
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__A       =      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def 	_A						(							lowercase__		):
						if "token" in model_name_or_path:
												return "rag_token"
						if "sequence" in model_name_or_path:
												return "rag_sequence"
						if "bart" in model_name_or_path:
												return "bart"
						return None





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						return max(metric_fn(lowercase__						,						lowercase__		) for gt in ground_truths		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    [line.strip() for line in open(lowercase__						,						"""r"""		).readlines()]
						lowercase__								=    []

						if args.gold_data_mode == "qa":
												lowercase__								=    pd.read_csv(lowercase__						,						sep="""\t"""						,						header=lowercase__		)
												for answer_list in data[1]:
																		lowercase__								=    ast.literal_eval(lowercase__		)
																		answers.append(lowercase__		)
						else:
												lowercase__								=    [line.strip() for line in open(lowercase__						,						"""r"""		).readlines()]
												lowercase__								=    [[reference] for reference in references]

						lowercase__								=    0
						for prediction, ground_truths in zip(lowercase__						,						lowercase__		):
												total += 1
												em += metric_max_over_ground_truths(lowercase__						,						lowercase__						,						lowercase__		)
												fa += metric_max_over_ground_truths(lowercase__						,						lowercase__						,						lowercase__		)

						lowercase__								=    1_0_0.0 * em / total
						lowercase__								=    1_0_0.0 * fa / total

						logger.info(f'''F1: {fa:.2f}'''		)
						logger.info(f'''EM: {em:.2f}'''		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    args.k
						lowercase__								=    [line.strip() for line in open(lowercase__						,						"""r"""		).readlines()]
						lowercase__								=    [line.strip() for line in open(lowercase__						,						"""r"""		).readlines()]

						lowercase__								=    0
						for hypo, reference in zip(lowercase__						,						lowercase__		):
												lowercase__								=    set(hypo.split("""\t"""		)[:k]		)
												lowercase__								=    set(reference.split("""\t"""		)		)
												total += 1
												em += len(hypo_provenance & ref_provenance		) / k

						lowercase__								=    1_0_0.0 * em / total
						logger.info(f'''Precision@{k}: {em: .2f}'''		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						def strip_title(lowercase__		):
												if title.startswith("""\""""		):
																		lowercase__								=    title[1:]
												if title.endswith("""\""""		):
																		lowercase__								=    title[:-1]
												return title

						lowercase__								=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowercase__						,						return_tensors="""pt"""						,						padding=lowercase__						,						truncation=lowercase__						,						)["""input_ids"""].to(args.device		)

						lowercase__								=    rag_model.rag.question_encoder(lowercase__		)
						lowercase__								=    question_enc_outputs[0]

						lowercase__								=    rag_model.retriever(
						    lowercase__						,						question_enc_pool_output.cpu().detach().to(torch.floataa		).numpy()						,						prefix=rag_model.rag.generator.config.prefix						,						n_docs=rag_model.config.n_docs						,						return_tensors="""pt"""						,						)
						lowercase__								=    rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
						lowercase__								=    []
						for docs in all_docs:
												lowercase__								=    [strip_title(lowercase__		) for title in docs["""title"""]]
												provenance_strings.append("""\t""".join(lowercase__		)		)
						return provenance_strings





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						with torch.no_grad():
												lowercase__								=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    lowercase__						,						return_tensors="""pt"""						,						padding=lowercase__						,						truncation=lowercase__		)

												lowercase__								=    inputs_dict.input_ids.to(args.device		)
												lowercase__								=    inputs_dict.attention_mask.to(args.device		)
												lowercase__								=    rag_model.generate(  # rag_model overwrites generate
												    lowercase__						,						attention_mask=lowercase__						,						num_beams=args.num_beams						,						min_length=args.min_length						,						max_length=args.max_length						,						early_stopping=lowercase__						,						num_return_sequences=1						,						bad_words_ids=[[0, 0]]						,						)
												lowercase__								=    rag_model.retriever.generator_tokenizer.batch_decode(lowercase__						,						skip_special_tokens=lowercase__		)

												if args.print_predictions:
																		for q, a in zip(lowercase__						,						lowercase__		):
																								logger.info("""Q: {} - A: {}""".format(lowercase__						,						lowercase__		)		)

