

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

transformers_logging.set_verbosity_info()




def       UpperCAmelCase__    (      _A				:			Optional[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       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			List[Any]     ,				_A				:			int							):





		'''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			Any     ,				_A				:			List[str]							):





		'''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       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Optional[Any]     ,				_A				:			Union[str, 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       UpperCAmelCase__    (      _A				:			Any     ,				_A				:			Union[str, Any]     ,				_A				:			List[Any]							):





		'''simple docstring'''







		def strip_title(_A				:			str							):
				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       UpperCAmelCase__    (      _A				:			Union[str, Any]     ,				_A				:			Tuple     ,				_A				:			Dict							):





		'''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       UpperCAmelCase__    (      ):





		'''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       UpperCAmelCase__    (      _A				:			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__":
							lowerCamelCase									= get_args()
							main(args)



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






     return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_ = 13,   lowercase_ = 6	)							->   None:






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






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

     # while there are still characters in text to compress
     while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

       # update the text
       a__  =text[token.length + 1 :]

       # append the token to output
       output.append(lowercase_	)

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

     for token in tokens:
       for _ in range(token.length	):
         output += output[-token.offset]
       output += token.indicator

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






     if not text:
       raise ValueError('''We need some text to work with.'''	)

     # Initialise result parameters to default values
     a__,				a__  =0, 0

     if not search_buffer:
       return Token(lowercase_,   lowercase_,   text[length]	)

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
           a__,				a__  =found_offset, found_length

     return Token(lowercase_,   lowercase_,   text[length]	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






     if not text or text[text_index] != window[window_index]:
       return 0
     return 1 + self._match_length_from_index(
         lowercase_,   window + text[text_index],   text_index + 1,   window_index + 1	)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       lowerCamelCase									= '''cabracadabrarrarrad'''
       lowerCamelCase									= lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       lowerCamelCase									= lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
