



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


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

transformers_logging.set_verbosity_info()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			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 (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			return max(metric_fn(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) for gt in ground_truths  )



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

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

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

			lowercase__					  =				100.0 * em / total
			lowercase__					  =				100.0 * fa / total

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



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

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

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



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

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

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

			lowercase__					  =				rag_model.retriever(
			    SCREAMING_SNAKE_CASE_						,  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(SCREAMING_SNAKE_CASE_  ) for title in docs["title"]]
						provenance_strings.append("\t".join(SCREAMING_SNAKE_CASE_  )  )
			return provenance_strings



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

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

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

						return answers



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

			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(SCREAMING_SNAKE_CASE_						,  args.predictions_path						,  args.gold_data_path  )
									continue

						logger.info("***** Running evaluation for {} *****".format(SCREAMING_SNAKE_CASE_  )  )
						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(SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				model_class.from_pretrained(SCREAMING_SNAKE_CASE_						,  retriever=SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_  )
									model.retriever.init_retrieval()
						else:
									lowercase__					  =				model_class.from_pretrained(SCREAMING_SNAKE_CASE_						,  **SCREAMING_SNAKE_CASE_  )
						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(SCREAMING_SNAKE_CASE_  ):
												questions.append(line.strip()  )
												if len(SCREAMING_SNAKE_CASE_  ) == args.eval_batch_size:
															lowercase__					  =				evaluate_batch_fn(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
															preds_file.write("\n".join(SCREAMING_SNAKE_CASE_  ) + "\n"  )
															preds_file.flush()
															lowercase__					  =				[]
									if len(SCREAMING_SNAKE_CASE_  ) > 0:
												lowercase__					  =				evaluate_batch_fn(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
												preds_file.write("\n".join(SCREAMING_SNAKE_CASE_  )  )
												preds_file.flush()

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


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









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XmodForCausalLM""",
							    """XmodForMaskedLM""",
							    """XmodForMultipleChoice""",
							    """XmodForQuestionAnswering""",
							    """XmodForSequenceClassification""",
							    """XmodForTokenClassification""",
							    """XmodModel""",
							    """XmodPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xmod import (
														    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XmodForCausalLM,
														    XmodForMaskedLM,
														    XmodForMultipleChoice,
														    XmodForQuestionAnswering,
														    XmodForSequenceClassification,
														    XmodForTokenClassification,
														    XmodModel,
														    XmodPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




