
"""simple docstring"""







import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
	__lowerCAmelCase      :						int						      =				Image.open(requests.get(_UpperCamelCase	,  stream=_UpperCamelCase		).raw		).convert('RGB'		)

	__lowerCAmelCase      :						List[Any]						      =				transforms.Compose(
	    [
	        transforms.Resize((image_size, image_size)	,  interpolation=InterpolationMode.BICUBIC		),
	        transforms.ToTensor(),
	        transforms.Normalize((0.48145466, 0.4578275, 0.40821073)	,  (0.26862954, 0.26130258, 0.27577711)		),
	    ]		)
	__lowerCAmelCase      :						Union[str, Any]						      =				transform(_UpperCamelCase		).unsqueeze(0		).to(_UpperCamelCase		)
	return image
def    __lowerCAmelCase    (_UpperCamelCase		):
	if "visual_encoder" in key:
		__lowerCAmelCase      :						int						      =				re.sub('visual_encoder*'	,  'vision_model.encoder'	,  _UpperCamelCase		)
	if "blocks" in key:
		__lowerCAmelCase      :						Tuple						      =				re.sub(r'blocks'	,  'layers'	,  _UpperCamelCase		)
	if "attn" in key:
		__lowerCAmelCase      :						Union[str, Any]						      =				re.sub(r'attn'	,  'self_attn'	,  _UpperCamelCase		)
	if "norm1" in key:
		__lowerCAmelCase      :						int						      =				re.sub(r'norm1'	,  'layer_norm1'	,  _UpperCamelCase		)
	if "norm2" in key:
		__lowerCAmelCase      :						Optional[Any]						      =				re.sub(r'norm2'	,  'layer_norm2'	,  _UpperCamelCase		)
	if "encoder.norm" in key:
		__lowerCAmelCase      :						Dict						      =				re.sub(r'encoder.norm'	,  'post_layernorm'	,  _UpperCamelCase		)
	if "encoder.patch_embed.proj" in key:
		__lowerCAmelCase      :						List[str]						      =				re.sub(r'encoder.patch_embed.proj'	,  'embeddings.patch_embedding'	,  _UpperCamelCase		)

	if "encoder.pos_embed" in key:
		__lowerCAmelCase      :						List[Any]						      =				re.sub(r'encoder.pos_embed'	,  'embeddings.position_embedding'	,  _UpperCamelCase		)
	if "encoder.cls_token" in key:
		__lowerCAmelCase      :						List[str]						      =				re.sub(r'encoder.cls_token'	,  'embeddings.class_embedding'	,  _UpperCamelCase		)

	if "self_attn" in key:
		__lowerCAmelCase      :						Optional[int]						      =				re.sub(r'self_attn.proj'	,  'self_attn.projection'	,  _UpperCamelCase		)

	return key
@torch.no_grad()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=None		):
	if config_path is not None:
		__lowerCAmelCase      :						Union[str, Any]						      =				BlipConfig.from_pretrained(_UpperCamelCase		)
	else:
		__lowerCAmelCase      :						Any						      =				BlipConfig(projection_dim=512	,  text_config={}	,  vision_config={}		)

	__lowerCAmelCase      :						Dict						      =				BlipForConditionalGeneration(_UpperCamelCase		).eval()

	__lowerCAmelCase      :						str						      =				'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

	__lowerCAmelCase      :						Optional[Any]						      =				blip_decoder(pretrained=_UpperCamelCase	,  image_size=384	,  vit='base'		)
	__lowerCAmelCase      :						Any						      =				pt_model.eval()

	__lowerCAmelCase      :						Dict						      =				pt_model.state_dict()
	for key in modified_state_dict.copy():
		__lowerCAmelCase      :						Any						      =				modified_state_dict.pop(_UpperCamelCase		)
		__lowerCAmelCase      :						Any						      =				rename_key(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				value

	hf_model.load_state_dict(_UpperCamelCase		)

	__lowerCAmelCase      :						int						      =				384
	__lowerCAmelCase      :						Any						      =				load_demo_image(image_size=_UpperCamelCase	,  device='cpu'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				BertTokenizer.from_pretrained('bert-base-uncased'		)
	__lowerCAmelCase      :						List[str]						      =				tokenizer(['a picture of']		).input_ids

	__lowerCAmelCase      :						Dict						      =				hf_model.generate(_UpperCamelCase	,  _UpperCamelCase		)

	assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

	__lowerCAmelCase      :						int						      =				hf_model.generate(_UpperCamelCase		)

	assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

	if pytorch_dump_folder_path is not None:
		hf_model.save_pretrained(_UpperCamelCase		)

	# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
	__lowerCAmelCase      :						Any						      =				(
	    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
	)

	__lowerCAmelCase      :						str						      =				blip_vqa(pretrained=_UpperCamelCase	,  image_size=_UpperCamelCase	,  vit='base'		)
	vqa_model.eval()

	__lowerCAmelCase      :						Optional[int]						      =				vqa_model.state_dict()
	for key in modified_state_dict.copy():
		__lowerCAmelCase      :						str						      =				modified_state_dict.pop(_UpperCamelCase		)
		__lowerCAmelCase      :						List[Any]						      =				rename_key(_UpperCamelCase		)
		__lowerCAmelCase      :						Union[str, Any]						      =				value

