

"""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  :      int							      =		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	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   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	return max(metric_fn(A_						,     A_ ) for gt in ground_truths )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]
	lowerCAmelCase__	:						str  				=	[]

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

	lowerCAmelCase__	:						Any  				=	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_ )

	lowerCAmelCase__	:						Any  				=	100.0 * em / total
	lowerCAmelCase__	:						List[Any]  				=	100.0 * fa / total

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	args.k
	lowerCAmelCase__	:						Union[str, Any]  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]
	lowerCAmelCase__	:						Union[str, Any]  				=	[line.strip() for line in open(A_						,     '''r''' ).readlines()]

	lowerCAmelCase__	:						Optional[int]  				=	0
	for hypo, reference in zip(A_						,     A_ ):
		lowerCAmelCase__	:						List[str]  				=	set(hypo.split('''\t''' )[:k] )
		lowerCAmelCase__	:						int  				=	set(reference.split('''\t''' ) )
		total += 1
		em += len(hypo_provenance & ref_provenance ) / k

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	def strip_title(A_ ):
		if title.startswith('''"''' ):
			lowerCAmelCase__	:						List[str]  				=	title[1:]
		if title.endswith('''"''' ):
			lowerCAmelCase__	:						Optional[Any]  				=	title[:-1]
		return title

	lowerCAmelCase__	:						str  				=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    A_						,     return_tensors='''pt'''						,     padding=A_						,     truncation=A_						,     )['''input_ids'''].to(args.device )

	lowerCAmelCase__	:						Optional[int]  				=	rag_model.rag.question_encoder(A_ )
	lowerCAmelCase__	:						str  				=	question_enc_outputs[0]

	lowerCAmelCase__	:						Any  				=	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'''						,     )
	lowerCAmelCase__	:						Optional[Any]  				=	rag_model.retriever.index.get_doc_dicts(result.doc_ids )
	lowerCAmelCase__	:						Tuple  				=	[]
	for docs in all_docs:
		lowerCAmelCase__	:						int  				=	[strip_title(A_ ) for title in docs['''title''']]
		provenance_strings.append('''\t'''.join(A_ ) )
	return provenance_strings

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	with torch.no_grad():
		lowerCAmelCase__	:						Tuple  				=	rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    A_						,     return_tensors='''pt'''						,     padding=A_						,     truncation=A_ )

		lowerCAmelCase__	:						Dict  				=	inputs_dict.input_ids.to(args.device )
		lowerCAmelCase__	:						Tuple  				=	inputs_dict.attention_mask.to(args.device )
		lowerCAmelCase__	:						List[str]  				=	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]]						,     )
		lowerCAmelCase__	:						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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	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=50						,     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.'''						,     )
	lowerCAmelCase__	:						List[str]  				=	parser.parse_args()
	lowerCAmelCase__	:						Tuple  				=	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
	return args

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						int  				=	{}
	if args.model_type is None:
		lowerCAmelCase__	:						Optional[int]  				=	infer_model_type(args.model_name_or_path )
		assert args.model_type is not None
	if args.model_type.startswith('''rag''' ):
		lowerCAmelCase__	:						Dict  				=	RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
		lowerCAmelCase__	:						int  				=	args.n_docs
		if args.index_name is not None:
			lowerCAmelCase__	:						List[str]  				=	args.index_name
		if args.index_path is not None:
			lowerCAmelCase__	:						Optional[int]  				=	args.index_path
	else:
		lowerCAmelCase__	:						Dict  				=	BartForConditionalGeneration

	lowerCAmelCase__	:						Optional[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'''						,     A_ )

	lowerCAmelCase__	:						Tuple  				=	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(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''' ):
			lowerCAmelCase__	:						Dict  				=	RagRetriever.from_pretrained(A_						,     **A_ )
			lowerCAmelCase__	:						List[Any]  				=	model_class.from_pretrained(A_						,     retriever=A_						,     **A_ )
			model.retriever.init_retrieval()
		else:
			lowerCAmelCase__	:						Union[str, Any]  				=	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:
			lowerCAmelCase__	:						Tuple  				=	[]
			for line in tqdm(A_ ):
				questions.append(line.strip() )
				if len(A_ ) == args.eval_batch_size:
					lowerCAmelCase__	:						int  				=	evaluate_batch_fn(A_						,     A_						,     A_ )
					preds_file.write('''\n'''.join(A_ ) + '''\n''' )
					preds_file.flush()
					lowerCAmelCase__	:						int  				=	[]
			if len(A_ ) > 0:
				lowerCAmelCase__	:						Optional[int]  				=	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__":
							__UpperCamelCase  :      Union[str, Any]							      =		get_args()
							main(args)











'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
