



import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def A						(       )						->      Tuple:
			A__       =    argparse.ArgumentParser()
			parser.add_argument(
			    '-m' ,	'--pretrained_model_name_or_path' ,	type=__UpperCamelCase ,	default=__UpperCamelCase ,	required=__UpperCamelCase ,	help='Path to pretrained model or model identifier from huggingface.co/models.' ,	)
			parser.add_argument(
			    '-c' ,	'--caption' ,	type=__UpperCamelCase ,	default='robotic cat with wings' ,	help='Text used to generate images.' ,	)
			parser.add_argument(
			    '-n' ,	'--images_num' ,	type=__UpperCamelCase ,	default=4 ,	help='How much images to generate.' ,	)
			parser.add_argument(
			    '-s' ,	'--seed' ,	type=__UpperCamelCase ,	default=42 ,	help='Seed for random process.' ,	)
			parser.add_argument(
			    '-ci' ,	'--cuda_id' ,	type=__UpperCamelCase ,	default=0 ,	help='cuda_id.' ,	)
			A__       =    parser.parse_args()
			return args


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[Any]:
			if not len(__UpperCamelCase					) == rows * cols:
						raise ValueError('The specified number of rows and columns are not correct.'					)

			A__     , A__       =    imgs[0].size
			A__       =    Image.new('RGB' ,	size=(cols * w, rows * h)					)
			A__     , A__       =    grid.size

			for i, img in enumerate(__UpperCamelCase					):
						grid.paste(__UpperCamelCase ,	box=(i % cols * w, i // cols * h)					)
			return grid


def A						(       __UpperCamelCase ,	__UpperCamelCase="robotic cat with wings" ,	__UpperCamelCase=7.5 ,	__UpperCamelCase=50 ,	__UpperCamelCase=1 ,	__UpperCamelCase=42 ,	)						->      Union[str, Any]:
			A__       =    torch.Generator(pipeline.device					).manual_seed(__UpperCamelCase					)
			A__       =    pipeline(
			    __UpperCamelCase ,	guidance_scale=__UpperCamelCase ,	num_inference_steps=__UpperCamelCase ,	generator=__UpperCamelCase ,	num_images_per_prompt=__UpperCamelCase ,	).images
			A__       =    int(math.sqrt(__UpperCamelCase					)					)
			A__       =    image_grid(__UpperCamelCase ,	rows=_rows ,	cols=num_images_per_prompt // _rows					)
			return grid, images


SCREAMING_SNAKE_CASE__       =      parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__       =      CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
SCREAMING_SNAKE_CASE__       =      CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
SCREAMING_SNAKE_CASE__       =      AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
SCREAMING_SNAKE_CASE__       =      UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

SCREAMING_SNAKE_CASE__       =      StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__       =      lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
						SCREAMING_SNAKE_CASE__       =      load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, '''unet''', unet)
else:
						SCREAMING_SNAKE_CASE__       =      unet.to(torch.device('''cuda''', args.cuda_id))
SCREAMING_SNAKE_CASE__       =      pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__       =      generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
SCREAMING_SNAKE_CASE__       =      os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
						image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))









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


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

transformers_logging.set_verbosity_info()


def A						(       __UpperCamelCase					)						->      Tuple:
			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						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return max(metric_fn(__UpperCamelCase ,	__UpperCamelCase					) for gt in ground_truths					)


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      List[str]:
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]
			A__       =    []

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

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

			A__       =    100.0 * em / total
			A__       =    100.0 * fa / total

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[int]:
			A__       =    args.k
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]
			A__       =    [line.strip() for line in open(__UpperCamelCase ,	'r'					).readlines()]

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

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


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

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

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

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[Any]:
			with torch.no_grad():
						A__       =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    __UpperCamelCase ,	return_tensors='pt' ,	padding=__UpperCamelCase ,	truncation=__UpperCamelCase					)

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

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

						return answers


def A						(       )						->      Any:
			A__       =    argparse.ArgumentParser()
			parser.add_argument(
			    '--model_type' ,	choices=['rag_sequence', 'rag_token', 'bart'] ,	type=__UpperCamelCase ,	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=__UpperCamelCase ,	choices=['exact', 'compressed', 'legacy'] ,	type=__UpperCamelCase ,	help='RAG model retriever type' ,	)
			parser.add_argument(
			    '--index_path' ,	default=__UpperCamelCase ,	type=__UpperCamelCase ,	help='Path to the retrieval index' ,	)
			parser.add_argument('--n_docs' ,	default=5 ,	type=__UpperCamelCase ,	help='Number of retrieved docs'					)
			parser.add_argument(
			    '--model_name_or_path' ,	default=__UpperCamelCase ,	type=__UpperCamelCase ,	required=__UpperCamelCase ,	help='Path to pretrained checkpoints or model identifier from huggingface.co/models' ,	)
			parser.add_argument(
			    '--eval_mode' ,	choices=['e2e', 'retrieval'] ,	default='e2e' ,	type=__UpperCamelCase ,	help=(
			        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
			        ' precision@k.'
			    ) ,	)
			parser.add_argument('--k' ,	default=1 ,	type=__UpperCamelCase ,	help='k for the precision@k calculation'					)
			parser.add_argument(
			    '--evaluation_set' ,	default=__UpperCamelCase ,	type=__UpperCamelCase ,	required=__UpperCamelCase ,	help='Path to a file containing evaluation samples' ,	)
			parser.add_argument(
			    '--gold_data_path' ,	default=__UpperCamelCase ,	type=__UpperCamelCase ,	required=__UpperCamelCase ,	help='Path to a tab-separated file with gold samples' ,	)
			parser.add_argument(
			    '--gold_data_mode' ,	default='qa' ,	type=__UpperCamelCase ,	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=__UpperCamelCase ,	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=__UpperCamelCase ,	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=__UpperCamelCase ,	help='Number of beams to be used when generating answers' ,	)
			parser.add_argument('--min_length' ,	default=1 ,	type=__UpperCamelCase ,	help='Min length of the generated answers'					)
			parser.add_argument('--max_length' ,	default=50 ,	type=__UpperCamelCase ,	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.' ,	)
			A__       =    parser.parse_args()
			A__       =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
			return args


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

			A__       =    (
			    [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' ,	__UpperCamelCase					)

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

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

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      get_args()
							main(args)




