
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 		lowercase_     (  A__						)				->				Tuple:
    """simple docstring"""

    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 		lowercase_     (  A__					,						A__					,						A__						)				->				Optional[Any]:
    """simple docstring"""

    return max(metric_fn(A__					,						A__						) for gt in ground_truths						)



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Tuple:
    """simple docstring"""

    snake_case						=   [line.strip() for line in open(A__					,						"r"						).readlines()]
    snake_case						=   []

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

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

    snake_case						=   100.0 * em / total
    snake_case						=   100.0 * fa / total

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



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Optional[Any]:
    """simple docstring"""

    snake_case						=   args.k
    snake_case						=   [line.strip() for line in open(A__					,						"r"						).readlines()]
    snake_case						=   [line.strip() for line in open(A__					,						"r"						).readlines()]

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

    snake_case						=   100.0 * em / total
    logger.info(F'Precision@{k}: {em: .2f}'						)



def 		lowercase_     (  A__					,						A__					,						A__						)				->				List[Any]:
    """simple docstring"""

    def strip_title(A__						):
        if title.startswith("\""						):
            snake_case						=   title[1:]
        if title.endswith("\""						):
            snake_case						=   title[:-1]
        return title

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

    snake_case						=   rag_model.rag.question_encoder(A__						)
    snake_case						=   question_enc_outputs[0]

    snake_case						=   rag_model.retriever(
        A__					,						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"					,						)
    snake_case						=   rag_model.retriever.index.get_doc_dicts(result.doc_ids						)
    snake_case						=   []
    for docs in all_docs:
        snake_case						=   [strip_title(A__						) for title in docs["title"]]
        provenance_strings.append("\t".join(A__						)						)
    return provenance_strings



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Optional[Any]:
    """simple docstring"""

    with torch.no_grad():
        snake_case						=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            A__					,						return_tensors="pt"					,						padding=A__					,						truncation=A__						)

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

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

        return answers



def 		lowercase_     (  )				->				Any:
    """simple docstring"""

    snake_case						=   argparse.ArgumentParser()
    parser.add_argument(
        "--model_type"					,						choices=["rag_sequence", "rag_token", "bart"]					,						type=A__					,						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=A__					,						choices=["exact", "compressed", "legacy"]					,						type=A__					,						help="RAG model retriever type"					,						)
    parser.add_argument(
        "--index_path"					,						default=A__					,						type=A__					,						help="Path to the retrieval index"					,						)
    parser.add_argument("--n_docs"					,						default=5					,						type=A__					,						help="Number of retrieved docs"						)
    parser.add_argument(
        "--model_name_or_path"					,						default=A__					,						type=A__					,						required=A__					,						help="Path to pretrained checkpoints or model identifier from huggingface.co/models"					,						)
    parser.add_argument(
        "--eval_mode"					,						choices=["e2e", "retrieval"]					,						default="e2e"					,						type=A__					,						help=(
            "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
            " precision@k."
        )					,						)
    parser.add_argument("--k"					,						default=1					,						type=A__					,						help="k for the precision@k calculation"						)
    parser.add_argument(
        "--evaluation_set"					,						default=A__					,						type=A__					,						required=A__					,						help="Path to a file containing evaluation samples"					,						)
    parser.add_argument(
        "--gold_data_path"					,						default=A__					,						type=A__					,						required=A__					,						help="Path to a tab-separated file with gold samples"					,						)
    parser.add_argument(
        "--gold_data_mode"					,						default="qa"					,						type=A__					,						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=A__					,						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=A__					,						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=A__					,						help="Number of beams to be used when generating answers"					,						)
    parser.add_argument("--min_length"					,						default=1					,						type=A__					,						help="Min length of the generated answers"						)
    parser.add_argument("--max_length"					,						default=50					,						type=A__					,						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."					,						)
    snake_case						=   parser.parse_args()
    snake_case						=   torch.device("cuda" if torch.cuda.is_available() else "cpu"						)
    return args



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

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

    snake_case						=   (
        [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"					,						A__						)

    snake_case						=   get_scores if args.eval_mode == "e2e" else get_precision_at_k
    snake_case						=   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(A__					,						args.predictions_path					,						args.gold_data_path						)
            continue

        logger.info("***** Running evaluation for {} *****".format(A__						)						)
        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"						):
            snake_case						=   RagRetriever.from_pretrained(A__					,						**A__						)
            snake_case						=   model_class.from_pretrained(A__					,						retriever=A__					,						**A__						)
            model.retriever.init_retrieval()
        else:
            snake_case						=   model_class.from_pretrained(A__					,						**A__						)
        model.to(args.device						)

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

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


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



_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

