





'''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


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

transformers_logging.set_verbosity_info()







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    str:
			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  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]     )   ->    List[str]:
			return max(metric_fn(__lowerCamelCase					,  __lowerCamelCase     ) for gt in ground_truths     )







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     Union[str, Any]     )   ->    Optional[int]:
			a__     = [line.strip() for line in open(__lowerCamelCase					,  "r"     ).readlines()]
			a__     = []

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

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

			a__     = 100.0 * em / total
			a__     = 100.0 * fa / total

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







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     List[Any]     )   ->    Tuple:
			a__     = args.k
			a__     = [line.strip() for line in open(__lowerCamelCase					,  "r"     ).readlines()]
			a__     = [line.strip() for line in open(__lowerCamelCase					,  "r"     ).readlines()]

			a__     = a__     = 0
			for hypo, reference in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						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    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]     )   ->    str:
			def strip_title(__lowerCamelCase  :     Optional[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(
			    __lowerCamelCase					,  return_tensors="pt"					,  padding=__lowerCamelCase					,  truncation=__lowerCamelCase					,  )["input_ids"].to(args.device     )

			a__     = rag_model.rag.question_encoder(__lowerCamelCase     )
			a__     = question_enc_outputs[0]

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







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     str     )   ->    List[str]:
			with torch.no_grad():
						a__     = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    __lowerCamelCase					,  return_tensors="pt"					,  padding=__lowerCamelCase					,  truncation=__lowerCamelCase     )

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

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

						return answers







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

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

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

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


if __name__ == "__main__":
		lowerCAmelCase_   :   Any       =					get_args()
		main(args)










'''simple docstring'''


from manim import *






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = Rectangle(height=0.5 , width=0.5		)
				a__     = Rectangle(height=0.25 , width=0.25		)
				a__     = Rectangle(height=0.46 , width=0.46		).set_stroke(width=0		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("CPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				cpu.move_to([-2.5, -0.5, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(4		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("GPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				gpu.move_to([-1, -1, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)

				a__     = Text("Model" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				model.move_to([3, -1.0, 0]		)
				self.add(lowerCamelCase		)

				a__     = []
				a__     = []
				a__     = []

				for i, rect in enumerate(lowerCamelCase		):
							rect.set_stroke(lowerCamelCase		)

							a__     = Rectangle(height=0.46 / 4 , width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowerCamelCase , opacity=0.7		)

							if i == 0:
										cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		) , buff=0.02 , direction=lowerCamelCase		)
										cpu_target.set_x(cpu_target.get_x() + 0.1		)
							elif i == 3:
										cpu_target.next_to(model_cpu_arr[0] , direction=lowerCamelCase , buff=0.0		)
							else:
										cpu_target.next_to(model_cpu_arr[i - 1] , direction=lowerCamelCase , buff=0.0		)
							self.add(lowerCamelCase		)
							model_cpu_arr.append(lowerCamelCase		)

				self.add(*lowerCamelCase , *lowerCamelCase , *lowerCamelCase		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)

				a__     = Text("Loaded Checkpoint" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				checkpoint.move_to([3, 0.5, 0]		)

				self.add(lowerCamelCase		)

				a__     = []
				a__     = []

				for i, rect in enumerate(lowerCamelCase		):
							a__     = fill.copy().set_fill(lowerCamelCase , opacity=0.7		)
							target.move_to(lowerCamelCase		)
							ckpt_arr.append(lowerCamelCase		)

							a__     = target.copy()
							if i < 5:
										cpu_target.move_to(cpu_left_col_base[i + 1]		)
							else:
										cpu_target.move_to(cpu_right_col_base[i - 5]		)
							ckpt_cpu_arr.append(lowerCamelCase		)
				self.add(*lowerCamelCase , *lowerCamelCase		)

				a__     = Square(side_length=2.2		)
				key.move_to([-5, 2, 0]		)

				a__     = MarkupText(
				    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=1_8 , )

				key_text.move_to([-5, 2.4, 0]		)

				self.add(lowerCamelCase , lowerCamelCase		)

				a__     = MarkupText(
				    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )

				blue_text.next_to(lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left()		)
				self.add(lowerCamelCase		)

				a__     = MarkupText(
				    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=2_4 , )
				step_a.move_to([2, 2, 0]		)

				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("Disk" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				disk.move_to([-4.0, -1.25, 0]		)
				self.play(Write(lowerCamelCase , run_time=3		) , Write(lowerCamelCase , run_time=1		) , Create(lowerCamelCase , run_time=1		)		)

				a__     = []
				for i, rect in enumerate(lowerCamelCase		):
							a__     = rect.copy()
							target.generate_target()
							target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
							animations.append(MoveToTarget(lowerCamelCase , run_time=1.5		)		)
				self.play(*lowerCamelCase		)

				self.play(FadeOut(lowerCamelCase		)		)

				a__     = MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=2_4		)
				step_a.move_to([2, 2, 0]		)

				self.play(Write(lowerCamelCase , run_time=3		)		)

				self.play(
				    FadeOut(lowerCamelCase , lowerCamelCase , *lowerCamelCase , *lowerCamelCase		) , )

				self.wait()



