

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


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

transformers_logging.set_verbosity_info()



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



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

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

	UpperCAmelCase_    			=     UpperCAmelCase_    			=     UpperCAmelCase_    			=     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      )

	UpperCAmelCase_    			=     100.0 * em / total
	UpperCAmelCase_    			=     100.0 * fa / total

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



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

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

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



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

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

	UpperCAmelCase_    			=     rag_model.rag.question_encoder(A      )
	UpperCAmelCase_    			=     question_enc_outputs[0]

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



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

		UpperCAmelCase_    			=     inputs_dict.input_ids.to(args.device      )
		UpperCAmelCase_    			=     inputs_dict.attention_mask.to(args.device      )
		UpperCAmelCase_    			=     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]]    ,       )
		UpperCAmelCase_    			=     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 (  ):
	UpperCAmelCase_    			=     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."    ,       )
	UpperCAmelCase_    			=     parser.parse_args()
	UpperCAmelCase_    			=     torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
	return args



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

	UpperCAmelCase_    			=     (
	    [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      )

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


import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
				import torch_xla.core.xla_model as xm  # noqa: F401

				_a:      List[Any]								=     True
except ImportError:
				_a:      int								=     False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_a:      str								=     torch.distributed.is_available()



def  __lowerCAmelCase (  A      ):
	# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
	UpperCAmelCase_    			=     importlib.util.find_spec(A      ) is not None
	if package_exists:
		try:
			UpperCAmelCase_    			=     importlib.metadata.metadata(A      )
			return True
		except importlib.metadata.PackageNotFoundError:
			return False



def  __lowerCAmelCase (  ):
	return _torch_distributed_available



def  __lowerCAmelCase (  ):
	try:
		pass
	except ImportError:
		print(
		    "Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"
		    " detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"
		    " Bindings for PyTorch*."      )
	return (
	    importlib.util.find_spec("torch_ccl"      ) is not None
	    or importlib.util.find_spec("oneccl_bindings_for_pytorch"      ) is not None
	)



def  __lowerCAmelCase (  ):
	return importlib.metadata.version("oneccl_bind_pt"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("transformer_engine"      )



@lru_cache
def  __lowerCAmelCase (  A=True      ):
	# Due to bugs on the amp series GPUs, we disable torch-xla on them
	if torch.cuda.is_available():
		return False
	if _tpu_available and check_device:
		try:
			# Will raise a RuntimeError if no XLA configuration is found
			UpperCAmelCase_    			=     xm.xla_device()
			return True
		except RuntimeError:
			return False
	return _tpu_available



def  __lowerCAmelCase (  ):
	return _is_package_available("deepspeed"      )



def  __lowerCAmelCase (  A=False      ):
	if is_tpu_available():
		return not ignore_tpu
	if torch.cuda.is_available():
		return torch.cuda.is_bfaa_supported()
	if is_npu_available():
		return False
	return True



def  __lowerCAmelCase (  ):
	UpperCAmelCase_    			=     _is_package_available("bitsandbytes"      )
	if package_exists:
		UpperCAmelCase_    			=     version.parse(importlib.metadata.version("bitsandbytes"      )      )
		return compare_versions(A    ,       ">="    ,       "0.39.0"      )
	return False



def  __lowerCAmelCase (  ):
	UpperCAmelCase_    			=     _is_package_available("bitsandbytes"      )
	if package_exists:
		UpperCAmelCase_    			=     version.parse(importlib.metadata.version("bitsandbytes"      )      )
		return compare_versions(A    ,       ">="    ,       "0.37.2"      )
	return False



def  __lowerCAmelCase (  ):
	return _is_package_available("bitsandbytes"      )



def  __lowerCAmelCase (  ):
	if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM"    ,       "False"      )      ) == 1:
		UpperCAmelCase_    			=     importlib.util.find_spec("megatron"      ) is not None
		if package_exists:
			try:
				UpperCAmelCase_    			=     parse(importlib.metadata.version("megatron-lm"      )      )
				return compare_versions(A    ,       ">="    ,       "2.2.0"      )
			except Exception as e:
				warnings.warn(F"Parse Megatron version failed. Exception:{e}"      )
				return False



def  __lowerCAmelCase (  ):
	return _is_package_available("safetensors"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("transformers"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("datasets"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("aim"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("tensorboard"      ) or _is_package_available("tensorboardX"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("wandb"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("comet_ml"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("boto3"      )



def  __lowerCAmelCase (  ):
	if _is_package_available("rich"      ):
		if "ACCELERATE_DISABLE_RICH" in os.environ:
			warnings.warn(
			    "`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."      )
			return not parse_flag_from_env("ACCELERATE_DISABLE_RICH"    ,       A      )
		return parse_flag_from_env("ACCELERATE_ENABLE_RICH"    ,       A      )
	return False



def  __lowerCAmelCase (  ):
	return _is_package_available("sagemaker"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("tqdm"      )



def  __lowerCAmelCase (  ):
	return _is_package_available("mlflow"      )



def  __lowerCAmelCase (  ):
	return is_torch_version(">="    ,       "1.12"      ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()



def  __lowerCAmelCase (  ):
	def get_major_and_minor_from_version(A      ):
		return str(version.parse(A      ).major      ) + "." + str(version.parse(A      ).minor      )

	UpperCAmelCase_    			=     importlib.metadata.version("torch"      )
	if importlib.util.find_spec("intel_extension_for_pytorch"      ) is None:
		return False
	UpperCAmelCase_    			=     "N/A"
	try:
		UpperCAmelCase_    			=     importlib.metadata.version("intel_extension_for_pytorch"      )
	except importlib.metadata.PackageNotFoundError:
		return False
	UpperCAmelCase_    			=     get_major_and_minor_from_version(_torch_version      )
	UpperCAmelCase_    			=     get_major_and_minor_from_version(_ipex_version      )
	if torch_major_and_minor != ipex_major_and_minor:
		warnings.warn(
		    F"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
		    F" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."      )
		return False
	return True



@lru_cache
def  __lowerCAmelCase (  A=False      ):
	if importlib.util.find_spec("torch"      ) is None or importlib.util.find_spec("torch_npu"      ) is None:
		return False

	import torch
	import torch_npu  # noqa: F401

	if check_device:
		try:
			# Will raise a RuntimeError if no NPU is found
			UpperCAmelCase_    			=     torch.npu.device_count()
			return torch.npu.is_available()
		except RuntimeError:
			return False
	return hasattr(A    ,       "npu"      ) and torch.npu.is_available()




@lru_cache
def  __lowerCAmelCase (  A=False      ):
	if not parse_flag_from_env("ACCELERATE_USE_XPU"    ,       default=A      ):
		return False
	if is_ipex_available():
		import torch

		if is_torch_version("<="    ,       "1.12"      ):
			return False
	else:
		return False

	import intel_extension_for_pytorch  # noqa: F401

	if check_device:
		try:
			# Will raise a RuntimeError if no XPU  is found
			UpperCAmelCase_    			=     torch.xpu.device_count()
			return torch.xpu.is_available()
		except RuntimeError:
			return False
	return hasattr(A    ,       "xpu"      ) and torch.xpu.is_available()