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


SCREAMING_SNAKE_CASE							:List[Any]											=       logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def 		UpperCAmelCase    (			a_		)	->			Union[str, Any]:







				"""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 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Any:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			str:







				"""simple docstring"""



				__A         =				[line.strip() for line in open(UpperCamelCase__	,     "r"		).readlines()]
				__A         =				[]

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

				__A         =				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__		)

				__A         =				100.0 * em / total
				__A         =				100.0 * fa / total

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


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Optional[int]:







				"""simple docstring"""



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

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

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


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Optional[Any]:







				"""simple docstring"""



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

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

				__A         =				rag_model.rag.question_encoder(UpperCamelCase__		)
				__A         =				question_enc_outputs[0]

				__A         =				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"	,     )
				__A         =				rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
				__A         =				[]
				for docs in all_docs:
								__A         =				[strip_title(UpperCamelCase__		) for title in docs['''title''']]
								provenance_strings.append("\t".join(UpperCamelCase__		)		)
				return provenance_strings


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Optional[Any]:







				"""simple docstring"""



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

								__A         =				inputs_dict.input_ids.to(args.device		)
								__A         =				inputs_dict.attention_mask.to(args.device		)
								__A         =				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]]	,     )
								__A         =				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 		UpperCAmelCase    (			)	->			Tuple:







				"""simple docstring"""



				__A         =				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=5_0	,     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."	,     )
				__A         =				parser.parse_args()
				__A         =				torch.device("cuda" if torch.cuda.is_available() else "cpu"		)
				return args


def 		UpperCAmelCase    (			a_		)	->			Optional[int]:







				"""simple docstring"""



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

				__A         =				(
				    [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__		)

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


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
