



'''simple docstring'''





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


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

transformers_logging.set_verbosity_info()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    return max(metric_fn(UpperCamelCase							,    UpperCamelCase			) for gt in ground_truths			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[line.strip() for line in open(UpperCamelCase							,    """r"""			).readlines()]
    lowerCAmelCase__ :  Union[str, Any]							      =					[]

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

    lowerCAmelCase__ :  Optional[Any]							      =					0
    for prediction, ground_truths in zip(UpperCamelCase							,    UpperCamelCase			):
        total += 1
        em += metric_max_over_ground_truths(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)
        fa += metric_max_over_ground_truths(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)

    lowerCAmelCase__ :  str							      =					100.0 * em / total
    lowerCAmelCase__ :  Optional[int]							      =					100.0 * fa / total

    logger.info(f"""F1: {fa:.2f}"""			)
    logger.info(f"""EM: {em:.2f}"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					args.k
    lowerCAmelCase__ :  Optional[int]							      =					[line.strip() for line in open(UpperCamelCase							,    """r"""			).readlines()]
    lowerCAmelCase__ :  Optional[int]							      =					[line.strip() for line in open(UpperCamelCase							,    """r"""			).readlines()]

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

    lowerCAmelCase__ :  Optional[int]							      =					100.0 * em / total
    logger.info(f"""Precision@{k}: {em: .2f}"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    def strip_title(UpperCamelCase			):
        if title.startswith("""\""""			):
            lowerCAmelCase__ :  Optional[Any]							      =					title[1:]
        if title.endswith("""\""""			):
            lowerCAmelCase__ :  int							      =					title[:-1]
        return title

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

    lowerCAmelCase__ :  Optional[int]							      =					rag_model.rag.question_encoder(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					question_enc_outputs[0]

    lowerCAmelCase__ :  List[str]							      =					rag_model.retriever(
        UpperCamelCase							,    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"""							,    )
    lowerCAmelCase__ :  List[str]							      =					rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
    lowerCAmelCase__ :  List[Any]							      =					[]
    for docs in all_docs:
        lowerCAmelCase__ :  Tuple							      =					[strip_title(UpperCamelCase			) for title in docs["""title"""]]
        provenance_strings.append("""\t""".join(UpperCamelCase			)			)
    return provenance_strings
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    with torch.no_grad():
        lowerCAmelCase__ :  str							      =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            UpperCamelCase							,    return_tensors="""pt"""							,    padding=UpperCamelCase							,    truncation=UpperCamelCase			)

        lowerCAmelCase__ :  Union[str, Any]							      =					inputs_dict.input_ids.to(args.device			)
        lowerCAmelCase__ :  List[Any]							      =					inputs_dict.attention_mask.to(args.device			)
        lowerCAmelCase__ :  Optional[Any]							      =					rag_model.generate(  # rag_model overwrites generate
            UpperCamelCase							,    attention_mask=UpperCamelCase							,    num_beams=args.num_beams							,    min_length=args.min_length							,    max_length=args.max_length							,    early_stopping=UpperCamelCase							,    num_return_sequences=1							,    bad_words_ids=[[0, 0]]							,    )
        lowerCAmelCase__ :  Optional[Any]							      =					rag_model.retriever.generator_tokenizer.batch_decode(UpperCamelCase							,    skip_special_tokens=UpperCamelCase			)

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

        return answers
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					{}
    if args.model_type is None:
        lowerCAmelCase__ :  Any							      =					infer_model_type(args.model_name_or_path			)
        assert args.model_type is not None
    if args.model_type.startswith("""rag"""			):
        lowerCAmelCase__ :  str							      =					RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
        lowerCAmelCase__ :  Any							      =					args.n_docs
        if args.index_name is not None:
            lowerCAmelCase__ :  Any							      =					args.index_name
        if args.index_path is not None:
            lowerCAmelCase__ :  Dict							      =					args.index_path
    else:
        lowerCAmelCase__ :  Tuple							      =					BartForConditionalGeneration

    lowerCAmelCase__ :  List[Any]							      =					(
        [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"""							,    UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					get_scores if args.eval_mode == """e2e""" else get_precision_at_k
    lowerCAmelCase__ :  Tuple							      =					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(UpperCamelCase							,    args.predictions_path							,    args.gold_data_path			)
            continue

        logger.info("""***** Running evaluation for {} *****""".format(UpperCamelCase			)			)
        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"""			):
            lowerCAmelCase__ :  str							      =					RagRetriever.from_pretrained(UpperCamelCase							,    **UpperCamelCase			)
            lowerCAmelCase__ :  List[str]							      =					model_class.from_pretrained(UpperCamelCase							,    retriever=UpperCamelCase							,    **UpperCamelCase			)
            model.retriever.init_retrieval()
        else:
            lowerCAmelCase__ :  Dict							      =					model_class.from_pretrained(UpperCamelCase							,    **UpperCamelCase			)
        model.to(args.device			)

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

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


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










"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

  # Reading the image and converting it to grayscale.
  __A									= imread(__UpperCamelCase		,       0 )

  # Test for get_neighbors_pixel function() return not None
  __A									= 0
  __A									= 0
  __A									= image[x_coordinate][y_coordinate]

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  __A									= np.zeros((image.shape[0], image.shape[1]) )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,       image.shape[0] ):
    for j in range(0		,       image.shape[1] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




