      =       [[] for _ in range(_UpperCAmelCase)]
        lowercase__				:     Optional[int]					      =       [[] for _ in range(_UpperCAmelCase)]

        for i in range(_UpperCAmelCase):
            for j in range(_UpperCAmelCase):
                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(_lowerCamelCase							: List[Any]) -> Dict:
        lowercase__				:     str					      =       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
    lowercase__				:     Tuple					      =       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
    lowercase__				:     List[str]					      =       {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        lowercase__				:     Tuple					      =       trainer.evaluate()
        lowercase__				:     Optional[int]					      =       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:
        lowercase__				:     List[Any]					      =       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	,			)

        lowercase__				:     Optional[int]					      =       trainer.predict(test_dataset.get_dataset())
        lowercase__				:     List[str]					      =       align_predictions(_UpperCAmelCase	,			_UpperCAmelCase)
        lowercase__				:     int					      =       classification_report(_UpperCAmelCase	,			_UpperCAmelCase)

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

        lowercase__				:     Optional[int]					      =       os.path.join(training_args.output_dir	,			"test_results.txt")

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

        # Save predictions
        lowercase__				:     int					      =       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:
                lowercase__				:     int					      =       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]:
                        lowercase__				:     Optional[int]					      =       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()




import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       RetriBertConfig
   __A			:	str        =       None
   __A			:	Any        =       "retribert"






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[Any]						)      ->		List[str]:
       if isinstance(lowercase_			,		nn.Linear						):
           module.weight.data.normal_(mean=0.0			,		std=self.config.initializer_range						)
           if module.bias is not None:
               module.bias.data.zero_()
       elif isinstance(lowercase_			,		nn.Embedding						):
           module.weight.data.normal_(mean=0.0			,		std=self.config.initializer_range						)
           if module.padding_idx is not None:
               module.weight.data[module.padding_idx].zero_()
       elif isinstance(lowercase_			,		nn.LayerNorm						):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0						)



UpperCamelCase													=		R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''



@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."						,__A						,)
class 						snake_case_ (	__A ):






   def __init__(   self       :  Dict			,		lowercase_       :  RetriBertConfig						)      ->		None:
       super().__init__(lowercase_						)
       lowercase__				:     str					      =       config.projection_dim

       lowercase__				:     Optional[int]					      =       BertModel(lowercase_						)
       lowercase__				:     Union[str, Any]					      =       None if config.share_encoders else BertModel(lowercase_						)
       lowercase__				:     Tuple					      =       nn.Dropout(config.hidden_dropout_prob						)
       lowercase__				:     Any					      =       nn.Linear(config.hidden_size			,		config.projection_dim			,		bias=lowercase_						)
       lowercase__				:     str					      =       nn.Linear(config.hidden_size			,		config.projection_dim			,		bias=lowercase_						)

       lowercase__				:     Optional[Any]					      =       nn.CrossEntropyLoss(reduction="mean"						)

       # Initialize weights and apply final processing
       self.post_init()






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  str			,		lowercase_       :  Optional[int]			,		lowercase_       :  Tuple			,		lowercase_       :  Optional[int]=-1			,		)      ->		List[str]:
       # reproduces BERT forward pass with checkpointing
       if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
           return sent_encoder(lowercase_			,		attention_mask=lowercase_						)[1]
       else:
           # prepare implicit variables
           lowercase__				:     Union[str, Any]					      =       input_ids.device
           lowercase__				:     Union[str, Any]					      =       input_ids.size()
           lowercase__				:     int					      =       torch.zeros(lowercase_			,		dtype=torch.long			,		device=lowercase_						)
           lowercase__				:     List[Any]					      =       [None] * sent_encoder.config.num_hidden_layers
           lowercase__				:     torch.Tensor					      =       sent_encoder.get_extended_attention_mask(
               lowercase_			,		lowercase_						)

           # define function for checkpointing
           def partial_encode(*lowercase_       :  int						):
               lowercase__				:     List[Any]					      =       sent_encoder.encoder(
                   inputs[0]			,		attention_mask=inputs[1]			,		head_mask=lowercase_			,		)
               lowercase__				:     Dict					      =       encoder_outputs[0]
               lowercase__				:     List[Any]					      =       sent_encoder.pooler(lowercase_						)
               return pooled_output

           # run embedding layer on everything at once
           lowercase__				:     Optional[int]					      =       sent_encoder.embeddings(
               input_ids=lowercase_			,		position_ids=lowercase_			,		token_type_ids=lowercase_			,		inputs_embeds=lowercase_						)
           # run encoding and pooling on one mini-batch at a time
           lowercase__				:     str					      =       []
           for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
               lowercase__				:     int					      =       embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               lowercase__				:     int					      =       extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
               lowercase__				:     int					      =       checkpoint.checkpoint(lowercase_			,		lowercase_			,		lowercase_						)
               pooled_output_list.append(lowercase_						)
           return torch.cat(lowercase_			,		dim=0						)






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  Any			,		lowercase_       :  Any=None			,		lowercase_       :  Union[str, Any]=-1			,		)      ->		List[str]:
       lowercase__				:     Optional[Any]					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query			,		lowercase_			,		)
       return self.project_query(lowercase_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Dict=-1			,		)      ->		Optional[int]:
       lowercase__				:     str					      =       self.embed_sentences_checkpointed(
           lowercase_			,		lowercase_			,		self.bert_query if self.bert_doc is None else self.bert_doc			,		lowercase_			,		)
       return self.project_doc(lowercase_						)







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  torch.LongTensor			,		lowercase_       :  Optional[torch.FloatTensor]			,		lowercase_       :  torch.LongTensor			,		lowercase_       :  Optional[torch.FloatTensor]			,		lowercase_       :  int = -1			,		)      ->		torch.FloatTensor:
       lowercase__				:     Tuple					      =       input_ids_query.device
       lowercase__				:     str					      =       self.embed_questions(lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     Any					      =       self.embed_answers(lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     List[str]					      =       torch.mm(lowercase_			,		a_reps.t()						)
       lowercase__				:     Optional[int]					      =       self.ce_loss(lowercase_			,		torch.arange(compare_scores.shape[1]						).to(lowercase_						)						)
       lowercase__				:     str					      =       self.ce_loss(compare_scores.t()			,		torch.arange(compare_scores.shape[0]						).to(lowercase_						)						)
       lowercase__				:     Tuple					      =       (loss_qa + loss_aq) / 2
       return loss