												return answers





def 	_A						(							):
						lowercase__								=    argparse.ArgumentParser()
						parser.add_argument(
						    """--model_type"""						,						choices=["""rag_sequence""", """rag_token""", """bart"""]						,						type=lowercase__						,						help=(
						        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
						        """ model_name_or_path"""
						    )						,						)
						parser.add_argument(
						    """--index_name"""						,						default=lowercase__						,						choices=["""exact""", """compressed""", """legacy"""]						,						type=lowercase__						,						help="""RAG model retriever type"""						,						)
						parser.add_argument(
						    """--index_path"""						,						default=lowercase__						,						type=lowercase__						,						help="""Path to the retrieval index"""						,						)
						parser.add_argument("""--n_docs"""						,						default=5						,						type=lowercase__						,						help="""Number of retrieved docs"""		)
						parser.add_argument(
						    """--model_name_or_path"""						,						default=lowercase__						,						type=lowercase__						,						required=lowercase__						,						help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""						,						)
						parser.add_argument(
						    """--eval_mode"""						,						choices=["""e2e""", """retrieval"""]						,						default="""e2e"""						,						type=lowercase__						,						help=(
						        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
						        """ precision@k."""
						    )						,						)
						parser.add_argument("""--k"""						,						default=1						,						type=lowercase__						,						help="""k for the precision@k calculation"""		)
						parser.add_argument(
						    """--evaluation_set"""						,						default=lowercase__						,						type=lowercase__						,						required=lowercase__						,						help="""Path to a file containing evaluation samples"""						,						)
						parser.add_argument(
						    """--gold_data_path"""						,						default=lowercase__						,						type=lowercase__						,						required=lowercase__						,						help="""Path to a tab-separated file with gold samples"""						,						)
						parser.add_argument(
						    """--gold_data_mode"""						,						default="""qa"""						,						type=lowercase__						,						choices=["""qa""", """ans"""]						,						help=(
						        """Format of the gold data file"""
						        """qa - a single line in the following format: question [tab] answer_list"""
						        """ans - a single line of the gold file contains the expected answer string"""
						    )						,						)
						parser.add_argument(
						    """--predictions_path"""						,						type=lowercase__						,						default="""predictions.txt"""						,						help="""Name of the predictions file, to be stored in the checkpoints directory"""						,						)
						parser.add_argument(
						    """--eval_all_checkpoints"""						,						action="""store_true"""						,						help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""						,						)
						parser.add_argument(
						    """--eval_batch_size"""						,						default=8						,						type=lowercase__						,						help="""Batch size per GPU/CPU for evaluation."""						,						)
						parser.add_argument(
						    """--recalculate"""						,						help="""Recalculate predictions even if the prediction file exists"""						,						action="""store_true"""						,						)
						parser.add_argument(
						    """--num_beams"""						,						default=4						,						type=lowercase__						,						help="""Number of beams to be used when generating answers"""						,						)
						parser.add_argument("""--min_length"""						,						default=1						,						type=lowercase__						,						help="""Min length of the generated answers"""		)
						parser.add_argument("""--max_length"""						,						default=50						,						type=lowercase__						,						help="""Max length of the generated answers"""		)

						parser.add_argument(
						    """--print_predictions"""						,						action="""store_true"""						,						help="""If True, prints predictions while evaluating."""						,						)
						parser.add_argument(
						    """--print_docs"""						,						action="""store_true"""						,						help="""If True, prints docs retried while generating."""						,						)
						lowercase__								=    parser.parse_args()
						lowercase__								=    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
						return args





def 	_A						(							lowercase__		):
						lowercase__								=    {}
						if args.model_type is None:
												lowercase__								=    infer_model_type(args.model_name_or_path		)
												assert args.model_type is not None
						if args.model_type.startswith("""rag"""		):
												lowercase__								=    RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
												lowercase__								=    args.n_docs
												if args.index_name is not None:
																		lowercase__								=    args.index_name
												if args.index_path is not None:
																		lowercase__								=    args.index_path
						else:
												lowercase__								=    BartForConditionalGeneration

						lowercase__								=    (
						    [f.path for f in os.scandir(args.model_name_or_path		) if f.is_dir()]
						    if args.eval_all_checkpoints
						    else [args.model_name_or_path]
						)

						logger.info("""Evaluate the following checkpoints: %s"""						,						lowercase__		)

						lowercase__								=    get_scores if args.eval_mode == """e2e""" else get_precision_at_k
						lowercase__								=    evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

