
"""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    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  int  ):
	A     = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
	A     = Image.open(requests.get(snake_case__						,   stream=snake_case__  ).raw  ).convert('RGB'  )

	A     = 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)  ),
	    ]  )
	A     = transform(snake_case__  ).unsqueeze(0  ).to(snake_case__  )
	return image






def    _snake_case					(		snake_case__ :  Optional[Any]  ):
	if "visual_encoder" in key:
		A     = re.sub('visual_encoder*'						,   'vision_model.encoder'						,   snake_case__  )
	if "blocks" in key:
		A     = re.sub(r'blocks'						,   'layers'						,   snake_case__  )
	if "attn" in key:
		A     = re.sub(r'attn'						,   'self_attn'						,   snake_case__  )
	if "norm1" in key:
		A     = re.sub(r'norm1'						,   'layer_norm1'						,   snake_case__  )
	if "norm2" in key:
		A     = re.sub(r'norm2'						,   'layer_norm2'						,   snake_case__  )
	if "encoder.norm" in key:
		A     = re.sub(r'encoder.norm'						,   'post_layernorm'						,   snake_case__  )
	if "encoder.patch_embed.proj" in key:
		A     = re.sub(r'encoder.patch_embed.proj'						,   'embeddings.patch_embedding'						,   snake_case__  )

	if "encoder.pos_embed" in key:
		A     = re.sub(r'encoder.pos_embed'						,   'embeddings.position_embedding'						,   snake_case__  )
	if "encoder.cls_token" in key:
		A     = re.sub(r'encoder.cls_token'						,   'embeddings.class_embedding'						,   snake_case__  )

	if "self_attn" in key:
		A     = re.sub(r'self_attn.proj'						,   'self_attn.projection'						,   snake_case__  )

	return key






@torch.no_grad()
def    _snake_case					(		snake_case__ :  Any						,   snake_case__ :  str=None  ):
	if config_path is not None:
		A     = BlipConfig.from_pretrained(snake_case__  )
	else:
		A     = BlipConfig(projection_dim=512						,   text_config={}						,   vision_config={}  )

	A     = BlipForConditionalGeneration(snake_case__  ).eval()

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

	A     = blip_decoder(pretrained=snake_case__						,   image_size=384						,   vit='base'  )
	A     = pt_model.eval()

	A     = pt_model.state_dict()
	for key in modified_state_dict.copy():
		A     = modified_state_dict.pop(snake_case__  )
		A     = rename_key(snake_case__  )
		A     = value

	hf_model.load_state_dict(snake_case__  )

	A     = 384
	A     = load_demo_image(image_size=snake_case__						,   device='cpu'  )
	A     = BertTokenizer.from_pretrained('bert-base-uncased'  )
	A     = tokenizer(['a picture of']  ).input_ids

	A     = hf_model.generate(snake_case__						,   snake_case__  )

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

	A     = hf_model.generate(snake_case__  )

	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(snake_case__  )

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

	A     = blip_vqa(pretrained=snake_case__						,   image_size=snake_case__						,   vit='base'  )
	vqa_model.eval()

	A     = vqa_model.state_dict()
	for key in modified_state_dict.copy():
		A     = modified_state_dict.pop(snake_case__  )
		A     = rename_key(snake_case__  )
		A     = value

	A     = BlipForQuestionAnswering(snake_case__  )

	hf_vqa_model.load_state_dict(snake_case__  )

	A     = ['How many dogs are in this image?']
	A     = tokenizer(snake_case__						,   return_tensors='pt'  ).input_ids

	A     = hf_vqa_model.generate(snake_case__						,   snake_case__  )
	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'  )

	A     = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

	A     = blip_itm(pretrained=snake_case__						,   image_size=snake_case__						,   vit='base'  )
	itm_model.eval()

	A     = itm_model.state_dict()
	for key in modified_state_dict.copy():
		A     = modified_state_dict.pop(snake_case__  )
		A     = rename_key(snake_case__  )
		A     = value

	A     = BlipForImageTextRetrieval(snake_case__  )

	A     = ['A picture of a woman with a dog sitting in a beach']
	A     = tokenizer(
	    snake_case__						,   return_tensors='pt'						,   padding='max_length'						,   truncation=snake_case__						,   max_length=35						,   ).input_ids

	hf_itm_model.load_state_dict(snake_case__  )
	hf_itm_model.eval()

	A     = hf_itm_model(snake_case__						,   snake_case__						,   use_itm_head=snake_case__  )
	A     = hf_itm_model(snake_case__						,   snake_case__						,   use_itm_head=snake_case__  )

	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__":
		_lowercase				 =					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''')
		_lowercase				 =					parser.parse_args()

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






"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)