



# Copyright 2023 The HuggingFace Inc. 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.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
				import torch


_lowercase: Any      			=		logging.get_logger(__name__)



@dataclass
class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=6.0		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=None		,       lowerCamelCase_="fp4"		,       lowerCamelCase_=False		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   load_in_abit
						a					     =   load_in_abit
						a					     =   llm_inta_threshold
						a					     =   llm_inta_skip_modules
						a					     =   llm_inta_enable_fpaa_cpu_offload
						a					     =   llm_inta_has_fpaa_weight
						a					     =   bnb_abit_quant_type
						a					     =   bnb_abit_use_double_quant

						if bnb_abit_compute_dtype is None:
											a					     =   torch.floataa
						elif isinstance(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   getattr(lowerCamelCase_		,       lowerCamelCase_		)
						elif isinstance(lowerCamelCase_		,       torch.dtype		):
											a					     =   bnb_abit_compute_dtype
						else:
											raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype"		)

						self.post_init()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						if not isinstance(self.llm_inta_threshold		,       lowerCamelCase_		):
											raise ValueError("llm_int8_threshold must be a float"		)

						if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,       lowerCamelCase_		):
											raise ValueError("llm_int8_skip_modules must be a list of strings"		)
						if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,       lowerCamelCase_		):
											raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"		)

						if not isinstance(self.llm_inta_has_fpaa_weight		,       lowerCamelCase_		):
											raise ValueError("llm_int8_has_fp16_weight must be a boolean"		)

						if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,       torch.dtype		):
											raise ValueError("bnb_4bit_compute_dtype must be torch.dtype"		)

						if not isinstance(self.bnb_abit_quant_type		,       lowerCamelCase_		):
											raise ValueError("bnb_4bit_quant_type must be a string"		)

						if not isinstance(self.bnb_abit_use_double_quant		,       lowerCamelCase_		):
											raise ValueError("bnb_4bit_use_double_quant must be a boolean"		)

						if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes"		)		) >= version.parse(
						    "0.39.0"		):
											raise ValueError(
											    "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.load_in_abit or self.load_in_abit




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						if self.load_in_abit:
											return "llm_int8"
						elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
											return "fp4"
						elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
											return "nf4"
						else:
											return None




	@classmethod
	def      UpperCamelCase_       (cls		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   cls(**lowerCamelCase_		)

						a					     =   []
						for key, value in kwargs.items():
											if hasattr(lowerCamelCase_		,       lowerCamelCase_		):
																setattr(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
																to_remove.append(lowerCamelCase_		)
						for key in to_remove:
											kwargs.pop(lowerCamelCase_		,       lowerCamelCase_		)

						if return_unused_kwargs:
											return config, kwargs
						else:
											return config




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						with open(lowerCamelCase_		,       "w"		,       encoding="utf-8"		) as writer:
											a					     =   self.to_dict()
											a					     =   json.dumps(lowerCamelCase_		,       indent=2		,       sort_keys=lowerCamelCase_		) + "\n"

											writer.write(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   copy.deepcopy(self.__dict__		)
						a					     =   str(output["bnb_4bit_compute_dtype"]		).split("."		)[1]

						return output




	def __repr__(self		):
						"""simple docstring"""

						return F'''{self.__class__.__name__} {self.to_json_string()}'''




	def      UpperCamelCase_       (self		,       lowerCamelCase_ = True		):
						"""simple docstring"""

						if use_diff is True:
											a					     =   self.to_diff_dict()
						else:
											a					     =   self.to_dict()
						return json.dumps(lowerCamelCase_		,       indent=2		,       sort_keys=lowerCamelCase_		) + "\n"




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.to_dict()

						# get the default config dict
						a					     =   BitsAndBytesConfig().to_dict()

						a					     =   {}

						# only serialize values that differ from the default config
						for key, value in config_dict.items():
											if value != default_config_dict[key]:
																a					     =   value

						return serializable_config_dict





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: Tuple      			=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def     a(      A       :    Optional[Any]       )		->       str:
					"""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       :    Dict ,     A       :    List[Any] ,     A       :    str       )		->       List[str]:
					"""simple docstring"""





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






def     a(      A       :    str ,     A       :    Optional[Any] ,     A       :    Optional[Any]       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]
					a					     =   []

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

					a					     =   a					     =   a					     =   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       )

					a					     =   100.0 * em / total
					a					     =   100.0 * fa / total

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






def     a(      A       :    Dict ,     A       :    str ,     A       :    List[str]       )		->       List[Any]:
					"""simple docstring"""





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

					a					     =   a					     =   0
					for hypo, reference in zip(A ,     A       ):
										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     a(      A       :    Dict ,     A       :    Any ,     A       :    List[Any]       )		->       Any:
					"""simple docstring"""





					def strip_title(A       :    Any       ):
										if title.startswith("\""       ):
															a					     =   title[1:]
										if title.endswith("\""       ):
															a					     =   title[:-1]
										return title

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

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

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






def     a(      A       :    Union[str, Any] ,     A       :    Optional[int] ,     A       :    Tuple       )		->       Tuple:
					"""simple docstring"""





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

										a					     =   inputs_dict.input_ids.to(args.device       )
										a					     =   inputs_dict.attention_mask.to(args.device       )
										a					     =   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]] ,     )
										a					     =   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(      )		->       Any:
					"""simple docstring"""





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






def     a(      A       :    Any       )		->       Optional[Any]:
					"""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" ,     A       )

					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(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"       ):
															a					     =   RagRetriever.from_pretrained(A ,     **A       )
															a					     =   model_class.from_pretrained(A ,     retriever=A ,     **A       )
															model.retriever.init_retrieval()
										else:
															a					     =   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:
															a					     =   []
															for line in tqdm(A       ):
																				questions.append(line.strip()       )
																				if len(A       ) == args.eval_batch_size:
																									a					     =   evaluate_batch_fn(A ,     A ,     A       )
																									preds_file.write("\n".join(A       ) + "\n"       )
																									preds_file.flush()
																									a					     =   []
															if len(A       ) > 0:
																				a					     =   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__":
				_lowercase: Optional[int]      			=		get_args()
				main(args)