						for checkpoint in checkpoints:
												if os.path.exists(args.predictions_path		) and (not args.recalculate):
																		logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path		)		)
																		score_fn(lowercase__						,						args.predictions_path						,						args.gold_data_path		)
																		continue

												logger.info("""***** Running evaluation for {} *****""".format(lowercase__		)		)
												logger.info("""  Batch size = %d"""						,						args.eval_batch_size		)
												logger.info("""  Predictions will be stored under {}""".format(args.predictions_path		)		)

												if args.model_type.startswith("""rag"""		):
																		lowercase__								=    RagRetriever.from_pretrained(lowercase__						,						**lowercase__		)
																		lowercase__								=    model_class.from_pretrained(lowercase__						,						retriever=lowercase__						,						**lowercase__		)
																		model.retriever.init_retrieval()
												else:
																		lowercase__								=    model_class.from_pretrained(lowercase__						,						**lowercase__		)
												model.to(args.device		)

												with open(args.evaluation_set						,						"""r"""		) as eval_file, open(args.predictions_path						,						"""w"""		) as preds_file:
																		lowercase__								=    []
																		for line in tqdm(lowercase__		):
																								questions.append(line.strip()		)
																								if len(lowercase__		) == args.eval_batch_size:
																														lowercase__								=    evaluate_batch_fn(lowercase__						,						lowercase__						,						lowercase__		)
																														preds_file.write("""\n""".join(lowercase__		) + """\n"""		)
																														preds_file.flush()
																														lowercase__								=    []
																		if len(lowercase__		) > 0:
																								lowercase__								=    evaluate_batch_fn(lowercase__						,						lowercase__						,						lowercase__		)
																								preds_file.write("""\n""".join(lowercase__		)		)
																								preds_file.flush()

																		score_fn(lowercase__						,						args.predictions_path						,						args.gold_data_path		)


if __name__ == "__main__":
				__A       =      get_args()
				main(args)








'''simple docstring'''



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()


__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=__UpperCAmelCase						,  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=__UpperCAmelCase						,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
			lowerCamelCase							:							bool    						=					field(default=__UpperCAmelCase						,  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=__UpperCAmelCase						,  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(
			    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=__UpperCAmelCase						,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)





def 	_A						(							):
						# 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.
						lowercase__								=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
						lowercase__	,  lowercase__	,  lowercase__								=    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__								=    import_module("""tasks"""		)

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

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

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

						lowercase__								=    AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,						num_labels=lowercase__						,						idalabel=lowercase__						,						labelaid={label: i for i, label in enumerate(lowercase__		)}						,						cache_dir=model_args.cache_dir						,						)
						lowercase__								=    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__								=    TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path						,						from_pt=bool(""".bin""" in model_args.model_name_or_path		)						,						config=lowercase__						,						cache_dir=model_args.cache_dir						,						)

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

												for i in range(lowercase__		):
																		for j in range(lowercase__		):
																								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(lowercase__		) -> Dict:
												lowercase__	,  lowercase__								=    align_predictions(p.predictions						,						p.label_ids		)

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

						# Initialize our Trainer
						lowercase__								=    TFTrainer(
						    model=lowercase__						,						args=lowercase__						,						train_dataset=train_dataset.get_dataset() if train_dataset else None						,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,						compute_metrics=lowercase__						,						)

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

						# Evaluation
						lowercase__								=    {}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""		)

												lowercase__								=    trainer.evaluate()
												lowercase__								=    os.path.join(training_args.output_dir						,						"""eval_results.txt"""		)

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

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

																		results.update(lowercase__		)

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

												lowercase__	,  lowercase__	,  lowercase__								=    trainer.predict(test_dataset.get_dataset()		)
												lowercase__	,  lowercase__								=    align_predictions(lowercase__						,						lowercase__		)
												lowercase__								=    classification_report(lowercase__						,						lowercase__		)

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

												lowercase__								=    os.path.join(training_args.output_dir						,						"""test_results.txt"""		)

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

												# Save predictions
												lowercase__								=    os.path.join(training_args.output_dir						,						"""test_predictions.txt"""		)

												with open(lowercase__						,						"""w"""		) as writer:
																		with open(os.path.join(data_args.data_dir						,						"""test.txt"""		)						,						"""r"""		) as f:
																								lowercase__								=    0

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

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				lowercase__								=    line.split()[0] + """ """ + preds_list[example_id].pop(0		) + """\n"""

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

						return results


if __name__ == "__main__":
				main()






