



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


def A						(       __UpperCamelCase					)						->      YolosConfig:
			A__       =    YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						A__       =    192
						A__       =    768
						A__       =    12
						A__       =    3
						A__       =    [800, 1_333]
						A__       =    False
			elif yolos_name == "yolos_s_dWr":
						A__       =    330
						A__       =    14
						A__       =    6
						A__       =    1_320
			elif "yolos_s" in yolos_name:
						A__       =    384
						A__       =    1_536
						A__       =    12
						A__       =    6
			elif "yolos_b" in yolos_name:
						A__       =    [800, 1_344]

			A__       =    91
			A__       =    'huggingface/label-files'
			A__       =    'coco-detection-id2label.json'
			A__       =    json.load(open(hf_hub_download(__UpperCamelCase ,	__UpperCamelCase ,	repo_type='dataset'					) ,	'r'					)					)
			A__       =    {int(__UpperCamelCase					): v for k, v in idalabel.items()}
			A__       =    idalabel
			A__       =    {v: k for k, v in idalabel.items()}

			return config


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      str:
			for i in range(config.num_hidden_layers					):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''					)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''					)
						# next, add query, keys and values (in that order) to the state dict
						A__       =    in_proj_weight[: config.hidden_size, :]
						A__       =    in_proj_bias[: config.hidden_size]
						A__       =    in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__       =    in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A__       =    in_proj_weight[-config.hidden_size :, :]
						A__       =    in_proj_bias[-config.hidden_size :]


def A						(       __UpperCamelCase					)						->      str:
			if "backbone" in name:
						A__       =    name.replace('backbone' ,	'vit'					)
			if "cls_token" in name:
						A__       =    name.replace('cls_token' ,	'embeddings.cls_token'					)
			if "det_token" in name:
						A__       =    name.replace('det_token' ,	'embeddings.detection_tokens'					)
			if "mid_pos_embed" in name:
						A__       =    name.replace('mid_pos_embed' ,	'encoder.mid_position_embeddings'					)
			if "pos_embed" in name:
						A__       =    name.replace('pos_embed' ,	'embeddings.position_embeddings'					)
			if "patch_embed.proj" in name:
						A__       =    name.replace('patch_embed.proj' ,	'embeddings.patch_embeddings.projection'					)
			if "blocks" in name:
						A__       =    name.replace('blocks' ,	'encoder.layer'					)
			if "attn.proj" in name:
						A__       =    name.replace('attn.proj' ,	'attention.output.dense'					)
			if "attn" in name:
						A__       =    name.replace('attn' ,	'attention.self'					)
			if "norm1" in name:
						A__       =    name.replace('norm1' ,	'layernorm_before'					)
			if "norm2" in name:
						A__       =    name.replace('norm2' ,	'layernorm_after'					)
			if "mlp.fc1" in name:
						A__       =    name.replace('mlp.fc1' ,	'intermediate.dense'					)
			if "mlp.fc2" in name:
						A__       =    name.replace('mlp.fc2' ,	'output.dense'					)
			if "class_embed" in name:
						A__       =    name.replace('class_embed' ,	'class_labels_classifier'					)
			if "bbox_embed" in name:
						A__       =    name.replace('bbox_embed' ,	'bbox_predictor'					)
			if "vit.norm" in name:
						A__       =    name.replace('vit.norm' ,	'vit.layernorm'					)

			return name


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      dict:
			for key in orig_state_dict.copy().keys():
						A__       =    orig_state_dict.pop(__UpperCamelCase					)

						if "qkv" in key:
									A__       =    key.split('.'					)
									A__       =    int(key_split[2]					)
									A__       =    model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												A__       =    val[:dim, :]
												A__       =    val[
												    dim : dim * 2, :
												]
												A__       =    val[-dim:, :]
									else:
												A__       =    val[:dim]
												A__       =    val[dim : dim * 2]
												A__       =    val[-dim:]
						else:
									A__       =    val

			return orig_state_dict


def A						(       )						->      torch.Tensor:
			A__       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__       =    Image.open(requests.get(__UpperCamelCase ,	stream=__UpperCamelCase					).raw					)
			return im


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      List[str]:
			A__       =    get_yolos_config(__UpperCamelCase					)

			# load original state_dict
			A__       =    torch.load(__UpperCamelCase ,	map_location='cpu'					)['model']

			# load 🤗 model
			A__       =    YolosForObjectDetection(__UpperCamelCase					)
			model.eval()
			A__       =    convert_state_dict(__UpperCamelCase ,	__UpperCamelCase					)
			model.load_state_dict(__UpperCamelCase					)

			# Check outputs on an image, prepared by YolosImageProcessor
			A__       =    800 if yolos_name != 'yolos_ti' else 512
			A__       =    YolosImageProcessor(format='coco_detection' ,	size=__UpperCamelCase					)
			A__       =    image_processor(images=prepare_img() ,	return_tensors='pt'					)
			A__       =    model(**__UpperCamelCase					)
			A__     , A__       =    outputs.logits, outputs.pred_boxes

			A__     , A__       =    None, None
			if yolos_name == "yolos_ti":
						A__       =    torch.tensor(
						    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
						A__       =    torch.tensor(
						    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]					)
			elif yolos_name == "yolos_s_200_pre":
						A__       =    torch.tensor(
						    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
						A__       =    torch.tensor(
						    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					)
			elif yolos_name == "yolos_s_300_pre":
						A__       =    torch.tensor(
						    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
						A__       =    torch.tensor(
						    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]					)
			elif yolos_name == "yolos_s_dWr":
						A__       =    torch.tensor(
						    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
						A__       =    torch.tensor(
						    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]					)
			elif yolos_name == "yolos_base":
						A__       =    torch.tensor(
						    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
						A__       =    torch.tensor(
						    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]					)
			else:
						raise ValueError(f'''Unknown yolos_name: {yolos_name}'''					)

			assert torch.allclose(logits[0, :3, :3] ,	__UpperCamelCase ,	atol=1E-4					)
			assert torch.allclose(pred_boxes[0, :3, :3] ,	__UpperCamelCase ,	atol=1E-4					)

			Path(__UpperCamelCase					).mkdir(exist_ok=__UpperCamelCase					)
			print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''					)
			model.save_pretrained(__UpperCamelCase					)
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
			image_processor.save_pretrained(__UpperCamelCase					)

			if push_to_hub:
						A__       =    {
						    'yolos_ti': 'yolos-tiny',
						    'yolos_s_200_pre': 'yolos-small',
						    'yolos_s_300_pre': 'yolos-small-300',
						    'yolos_s_dWr': 'yolos-small-dwr',
						    'yolos_base': 'yolos-base',
						}

						print('Pushing to the hub...'					)
						A__       =    model_mapping[yolos_name]
						image_processor.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)
						model.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--yolos_name''',
						    default='''yolos_s_200_pre''',
						    type=str,
						    help=(
						        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
						        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						SCREAMING_SNAKE_CASE__       =      parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









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)




