



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

transformers_logging.set_verbosity_info()

def 			a		( _UpperCAmelCase				:   Any		):







		'''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		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   List[Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      [line.strip() for line in open(__UpperCAmelCase					,  '''r'''		).readlines()]
		__UpperCAmelCase       :	Tuple						=      []

		if args.gold_data_mode == "qa":
				__UpperCAmelCase       :	Optional[Any]						=      pd.read_csv(__UpperCAmelCase					,  sep='''\t'''					,  header=__UpperCAmelCase		)
				for answer_list in data[1]:
						__UpperCAmelCase       :	Optional[Any]						=      ast.literal_eval(__UpperCAmelCase		)
						answers.append(__UpperCAmelCase		)
		else:
				__UpperCAmelCase       :	Optional[int]						=      [line.strip() for line in open(__UpperCAmelCase					,  '''r'''		).readlines()]
				__UpperCAmelCase       :	Union[str, Any]						=      [[reference] for reference in references]

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

		__UpperCAmelCase       :	Union[str, Any]						=      1_00.0 * em / total
		__UpperCAmelCase       :	Any						=      1_00.0 * fa / total

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

def 			a		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      args.k
		__UpperCAmelCase       :	Tuple						=      [line.strip() for line in open(__UpperCAmelCase					,  '''r'''		).readlines()]
		__UpperCAmelCase       :	int						=      [line.strip() for line in open(__UpperCAmelCase					,  '''r'''		).readlines()]

		__UpperCAmelCase       :	Any						=      0
		for hypo, reference in zip(__UpperCAmelCase					,  __UpperCAmelCase		):
				__UpperCAmelCase       :	Tuple						=      set(hypo.split('''\t'''		)[:k]		)
				__UpperCAmelCase       :	Tuple						=      set(reference.split('''\t'''		)		)
				total += 1
				em += len(hypo_provenance & ref_provenance		) / k

		__UpperCAmelCase       :	List[Any]						=      1_00.0 * em / total
		logger.info(f'Precision@{k}: {em: .2f}'		)

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   List[Any]		):







		'''simple docstring'''





		def strip_title(_UpperCAmelCase				:   Tuple		):
				if title.startswith('''"'''		):
						__UpperCAmelCase       :	str						=      title[1:]
				if title.endswith('''"'''		):
						__UpperCAmelCase       :	Dict						=      title[:-1]
				return title

		__UpperCAmelCase       :	int						=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    __UpperCAmelCase					,  return_tensors='''pt'''					,  padding=__UpperCAmelCase					,  truncation=__UpperCAmelCase					,  )['''input_ids'''].to(args.device		)

		__UpperCAmelCase       :	List[str]						=      rag_model.rag.question_encoder(__UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[int]						=      question_enc_outputs[0]

		__UpperCAmelCase       :	Optional[Any]						=      rag_model.retriever(
		    __UpperCAmelCase					,  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       :	int						=      rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
		__UpperCAmelCase       :	Optional[int]						=      []
		for docs in all_docs:
				__UpperCAmelCase       :	Dict						=      [strip_title(__UpperCAmelCase		) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(__UpperCAmelCase		)		)
		return provenance_strings

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   Dict		):







		'''simple docstring'''





		with torch.no_grad():
				__UpperCAmelCase       :	Tuple						=      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    __UpperCAmelCase					,  return_tensors='''pt'''					,  padding=__UpperCAmelCase					,  truncation=__UpperCAmelCase		)

				__UpperCAmelCase       :	List[str]						=      inputs_dict.input_ids.to(args.device		)
				__UpperCAmelCase       :	List[str]						=      inputs_dict.attention_mask.to(args.device		)
				__UpperCAmelCase       :	str						=      rag_model.generate(  # rag_model overwrites generate
				    __UpperCAmelCase					,  attention_mask=__UpperCAmelCase					,  num_beams=args.num_beams					,  min_length=args.min_length					,  max_length=args.max_length					,  early_stopping=__UpperCAmelCase					,  num_return_sequences=1					,  bad_words_ids=[[0, 0]]					,  )
				__UpperCAmelCase       :	Optional[Any]						=      rag_model.retriever.generator_tokenizer.batch_decode(__UpperCAmelCase					,  skip_special_tokens=__UpperCAmelCase		)

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

				return answers

def 			a		( ):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   Tuple		):







		'''simple docstring'''





