


'''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


lowerCAmelCase   : Optional[Any]             =				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def 	_A  (       A							)  ->				Optional[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  (       A							,A							,A							)  ->				Tuple:
	return max(metric_fn(A							,A							) for gt in ground_truths							)




def 	_A  (       A							,A							,A							)  ->				Union[str, Any]:
	lowercase						:		Tuple   					=		[line.strip() for line in open(A							,"r"							).readlines()]
	lowercase						:		Optional[Any]   					=		[]

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

	lowercase						:		Optional[int]   					=		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							)

	lowercase						:		Optional[Any]   					=		100.0 * em / total
	lowercase						:		Tuple   					=		100.0 * fa / total

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




def 	_A  (       A							,A							,A							)  ->				Optional[Any]:
	lowercase						:		int   					=		args.k
	lowercase						:		List[str]   					=		[line.strip() for line in open(A							,"r"							).readlines()]
	lowercase						:		Any   					=		[line.strip() for line in open(A							,"r"							).readlines()]

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

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




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

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

	lowercase						:		Optional[Any]   					=		rag_model.rag.question_encoder(A							)
	lowercase						:		Optional[int]   					=		question_enc_outputs[0]

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




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	with torch.no_grad():
		lowercase						:		Optional[int]   					=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    A							,return_tensors="pt"							,padding=A							,truncation=A							)

		lowercase						:		Tuple   					=		inputs_dict.input_ids.to(args.device							)
		lowercase						:		str   					=		inputs_dict.attention_mask.to(args.device							)
		lowercase						:		Dict   					=		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]]							,)
		lowercase						:		Dict   					=		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 	_A  (       )  ->				Optional[int]:
	lowercase						:		Tuple   					=		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=5_0							,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."							,)
	lowercase						:		Union[str, Any]   					=		parser.parse_args()
	lowercase						:		int   					=		torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
	return args




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

	lowercase						:		Dict   					=		(
	    [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							)

	lowercase						:		Optional[int]   					=		get_scores if args.eval_mode == "e2e" else get_precision_at_k
	lowercase						:		List[str]   					=		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"							):
			lowercase						:		Dict   					=		RagRetriever.from_pretrained(A							,**A							)
			lowercase						:		List[str]   					=		model_class.from_pretrained(A							,retriever=A							,**A							)
			model.retriever.init_retrieval()
		else:
			lowercase						:		Tuple   					=		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:
			lowercase						:		str   					=		[]
			for line in tqdm(A							):
				questions.append(line.strip()							)
				if len(A							) == args.eval_batch_size:
					lowercase						:		Optional[Any]   					=		evaluate_batch_fn(A							,A							,A							)
					preds_file.write("\n".join(A							) + "\n"							)
					preds_file.flush()
					lowercase						:		Tuple   					=		[]
			if len(A							) > 0:
				lowercase						:		Tuple   					=		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   : str             =				get_args()
					main(args)






'''simple docstring'''
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
					raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
lowerCAmelCase   : Optional[int]             =				logging.get_logger(__name__)

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		Optional[Any]   					=		FairseqRobertaModel.from_pretrained(A							)
	roberta.eval()  # disable dropout
	lowercase						:		int   					=		roberta.model.encoder.sentence_encoder
	lowercase						:		Tuple   					=		XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings							,hidden_size=roberta.cfg.model.encoder_embed_dim							,num_hidden_layers=roberta.cfg.model.encoder_layers							,num_attention_heads=roberta.cfg.model.encoder_attention_heads							,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim							,max_position_embeddings=5_1_4							,type_vocab_size=1							,layer_norm_eps=1e-5							,)
	if classification_head:
		lowercase						:		Tuple   					=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,A							)

	lowercase						:		Union[str, Any]   					=		XLMRobertaXLForSequenceClassification(A							) if classification_head else XLMRobertaXLForMaskedLM(A							)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowercase						:		Optional[Any]   					=		roberta_sent_encoder.embed_tokens.weight
	lowercase						:		Dict   					=		roberta_sent_encoder.embed_positions.weight
	lowercase						:		str   					=		torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers							):
		# Encoder: start of layer
		lowercase						:		BertLayer   					=		model.roberta.encoder.layer[i]
		lowercase						:		TransformerSentenceEncoderLayer   					=		roberta_sent_encoder.layers[i]

		lowercase						:		RobertaAttention   					=		layer.attention
		lowercase						:		str   					=		roberta_layer.self_attn_layer_norm.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowercase						:		BertSelfAttention   					=		layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)							)
		)

		lowercase						:		str   					=		roberta_layer.self_attn.q_proj.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn.q_proj.bias
		lowercase						:		Union[str, Any]   					=		roberta_layer.self_attn.k_proj.weight
		lowercase						:		List[Any]   					=		roberta_layer.self_attn.k_proj.bias
		lowercase						:		Dict   					=		roberta_layer.self_attn.v_proj.weight
		lowercase						:		Tuple   					=		roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowercase						:		BertSelfOutput   					=		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowercase						:		List[Any]   					=		roberta_layer.final_layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta_layer.final_layer_norm.bias

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

		# output
		lowercase						:		BertOutput   					=		layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		str   					=		roberta_layer.fca.bias
		# end of layer

	if classification_head:
		lowercase						:		int   					=		roberta.model.classification_heads["mnli"].dense.weight
		lowercase						:		List[Any]   					=		roberta.model.classification_heads["mnli"].dense.bias
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"].out_proj.weight
		lowercase						:		Any   					=		roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		lowercase						:		str   					=		roberta.model.encoder.lm_head.dense.weight
		lowercase						:		List[Any]   					=		roberta.model.encoder.lm_head.dense.bias
		lowercase						:		str   					=		roberta.model.encoder.lm_head.layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta.model.encoder.lm_head.layer_norm.bias
		lowercase						:		int   					=		roberta.model.encoder.lm_head.weight
		lowercase						:		Optional[int]   					=		roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowercase						:		torch.Tensor   					=		roberta.encode(A							).unsqueeze(0							)  # batch of size 1

	lowercase						:		Tuple   					=		model(A							)[0]
	if classification_head:
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"](roberta.extract_features(A							)							)
	else:
		lowercase						:		Optional[Any]   					=		roberta.model(A							)[0]
	print(our_output.shape							,their_output.shape							)
	lowercase						:		Optional[Any]   					=		torch.max(torch.abs(our_output - their_output							)							).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
	lowercase						:		Dict   					=		torch.allclose(A							,A							,atol=1e-3							)
	print("Do both models output the same tensors?"							,"🔥" if success else "💩"							)
	if not success:
		raise Exception("Something went wRoNg"							)

	pathlib.Path(A							).mkdir(parents=A							,exist_ok=A							)
	print(F'''Saving model to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
					)
					lowerCAmelCase   : Optional[Any]             =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


