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

transformers_logging.set_verbosity_info()




def 	A_ (							snake_case							:   Dict )      ->    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 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   List[str] ,      snake_case							:   Optional[int] )      ->    str:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Dict ,      snake_case							:   str ,      snake_case							:   str )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]
						__UpperCamelCase       =			[]

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

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

						__UpperCamelCase       =			100.0 * em / total
						__UpperCamelCase       =			100.0 * fa / total

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




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   Any ,      snake_case							:   Dict )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			args.k
						__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]
						__UpperCamelCase       =			[line.strip() for line in open(snake_case ,      '''r''' ).readlines()]

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

						__UpperCamelCase       =			100.0 * em / total
						logger.info(f"Precision@{k}: {em: .2f}" )




def 	A_ (							snake_case							:   int ,      snake_case							:   List[Any] ,      snake_case							:   Optional[Any] )      ->    Tuple:



						'''simple docstring'''

						def strip_title(snake_case							:   Union[str, Any] ):
												if title.startswith('''"''' ):
																		__UpperCamelCase       =			title[1:]
												if title.endswith('''"''' ):
																		__UpperCamelCase       =			title[:-1]
												return title

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

						__UpperCamelCase       =			rag_model.rag.question_encoder(snake_case )
						__UpperCamelCase       =			question_enc_outputs[0]

						__UpperCamelCase       =			rag_model.retriever(
						    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''' ,      )
						__UpperCamelCase       =			rag_model.retriever.index.get_doc_dicts(result.doc_ids )
						__UpperCamelCase       =			[]
						for docs in all_docs:
												__UpperCamelCase       =			[strip_title(snake_case ) for title in docs['''title''']]
												provenance_strings.append('''\t'''.join(snake_case ) )
						return provenance_strings




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   List[Any] ,      snake_case							:   str )      ->    Tuple:



						'''simple docstring'''

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

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

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

												return answers




def 	A_ (							)      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_type''' ,      choices=['''rag_sequence''', '''rag_token''', '''bart'''] ,      type=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=snake_case ,      choices=['''exact''', '''compressed''', '''legacy'''] ,      type=snake_case ,      help='''RAG model retriever type''' ,      )
						parser.add_argument(
						    '''--index_path''' ,      default=snake_case ,      type=snake_case ,      help='''Path to the retrieval index''' ,      )
						parser.add_argument('''--n_docs''' ,      default=5 ,      type=snake_case ,      help='''Number of retrieved docs''' )
						parser.add_argument(
						    '''--model_name_or_path''' ,      default=snake_case ,      type=snake_case ,      required=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=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=snake_case ,      help='''k for the precision@k calculation''' )
						parser.add_argument(
						    '''--evaluation_set''' ,      default=snake_case ,      type=snake_case ,      required=snake_case ,      help='''Path to a file containing evaluation samples''' ,      )
						parser.add_argument(
						    '''--gold_data_path''' ,      default=snake_case ,      type=snake_case ,      required=snake_case ,      help='''Path to a tab-separated file with gold samples''' ,      )
						parser.add_argument(
						    '''--gold_data_mode''' ,      default='''qa''' ,      type=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=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=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=snake_case ,      help='''Number of beams to be used when generating answers''' ,      )
						parser.add_argument('''--min_length''' ,      default=1 ,      type=snake_case ,      help='''Min length of the generated answers''' )
						parser.add_argument('''--max_length''' ,      default=50 ,      type=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.''' ,      )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
						return args




def 	A_ (							snake_case							:   Any )      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			{}
						if args.model_type is None:
												__UpperCamelCase       =			infer_model_type(args.model_name_or_path )
												assert args.model_type is not None
						if args.model_type.startswith('''rag''' ):
												__UpperCamelCase       =			RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												__UpperCamelCase       =			args.n_docs
												if args.index_name is not None:
																		__UpperCamelCase       =			args.index_name
												if args.index_path is not None:
																		__UpperCamelCase       =			args.index_path
						else:
												__UpperCamelCase       =			BartForConditionalGeneration

						__UpperCamelCase       =			(
						    [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''' ,      snake_case )

						__UpperCamelCase       =			get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						__UpperCamelCase       =			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(snake_case ,      args.predictions_path ,      args.gold_data_path )
																		continue

												logger.info('''***** Running evaluation for {} *****'''.format(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''' ):
																		__UpperCamelCase       =			RagRetriever.from_pretrained(snake_case ,      **snake_case )
																		__UpperCamelCase       =			model_class.from_pretrained(snake_case ,      retriever=snake_case ,      **snake_case )
																		model.retriever.init_retrieval()
												else:
																		__UpperCamelCase       =			model_class.from_pretrained(snake_case ,      **snake_case )
												model.to(args.device )

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

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


if __name__ == "__main__":
				lowercase__							:							List[str]									=			get_args()
				main(args)





from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
				from ..tf_utils import stable_softmax

lowercase__							:							int									=			logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE_    )
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)

													requires_backends(self     ,	'''vision'''			)
													self.check_model_type(
													    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
													    if self.framework == '''tf'''
													    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING			)



							def __call__(  self     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													return super().__call__(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			{}
													if "candidate_labels" in kwargs:
																			__UpperCamelCase       =			kwargs['''candidate_labels''']
													if "hypothesis_template" in kwargs:
																			__UpperCamelCase       =			kwargs['''hypothesis_template''']

													return preprocess_params, {}, {}



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="This is a photo of {}."			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			load_image(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.image_processor(images=[image]     ,	return_tensors=self.framework			)
													__UpperCamelCase       =			candidate_labels
													__UpperCamelCase       =			[hypothesis_template.format(SCREAMING_SNAKE_CASE_			) for x in candidate_labels]
													__UpperCamelCase       =			self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=self.framework     ,	padding=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			[text_inputs]
													return inputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			model_inputs.pop('''candidate_labels'''			)
													__UpperCamelCase       =			model_inputs.pop('''text_inputs'''			)
													if isinstance(text_inputs[0]     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			text_inputs[0]
													else:
																			# Batching case.
																			__UpperCamelCase       =			text_inputs[0][0]

													__UpperCamelCase       =			self.model(**SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			{
													    '''candidate_labels''': candidate_labels,
													    '''logits''': outputs.logits_per_image,
													}
													return model_outputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			model_outputs.pop('''candidate_labels'''			)
													__UpperCamelCase       =			model_outputs['''logits'''][0]
													if self.framework == "pt":
																			__UpperCamelCase       =			logits.softmax(dim=-1			).squeeze(-1			)
																			__UpperCamelCase       =			probs.tolist()
																			if not isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			[scores]
													elif self.framework == "tf":
																			__UpperCamelCase       =			stable_softmax(SCREAMING_SNAKE_CASE_     ,	axis=-1			)
																			__UpperCamelCase       =			probs.numpy().tolist()
													else:
																			raise ValueError(F"Unsupported framework: {self.framework}"			)

													__UpperCamelCase       =			[
													    {'''score''': score, '''label''': candidate_label}
													    for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)     ,	key=lambda SCREAMING_SNAKE_CASE_			: -x[0]			)
													]
													return result




