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

transformers_logging.set_verbosity_info()






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						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     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Tuple			):
						return max(metric_fn(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) for gt in ground_truths			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Tuple			):
						UpperCamelCase_		:							Tuple  =		[line.strip() for line in open(_SCREAMING_SNAKE_CASE		, """r"""			).readlines()]
						UpperCamelCase_		:							Tuple  =		[]

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

						UpperCamelCase_		:							int  =		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			)

						UpperCamelCase_		:							int  =		100.0 * em / total
						UpperCamelCase_		:							int  =		100.0 * fa / total

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				int			):
						UpperCamelCase_		:							List[Any]  =		args.k
						UpperCamelCase_		:							str  =		[line.strip() for line in open(_SCREAMING_SNAKE_CASE		, """r"""			).readlines()]
						UpperCamelCase_		:							List[str]  =		[line.strip() for line in open(_SCREAMING_SNAKE_CASE		, """r"""			).readlines()]

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

						UpperCamelCase_		:							List[Any]  =		100.0 * em / total
						logger.info(f'''Precision@{k}: {em: .2f}'''			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				List[Any]			):
						def strip_title(_SCREAMING_SNAKE_CASE     :				Tuple			):
												if title.startswith("""\""""			):
																		UpperCamelCase_		:							str  =		title[1:]
												if title.endswith("""\""""			):
																		UpperCamelCase_		:							str  =		title[:-1]
												return title

						UpperCamelCase_		:							Optional[int]  =		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			)

						UpperCamelCase_		:							int  =		rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[Any]  =		question_enc_outputs[0]

						UpperCamelCase_		:							Any  =		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"""		, )
						UpperCamelCase_		:							Optional[Any]  =		rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
						UpperCamelCase_		:							List[str]  =		[]
						for docs in all_docs:
												UpperCamelCase_		:							str  =		[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     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]			):
						with torch.no_grad():
												UpperCamelCase_		:							int  =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    _SCREAMING_SNAKE_CASE		, return_tensors="""pt"""		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE			)

												UpperCamelCase_		:							Tuple  =		inputs_dict.input_ids.to(args.device			)
												UpperCamelCase_		:							Dict  =		inputs_dict.attention_mask.to(args.device			)
												UpperCamelCase_		:							int  =		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]]		, )
												UpperCamelCase_		:							Optional[Any]  =		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_			(   ):
						UpperCamelCase_		:							Optional[int]  =		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."""		, )
						UpperCamelCase_		:							Optional[Any]  =		parser.parse_args()
						UpperCamelCase_		:							Tuple  =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)
						return args






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

						UpperCamelCase_		:							int  =		(
						    [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			)

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







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							Optional[int]  =		use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''						)

												super().__init__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1			,     int((1.0 - self.overlap) * self.chunk_length						)						)





						@property
						def        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






