




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


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

transformers_logging.set_verbosity_info()




def 					lowerCamelCase__       (						A__       :					Optional[int]	):





       '''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 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Any  ,			A__       :					Optional[int]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Dict  ,			A__       :					Optional[int]  ,			A__       :					List[str]	):





       '''simple docstring'''



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

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

       __lowerCamelCase   =						__lowerCamelCase   =						__lowerCamelCase   =						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__	)

       __lowerCamelCase   =						100.0 * em / total
       __lowerCamelCase   =						100.0 * fa / total

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




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Union[str, Any]  ,			A__       :					Optional[int]	):





       '''simple docstring'''



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

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

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




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Union[str, Any]  ,			A__       :					str	):





       '''simple docstring'''



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

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

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

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




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Dict  ,			A__       :					List[Any]	):





       '''simple docstring'''



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

              __lowerCamelCase   =						inputs_dict.input_ids.to(args.device	)
              __lowerCamelCase   =						inputs_dict.attention_mask.to(args.device	)
              __lowerCamelCase   =						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]]  ,			)
              __lowerCamelCase   =						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 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						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."""  ,			)
       __lowerCamelCase   =						parser.parse_args()
       __lowerCamelCase   =						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)
       return args




def 					lowerCamelCase__       (						A__       :					List[Any]	):





       '''simple docstring'''



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

       __lowerCamelCase   =						(
           [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__	)

       __lowerCamelCase   =						get_scores if args.eval_mode == """e2e""" else get_precision_at_k
       __lowerCamelCase   =						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"""	):
                     __lowerCamelCase   =						RagRetriever.from_pretrained(A__  ,			**A__	)
                     __lowerCamelCase   =						model_class.from_pretrained(A__  ,			retriever=A__  ,			**A__	)
                     model.retriever.init_retrieval()
              else:
                     __lowerCamelCase   =						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:
                     __lowerCamelCase   =						[]
                     for line in tqdm(A__	):
                            questions.append(line.strip()	)
                            if len(A__	) == args.eval_batch_size:
                                   __lowerCamelCase   =						evaluate_batch_fn(A__  ,			A__  ,			A__	)
                                   preds_file.write("""\n""".join(A__	) + """\n"""	)
                                   preds_file.flush()
                                   __lowerCamelCase   =						[]
                     if len(A__	) > 0:
                            __lowerCamelCase   =						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__":
       UpperCAmelCase_									=	get_args()
       main(args)







from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]	) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 1 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     __lowerCamelCase   =						cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1	)
                     else:
                            next_generation_row.append(0	)

              next_generation.append(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


if __name__ == "__main__":
       UpperCAmelCase_									=	generate_images(GLIDER, 16)
       images[0].save('out.gif', save_all=True, append_images=images[1:])

