




'''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


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

transformers_logging.set_verbosity_info()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	return max(metric_fn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) for gt in ground_truths							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     []

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

	lowerCAmelCase   =     lowerCAmelCase   =     lowerCAmelCase   =     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   =     100.0 * em / total
	lowerCAmelCase   =     100.0 * fa / total

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     args.k
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]

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

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Any:







	"""simple docstring"""
	def strip_title(_SCREAMING_SNAKE_CASE					:						Union[str, Any]							):
		if title.startswith("""\""""							):
			lowerCAmelCase   =     title[1:]
		if title.endswith("""\""""							):
			lowerCAmelCase   =     title[:-1]
		return title

	lowerCAmelCase   =     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   =     rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     question_enc_outputs[0]

	lowerCAmelCase   =     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   =     rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
	lowerCAmelCase   =     []
	for docs in all_docs:
		lowerCAmelCase   =     [strip_title(_SCREAMING_SNAKE_CASE							) for title in docs["""title"""]]
		provenance_strings.append("""\t""".join(_SCREAMING_SNAKE_CASE							)							)
	return provenance_strings






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Tuple:







	"""simple docstring"""
	with torch.no_grad():
		lowerCAmelCase   =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _SCREAMING_SNAKE_CASE							,		return_tensors="""pt"""							,		padding=_SCREAMING_SNAKE_CASE							,		truncation=_SCREAMING_SNAKE_CASE							)

		lowerCAmelCase   =     inputs_dict.input_ids.to(args.device							)
		lowerCAmelCase   =     inputs_dict.attention_mask.to(args.device							)
		lowerCAmelCase   =     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   =     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 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     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   =     parser.parse_args()
	lowerCAmelCase   =     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)
	return args






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Optional[Any]:







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

	lowerCAmelCase   =     (
	    [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   =     get_scores if args.eval_mode == """e2e""" else get_precision_at_k
	lowerCAmelCase   =     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   =     RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase   =     model_class.from_pretrained(_SCREAMING_SNAKE_CASE							,		retriever=_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)
			model.retriever.init_retrieval()
		else:
			lowerCAmelCase   =     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   =     []
			for line in tqdm(_SCREAMING_SNAKE_CASE							):
				questions.append(line.strip()							)
				if len(_SCREAMING_SNAKE_CASE							) == args.eval_batch_size:
					lowerCAmelCase   =     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   =     []
			if len(_SCREAMING_SNAKE_CASE							) > 0:
				lowerCAmelCase   =     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__":
				UpperCAmelCase     		=   get_args()
				main(args)




'''simple docstring'''

import numpy

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_				)     ->     None:
							lowerCAmelCase   =     input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							lowerCAmelCase   =     numpy.random.rand(
							    self.input_array.shape[1]     ,	4				)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							lowerCAmelCase   =     numpy.random.rand(
							    4     ,	3				)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							lowerCAmelCase   =     numpy.random.rand(3     ,	1				)

							# Real output values provided.
							lowerCAmelCase   =     output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							lowerCAmelCase   =     numpy.zeros(output_array.shape				)


						def     __snake_case   ( self				)     ->     numpy.ndarray:
							lowerCAmelCase   =     sigmoid(
							    numpy.dot(self.input_array     ,	self.input_layer_and_first_hidden_layer_weights				)				)

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							lowerCAmelCase   =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer     ,	self.first_hidden_layer_and_second_hidden_layer_weights     ,	)				)

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							lowerCAmelCase   =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer     ,	self.second_hidden_layer_and_output_layer_weights     ,	)				)

							return self.layer_between_second_hidden_layer_and_output


						def     __snake_case   ( self				)     ->     None:
							lowerCAmelCase   =     numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T     ,	2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output				)     ,	)
							lowerCAmelCase   =     numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T     ,	numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output				)     ,	self.second_hidden_layer_and_output_layer_weights.T     ,	)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer				)     ,	)
							lowerCAmelCase   =     numpy.dot(
							    self.input_array.T     ,	numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output				)     ,	self.second_hidden_layer_and_output_layer_weights.T     ,	)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer				)     ,	self.first_hidden_layer_and_second_hidden_layer_weights.T     ,	)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer				)     ,	)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     None:
							for iteration in range(1     ,	iterations + 1				):
								lowerCAmelCase   =     self.feedforward()
								self.back_propagation()
								if give_loss:
									lowerCAmelCase   =     numpy.mean(numpy.square(output - self.feedforward()				)				)
									print(f'Iteration {iteration} Loss: {loss}'				)




						def     __snake_case   ( self     ,	A_				)     ->     int:
							lowerCAmelCase   =     input_arr

							lowerCAmelCase   =     sigmoid(
							    numpy.dot(self.array     ,	self.input_layer_and_first_hidden_layer_weights				)				)

							lowerCAmelCase   =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer     ,	self.first_hidden_layer_and_second_hidden_layer_weights     ,	)				)

							lowerCAmelCase   =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer     ,	self.second_hidden_layer_and_output_layer_weights     ,	)				)

							return int(self.layer_between_second_hidden_layer_and_output > 0.6				)







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return 1 / (1 + numpy.exp(-value							))






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return (value) * (1 - (value))






def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )							,		dtype=numpy.floataa							,		)

	# True output values for the given input values.
	lowerCAmelCase   =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,		dtype=numpy.floataa							)

	# Calling neural network class.
	lowerCAmelCase   =     TwoHiddenLayerNeuralNetwork(
	    input_array=_SCREAMING_SNAKE_CASE							,		output_array=_SCREAMING_SNAKE_CASE							)

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=_SCREAMING_SNAKE_CASE							,		iterations=10							,		give_loss=_SCREAMING_SNAKE_CASE							)

	return neural_network.predict(numpy.array(([1, 1, 1])							,		dtype=numpy.floataa							)							)


if __name__ == "__main__":
				example()