



"""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


__UpperCAmelCase      :      Union[str, Any]   					=					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def      A      (			_A							):




					"""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      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					return max(metric_fn(_A,     _A							) for gt in ground_truths							)
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:Optional[Any]								=						[line.strip() for line in open(_A,     "r"							).readlines()]
					snake_case_						:str								=						[]

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

					snake_case_						:str								=						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_						:Dict								=						100.0 * em / total
					snake_case_						:List[str]								=						100.0 * fa / total

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




					"""simple docstring"""
					snake_case_						:str								=						args.k
					snake_case_						:Tuple								=						[line.strip() for line in open(_A,     "r"							).readlines()]
					snake_case_						:Union[str, Any]								=						[line.strip() for line in open(_A,     "r"							).readlines()]

					snake_case_						:str								=						0
					for hypo, reference in zip(_A,     _A							):
										snake_case_						:int								=						set(hypo.split("\t"							)[:k]							)
										snake_case_						:Optional[Any]								=						set(reference.split("\t"							)							)
										total += 1
										em += len(hypo_provenance & ref_provenance							) / k

					snake_case_						:List[Any]								=						100.0 * em / total
					logger.info(F'''Precision@{k}: {em: .2f}'''							)
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					def strip_title(_A							):
										if title.startswith("\""							):
															snake_case_						:Optional[Any]								=						title[1:]
										if title.endswith("\""							):
															snake_case_						:Dict								=						title[:-1]
										return title

					snake_case_						:str								=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    _A,     return_tensors="pt",     padding=_A,     truncation=_A,     )["input_ids"].to(args.device							)

					snake_case_						:Tuple								=						rag_model.rag.question_encoder(_A							)
					snake_case_						:Optional[Any]								=						question_enc_outputs[0]

					snake_case_						:List[Any]								=						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_						:Optional[int]								=						rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
					snake_case_						:int								=						[]
					for docs in all_docs:
										snake_case_						:List[Any]								=						[strip_title(_A							) for title in docs["title"]]
										provenance_strings.append("\t".join(_A							)							)
					return provenance_strings
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					with torch.no_grad():
										snake_case_						:List[Any]								=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    _A,     return_tensors="pt",     padding=_A,     truncation=_A							)

										snake_case_						:int								=						inputs_dict.input_ids.to(args.device							)
										snake_case_						:Optional[int]								=						inputs_dict.attention_mask.to(args.device							)
										snake_case_						:str								=						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_						:List[str]								=						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      A      (			):




					"""simple docstring"""
					snake_case_						:Optional[int]								=						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_						:Optional[int]								=						parser.parse_args()
					snake_case_						:Any								=						torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
					return args
def      A      (			_A							):




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

					snake_case_						:Dict								=						(
					    [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_						:Optional[int]								=						get_scores if args.eval_mode == "e2e" else get_precision_at_k
					snake_case_						:Any								=						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_						:Tuple								=						RagRetriever.from_pretrained(_A,     **_A							)
															snake_case_						:int								=						model_class.from_pretrained(_A,     retriever=_A,     **_A							)
															model.retriever.init_retrieval()
										else:
															snake_case_						:str								=						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_						:int								=						[]
															for line in tqdm(_A							):
																				questions.append(line.strip()							)
																				if len(_A							) == args.eval_batch_size:
																									snake_case_						:str								=						evaluate_batch_fn(_A,     _A,     _A							)
																									preds_file.write("\n".join(_A							) + "\n"							)
																									preds_file.flush()
																									snake_case_						:Optional[int]								=						[]
															if len(_A							) > 0:
																				snake_case_						:List[Any]								=						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      :      List[str]   					=					get_args()
				main(args)










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase      :      Optional[int]   					=					{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      int   					=					['SegformerFeatureExtractor']
				__UpperCAmelCase      :      Dict   					=					['SegformerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Optional[Any]   					=					[
				    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SegformerDecodeHead',
				    'SegformerForImageClassification',
				    'SegformerForSemanticSegmentation',
				    'SegformerLayer',
				    'SegformerModel',
				    'SegformerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Dict   					=					[
				    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFSegformerDecodeHead',
				    'TFSegformerForImageClassification',
				    'TFSegformerForSemanticSegmentation',
				    'TFSegformerModel',
				    'TFSegformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				__UpperCAmelCase      :      Optional[Any]   					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

