



'''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


a   :			str					  =    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



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



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			int:
							UpperCAmelCase     :		List[Any]							     =		[line.strip() for line in open(_lowercase	,				"""r"""		).readlines()]
							UpperCAmelCase     :		Tuple							     =		[]

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

							UpperCAmelCase     :		str							     =		0
							for prediction, ground_truths in zip(_lowercase	,				_lowercase		):
														total += 1
														em += metric_max_over_ground_truths(_lowercase	,				_lowercase	,				_lowercase		)
														fa += metric_max_over_ground_truths(_lowercase	,				_lowercase	,				_lowercase		)

							UpperCAmelCase     :		Tuple							     =		100.0 * em / total
							UpperCAmelCase     :		Any							     =		100.0 * fa / total

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



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		Union[str, Any]							     =		args.k
							UpperCAmelCase     :		Union[str, Any]							     =		[line.strip() for line in open(_lowercase	,				"""r"""		).readlines()]
							UpperCAmelCase     :		str							     =		[line.strip() for line in open(_lowercase	,				"""r"""		).readlines()]

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

							UpperCAmelCase     :		Dict							     =		100.0 * em / total
							logger.info(F'''Precision@{k}: {em: .2f}'''		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			int:
							def strip_title(_lowercase		):
														if title.startswith("""\""""		):
																					UpperCAmelCase     :		Union[str, Any]							     =		title[1:]
														if title.endswith("""\""""		):
																					UpperCAmelCase     :		Optional[int]							     =		title[:-1]
														return title

							UpperCAmelCase     :		Optional[Any]							     =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    _lowercase	,				return_tensors="""pt"""	,				padding=_lowercase	,				truncation=_lowercase	,				)["""input_ids"""].to(args.device		)

							UpperCAmelCase     :		str							     =		rag_model.rag.question_encoder(_lowercase		)
							UpperCAmelCase     :		Tuple							     =		question_enc_outputs[0]

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



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Tuple:
							with torch.no_grad():
														UpperCAmelCase     :		Union[str, Any]							     =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    _lowercase	,				return_tensors="""pt"""	,				padding=_lowercase	,				truncation=_lowercase		)

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

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

														return answers



def       __lowerCamelCase  (					)      ->			List[Any]:
							UpperCAmelCase     :		Any							     =		argparse.ArgumentParser()
							parser.add_argument(
							    """--model_type"""	,				choices=["""rag_sequence""", """rag_token""", """bart"""]	,				type=_lowercase	,				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=_lowercase	,				choices=["""exact""", """compressed""", """legacy"""]	,				type=_lowercase	,				help="""RAG model retriever type"""	,				)
							parser.add_argument(
							    """--index_path"""	,				default=_lowercase	,				type=_lowercase	,				help="""Path to the retrieval index"""	,				)
							parser.add_argument("""--n_docs"""	,				default=5	,				type=_lowercase	,				help="""Number of retrieved docs"""		)
							parser.add_argument(
							    """--model_name_or_path"""	,				default=_lowercase	,				type=_lowercase	,				required=_lowercase	,				help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""	,				)
							parser.add_argument(
							    """--eval_mode"""	,				choices=["""e2e""", """retrieval"""]	,				default="""e2e"""	,				type=_lowercase	,				help=(
							        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
							        """ precision@k."""
							    )	,				)
							parser.add_argument("""--k"""	,				default=1	,				type=_lowercase	,				help="""k for the precision@k calculation"""		)
							parser.add_argument(
							    """--evaluation_set"""	,				default=_lowercase	,				type=_lowercase	,				required=_lowercase	,				help="""Path to a file containing evaluation samples"""	,				)
							parser.add_argument(
							    """--gold_data_path"""	,				default=_lowercase	,				type=_lowercase	,				required=_lowercase	,				help="""Path to a tab-separated file with gold samples"""	,				)
							parser.add_argument(
							    """--gold_data_mode"""	,				default="""qa"""	,				type=_lowercase	,				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=_lowercase	,				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=_lowercase	,				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=_lowercase	,				help="""Number of beams to be used when generating answers"""	,				)
							parser.add_argument("""--min_length"""	,				default=1	,				type=_lowercase	,				help="""Min length of the generated answers"""		)
							parser.add_argument("""--max_length"""	,				default=5_0	,				type=_lowercase	,				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     :		int							     =		parser.parse_args()
							UpperCAmelCase     :		Union[str, Any]							     =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)
							return args