	__lowerCAmelCase      :						Union[str, Any]						      =				BlipForQuestionAnswering(_UpperCamelCase		)

	hf_vqa_model.load_state_dict(_UpperCamelCase		)

	__lowerCAmelCase      :						Tuple						      =				['How many dogs are in this image?']
	__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'		).input_ids

	__lowerCAmelCase      :						str						      =				hf_vqa_model.generate(_UpperCamelCase	,  _UpperCamelCase		)
	print(tokenizer.decode(answer[0]		)		)

	assert tokenizer.decode(answer[0]		) == "[UNK] 1 [SEP]"
	if pytorch_dump_folder_path is not None:
		hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'		)

	__lowerCAmelCase      :						Optional[int]						      =				'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

	__lowerCAmelCase      :						Union[str, Any]						      =				blip_itm(pretrained=_UpperCamelCase	,  image_size=_UpperCamelCase	,  vit='base'		)
	itm_model.eval()

	__lowerCAmelCase      :						List[Any]						      =				itm_model.state_dict()
	for key in modified_state_dict.copy():
		__lowerCAmelCase      :						Tuple						      =				modified_state_dict.pop(_UpperCamelCase		)
		__lowerCAmelCase      :						int						      =				rename_key(_UpperCamelCase		)
		__lowerCAmelCase      :						Any						      =				value

	__lowerCAmelCase      :						Any						      =				BlipForImageTextRetrieval(_UpperCamelCase		)

	__lowerCAmelCase      :						Union[str, Any]						      =				['A picture of a woman with a dog sitting in a beach']
	__lowerCAmelCase      :						Optional[Any]						      =				tokenizer(
	    _UpperCamelCase	,  return_tensors='pt'	,  padding='max_length'	,  truncation=_UpperCamelCase	,  max_length=35	,  ).input_ids

	hf_itm_model.load_state_dict(_UpperCamelCase		)
	hf_itm_model.eval()

	__lowerCAmelCase      :						Optional[Any]						      =				hf_itm_model(_UpperCamelCase	,  _UpperCamelCase	,  use_itm_head=_UpperCamelCase		)
	__lowerCAmelCase      :						int						      =				hf_itm_model(_UpperCamelCase	,  _UpperCamelCase	,  use_itm_head=_UpperCamelCase		)

	assert out[0].item() == 0.2110687494277954
	assert torch.nn.functional.softmax(out_itm[0]	,  dim=1		)[:, 1].item() == 0.45698845386505127

	if pytorch_dump_folder_path is not None:
		hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'		)


if __name__ == "__main__":
							lowerCamelCase__        =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCamelCase__        =		parser.parse_args()

							convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""cuda""" if torch.cuda.is_available() else """cpu"""
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = DEFAULT_DEVICE	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'		)
			fout.flush()
	fout.close()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,  4		)}
def    __lowerCAmelCase    ():
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)
def    __lowerCAmelCase    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser()
	parser.add_argument('model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'		)
	parser.add_argument('input_path'	,  type=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument('save_path'	,  type=_UpperCamelCase	,  help='where to save summaries'		)
	parser.add_argument('--reference_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  help='like cnn_dm/test.target'		)
	parser.add_argument('--score_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default='metrics.json'	,  help='where to save metrics'		)
	parser.add_argument('--device'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='cuda, cuda:1, cpu etc.'		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--task'	,  type=_UpperCamelCase	,  default='summarization'	,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'	,  type=_UpperCamelCase	,  default=8	,  required=_UpperCamelCase	,  help='batch size'		)
	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  help='How many observations. Defaults to all.'		)
	parser.add_argument('--fp16'	,  action='store_true'		)
	parser.add_argument('--dump-args'	,  action='store_true'	,  help='print the custom hparams with the results'		)
	parser.add_argument(
	    '--info'	,  nargs='?'	,  type=_UpperCamelCase	,  const=datetime_now()	,  help=(
	        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
	        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
	    )	,  )
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase      :						int						      =				examples[: args.n_obs]
	Path(args.save_path		).parent.mkdir(exist_ok=_UpperCamelCase		)

	if args.reference_path is None and Path(args.score_path		).exists():
		warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."		)

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError('Can\'t mix --fp16 and --device cpu'		)

	__lowerCAmelCase      :						Optional[Any]						      =				generate_summaries_or_translations(
	    _UpperCamelCase	,  args.save_path	,  args.model_name	,  batch_size=args.bs	,  device=args.device	,  fpaa=args.fpaa	,  task=args.task	,  prefix=args.prefix	,  **_UpperCamelCase	,  )

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowerCAmelCase      :						str						      =				calculate_bleu if 'translation' in args.task else calculate_rouge
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.save_path		).readlines()]
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(_UpperCamelCase		)]
	__lowerCAmelCase      :						dict						      =				score_fn(_UpperCamelCase	,  _UpperCamelCase		)
	scores.update(_UpperCamelCase		)

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				args.info

	if verbose:
		print(_UpperCamelCase		)

	if args.score_path is not None:
		json.dump(_UpperCamelCase	,  open(args.score_path	,  'w'		)		)

	return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)