		__UpperCAmelCase       :	Tuple						=      {}
		if args.model_type is None:
				__UpperCAmelCase       :	List[str]						=      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       :	Union[str, Any]						=      args.n_docs
				if args.index_name is not None:
						__UpperCAmelCase       :	Dict						=      args.index_name
				if args.index_path is not None:
						__UpperCAmelCase       :	Any						=      args.index_path
		else:
				__UpperCAmelCase       :	Dict						=      BartForConditionalGeneration

		__UpperCAmelCase       :	Any						=      (
		    [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'''					,  __UpperCAmelCase		)

		__UpperCAmelCase       :	Tuple						=      get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
		__UpperCAmelCase       :	Optional[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(__UpperCAmelCase					,  args.predictions_path					,  args.gold_data_path		)
						continue

				logger.info('''***** Running evaluation for {} *****'''.format(__UpperCAmelCase		)		)
				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       :	str						=      RagRetriever.from_pretrained(__UpperCAmelCase					,  **__UpperCAmelCase		)
						__UpperCAmelCase       :	List[str]						=      model_class.from_pretrained(__UpperCAmelCase					,  retriever=__UpperCAmelCase					,  **__UpperCAmelCase		)
						model.retriever.init_retrieval()
				else:
						__UpperCAmelCase       :	Optional[int]						=      model_class.from_pretrained(__UpperCAmelCase					,  **__UpperCAmelCase		)
				model.to(args.device		)

				with open(args.evaluation_set					,  '''r'''		) as eval_file, open(args.predictions_path					,  '''w'''		) as preds_file:
						__UpperCAmelCase       :	Any						=      []
						for line in tqdm(__UpperCAmelCase		):
								questions.append(line.strip()		)
								if len(__UpperCAmelCase		) == args.eval_batch_size:
										__UpperCAmelCase       :	List[str]						=      evaluate_batch_fn(__UpperCAmelCase					,  __UpperCAmelCase					,  __UpperCAmelCase		)
										preds_file.write('''\n'''.join(__UpperCAmelCase		) + '''\n'''		)
										preds_file.flush()
										__UpperCAmelCase       :	Any						=      []
						if len(__UpperCAmelCase		) > 0:
								__UpperCAmelCase       :	Optional[int]						=      evaluate_batch_fn(__UpperCAmelCase					,  __UpperCAmelCase					,  __UpperCAmelCase		)
								preds_file.write('''\n'''.join(__UpperCAmelCase		)		)
								preds_file.flush()

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


if __name__ == "__main__":
				__A       =get_args()
				main(args)










from __future__ import annotations

import sys







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :int)						->   List[Any]:
												UpperCAmelCase_					=	letter
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	{}




						def __repr__(       self      :str)						->   str:
												return f"{self.letter}:{self.freq}"







class 				a_    :

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Letter | TreeNode  ,		_lowercase      :Letter | TreeNode)						->   Dict:
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	left
												UpperCAmelCase_					=	right







def 							A     (						__UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		UpperCAmelCase_					=	chars[c] + 1 if c in chars else 1
						return sorted((Letter(__UpperCAmelCase				,   __UpperCAmelCase      ) for c, f in chars.items())				,   key=lambda __UpperCAmelCase      : x.freq      )







def 							A     (						__UpperCAmelCase      )			->			Letter | TreeNode:



						'''simple docstring'''






						UpperCAmelCase_					=	letters  # type: ignore
						while len(__UpperCAmelCase      ) > 1:
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	left.freq + right.freq
												UpperCAmelCase_					=	TreeNode(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												response.append(__UpperCAmelCase      )
												response.sort(key=lambda __UpperCAmelCase      : x.freq      )
						return response[0]







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
												UpperCAmelCase_					=	bitstring
												return [root]
						UpperCAmelCase_					=	root  # type: ignore
						UpperCAmelCase_					=	[]
						letters += traverse_tree(treenode.left				,   bitstring + '''0'''      )
						letters += traverse_tree(treenode.right				,   bitstring + '''1'''      )
						return letters







def 							A     (						__UpperCAmelCase      )			->			None:



						'''simple docstring'''






						UpperCAmelCase_					=	parse_file(__UpperCAmelCase      )
						UpperCAmelCase_					=	build_tree(__UpperCAmelCase      )
						UpperCAmelCase_					=	{
						    k: v for letter in traverse_tree(__UpperCAmelCase				,   ''''''      ) for k, v in letter.bitstring.items()
						}
						print(f"Huffman Coding  of {file_path}: "      )
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		print(letters[c]				,   end=''' '''      )
						print()


if __name__ == "__main__":
							# pass the file path to the huffman function
							huffman(sys.argv[1])