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

							UpperCAmelCase     :		int							     =		(
							    [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"""	,				_lowercase		)

							UpperCAmelCase     :		int							     =		get_scores if args.eval_mode == """e2e""" else get_precision_at_k
							UpperCAmelCase     :		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(_lowercase	,				args.predictions_path	,				args.gold_data_path		)
																					continue

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

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


if __name__ == "__main__":
							a   :			Optional[int]					  =    get_args()
							main(args)






'''simple docstring'''




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

a   :			Union[str, Any]					  =    logging.get_logger(__name__)

@add_end_docstrings(__magic_name__			)
class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		**A    )			->			List[str]:
								super().__init__(**A    )

								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		,		A		,		**A    )			->			Optional[Any]:
								return super().__call__(A		,		**A    )





	def  _lowercase(  self		,		**A    )			->			Optional[Any]:
								UpperCAmelCase     :		List[Any]							     =		{}
								if "candidate_labels" in kwargs:
															UpperCAmelCase     :		Dict							     =		kwargs["""candidate_labels"""]
								if "hypothesis_template" in kwargs:
															UpperCAmelCase     :		Optional[Any]							     =		kwargs["""hypothesis_template"""]

								return preprocess_params, {}, {}





	def  _lowercase(  self		,		A		,		A=None		,		A="This is a photo of {}."    )			->			Optional[Any]:
								UpperCAmelCase     :		int							     =		load_image(A    )
								UpperCAmelCase     :		List[str]							     =		self.image_processor(images=[image]		,		return_tensors=self.framework    )
								UpperCAmelCase     :		List[str]							     =		candidate_labels
								UpperCAmelCase     :		Tuple							     =		[hypothesis_template.format(A    ) for x in candidate_labels]
								UpperCAmelCase     :		Union[str, Any]							     =		self.tokenizer(A		,		return_tensors=self.framework		,		padding=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		[text_inputs]
								return inputs





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		List[Any]							     =		model_inputs.pop("""candidate_labels"""    )
								UpperCAmelCase     :		Optional[Any]							     =		model_inputs.pop("""text_inputs"""    )
								if isinstance(text_inputs[0]		,		A    ):
															UpperCAmelCase     :		Optional[Any]							     =		text_inputs[0]
								else:
															# Batching case.
															UpperCAmelCase     :		Any							     =		text_inputs[0][0]

								UpperCAmelCase     :		Dict							     =		self.model(**A		,		**A    )

								UpperCAmelCase     :		List[Any]							     =		{
								    """candidate_labels""": candidate_labels,
								    """logits""": outputs.logits_per_image,
								}
								return model_outputs






	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		model_outputs.pop("""candidate_labels"""    )
								UpperCAmelCase     :		int							     =		model_outputs["""logits"""][0]
								if self.framework == "pt":
															UpperCAmelCase     :		Optional[int]							     =		logits.softmax(dim=-1    ).squeeze(-1    )
															UpperCAmelCase     :		Any							     =		probs.tolist()
															if not isinstance(A		,		A    ):
																						UpperCAmelCase     :		Any							     =		[scores]
								elif self.framework == "tf":
															UpperCAmelCase     :		List[str]							     =		stable_softmax(A		,		axis=-1    )
															UpperCAmelCase     :		Union[str, Any]							     =		probs.numpy().tolist()
								else:
															raise ValueError(f'''Unsupported framework: {self.framework}'''    )

								UpperCAmelCase     :		Any							     =		[
								    {"""score""": score, """label""": candidate_label}
								    for score, candidate_label in sorted(zip(A		,		A    )		,		key=lambda A    : -x[0]    )
								]
								return result

