




"""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


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

transformers_logging.set_verbosity_info()

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Any:
       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				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Optional[int]:
       return max(metric_fn(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ) for gt in ground_truths   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[Any]      			= [line.strip() for line in open(_SCREAMING_SNAKE_CASE			,"r"   ).readlines()]
       lowerCamelCase       :				str      			= []

       if args.gold_data_mode == "qa":
              lowerCamelCase       :				List[str]      			= pd.read_csv(_SCREAMING_SNAKE_CASE			,sep="\t"			,header=_SCREAMING_SNAKE_CASE   )
              for answer_list in data[1]:
                     lowerCamelCase       :				int      			= ast.literal_eval(_SCREAMING_SNAKE_CASE   )
                     answers.append(_SCREAMING_SNAKE_CASE   )
       else:
              lowerCamelCase       :				Union[str, Any]      			= [line.strip() for line in open(_SCREAMING_SNAKE_CASE			,"r"   ).readlines()]
              lowerCamelCase       :				Tuple      			= [[reference] for reference in references]

       lowerCamelCase       :				List[str]      			= 0
       for prediction, ground_truths in zip(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ):
              total += 1
              em += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
              fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= 100.0 * em / total
       lowerCamelCase       :				List[str]      			= 100.0 * fa / total

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

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Tuple:
       lowerCamelCase       :				Optional[int]      			= args.k
       lowerCamelCase       :				str      			= [line.strip() for line in open(_SCREAMING_SNAKE_CASE			,"r"   ).readlines()]
       lowerCamelCase       :				int      			= [line.strip() for line in open(_SCREAMING_SNAKE_CASE			,"r"   ).readlines()]

       lowerCamelCase       :				Optional[int]      			= 0
       for hypo, reference in zip(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   ):
              lowerCamelCase       :				Tuple      			= set(hypo.split("\t"   )[:k]   )
              lowerCamelCase       :				Optional[Any]      			= set(reference.split("\t"   )   )
              total += 1
              em += len(hypo_provenance & ref_provenance   ) / k

       lowerCamelCase       :				List[Any]      			= 100.0 * em / total
       logger.info(f'''Precision@{k}: {em: .2f}'''   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Tuple:
       def strip_title(_SCREAMING_SNAKE_CASE   ):
              if title.startswith("\""   ):
                     lowerCamelCase       :				List[Any]      			= title[1:]
              if title.endswith("\""   ):
                     lowerCamelCase       :				List[Any]      			= title[:-1]
              return title

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

       lowerCamelCase       :				List[Any]      			= rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Dict      			= question_enc_outputs[0]

       lowerCamelCase       :				Optional[int]      			= rag_model.retriever(
           _SCREAMING_SNAKE_CASE			,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"			,)
       lowerCamelCase       :				Tuple      			= rag_model.retriever.index.get_doc_dicts(result.doc_ids   )
       lowerCamelCase       :				int      			= []
       for docs in all_docs:
              lowerCamelCase       :				Union[str, Any]      			= [strip_title(_SCREAMING_SNAKE_CASE   ) for title in docs["title"]]
              provenance_strings.append("\t".join(_SCREAMING_SNAKE_CASE   )   )
       return provenance_strings

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Dict:
       with torch.no_grad():
              lowerCamelCase       :				str      			= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  _SCREAMING_SNAKE_CASE			,return_tensors="pt"			,padding=_SCREAMING_SNAKE_CASE			,truncation=_SCREAMING_SNAKE_CASE   )

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

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

              return answers

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

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Dict:
       lowerCamelCase       :				Any      			= {}
       if args.model_type is None:
              lowerCamelCase       :				str      			= infer_model_type(args.model_name_or_path   )
              assert args.model_type is not None
       if args.model_type.startswith("rag"   ):
              lowerCamelCase       :				Optional[Any]      			= RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              lowerCamelCase       :				Any      			= args.n_docs
              if args.index_name is not None:
                     lowerCamelCase       :				List[Any]      			= args.index_name
              if args.index_path is not None:
                     lowerCamelCase       :				str      			= args.index_path
       else:
              lowerCamelCase       :				Any      			= BartForConditionalGeneration

       lowerCamelCase       :				str      			= (
           [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"			,_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				str      			= get_scores if args.eval_mode == "e2e" else get_precision_at_k
       lowerCamelCase       :				List[Any]      			= 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(_SCREAMING_SNAKE_CASE			,args.predictions_path			,args.gold_data_path   )
                     continue

              logger.info("***** Running evaluation for {} *****".format(_SCREAMING_SNAKE_CASE   )   )
              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"   ):
                     lowerCamelCase       :				Any      			= RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE   )
                     lowerCamelCase       :				int      			= model_class.from_pretrained(_SCREAMING_SNAKE_CASE			,retriever=_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE   )
                     model.retriever.init_retrieval()
              else:
                     lowerCamelCase       :				Any      			= model_class.from_pretrained(_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE   )
              model.to(args.device   )

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

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				Union[str, Any]  =       get_args()
       main(args)












import numpy as np
import qiskit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Optional[int]      			= qiskit.QuantumCircuit(num_qubits + 1   )

       # First, let's deal with the case in which oracle is balanced
       if case == "balanced":
              # First generate a random number that tells us which CNOTs to
              # wrap in X-gates:
              lowerCamelCase       :				Optional[int]      			= np.random.randint(1			,2**num_qubits   )
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              lowerCamelCase       :				str      			= format(_SCREAMING_SNAKE_CASE			,f'''0{num_qubits}b'''   )
              # Next, we place the first X-gates. Each digit in our binary string
              # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
              # we apply an X-gate to that qubit:
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
              for index in range(_SCREAMING_SNAKE_CASE   ):
                     oracle_qc.cx(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
              # Next, place the final X-gates
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )

    # Case in which oracle is constant
       if case == "constant":
              # First decide what the fixed output of the oracle will be
              # (either always 0 or always 1)
              lowerCamelCase       :				str      			= np.random.randint(2   )
              if output == 1:
                     oracle_qc.x(_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= oracle_qc.to_gate()
       lowerCamelCase       :				Tuple      			= "Oracle"  # To show when we display the circuit
       return oracle_gate

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Union[str, Any]      			= qiskit.QuantumCircuit(num_qubits + 1			,_SCREAMING_SNAKE_CASE   )
       # Set up the output qubit:
       dj_circuit.x(_SCREAMING_SNAKE_CASE   )
       dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # And set up the input register:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )
       # Let's append the oracle gate to our circuit:
       dj_circuit.append(_SCREAMING_SNAKE_CASE			,range(num_qubits + 1   )   )
       # Finally, perform the H-gates again and measure:
       for qubit in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.h(_SCREAMING_SNAKE_CASE   )

       for i in range(_SCREAMING_SNAKE_CASE   ):
              dj_circuit.measure(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       return dj_circuit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.result.counts.Counts:
       lowerCamelCase       :				Tuple      			= qiskit.Aer.get_backend("aer_simulator"   )

       lowerCamelCase       :				Tuple      			= dj_oracle(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= dj_algorithm(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       # Execute the circuit on the simulator
       lowerCamelCase       :				List[Any]      			= qiskit.execute(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,shots=1000   )

       # Return the histogram data of the results of the experiment.
       return job.result().get_counts(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       print(f'''Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}''')
       print(f'''Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}''')






