

"""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     __A      (			a_						:Any   ,       a_						:List[Any])		->		Dict:
				__a :						List[str]			       =  '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
				__a :						List[str]			       =  Image.open(requests.get(lowerCamelCase_   ,       stream=lowerCamelCase_).raw).convert('''RGB''')

				__a :						List[str]			       =  transforms.Compose(
				    [
				        transforms.Resize((image_size, image_size)   ,       interpolation=InterpolationMode.BICUBIC),
				        transforms.ToTensor(),
				        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)   ,       (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)),
				    ])
				__a :						List[Any]			       =  transform(lowerCamelCase_).unsqueeze(0).to(lowerCamelCase_)
				return image

def     __A      (			a_						:str)		->		Union[str, Any]:

				if "visual_encoder" in key:
								__a :						int			       =  re.sub('''visual_encoder*'''   ,       '''vision_model.encoder'''   ,       lowerCamelCase_)
				if "blocks" in key:
								__a :						Optional[Any]			       =  re.sub(R'''blocks'''   ,       '''layers'''   ,       lowerCamelCase_)
				if "attn" in key:
								__a :						Tuple			       =  re.sub(R'''attn'''   ,       '''self_attn'''   ,       lowerCamelCase_)
				if "norm1" in key:
								__a :						Optional[Any]			       =  re.sub(R'''norm1'''   ,       '''layer_norm1'''   ,       lowerCamelCase_)
				if "norm2" in key:
								__a :						Optional[Any]			       =  re.sub(R'''norm2'''   ,       '''layer_norm2'''   ,       lowerCamelCase_)
				if "encoder.norm" in key:
								__a :						Dict			       =  re.sub(R'''encoder.norm'''   ,       '''post_layernorm'''   ,       lowerCamelCase_)
				if "encoder.patch_embed.proj" in key:
								__a :						int			       =  re.sub(R'''encoder.patch_embed.proj'''   ,       '''embeddings.patch_embedding'''   ,       lowerCamelCase_)

				if "encoder.pos_embed" in key:
								__a :						Optional[int]			       =  re.sub(R'''encoder.pos_embed'''   ,       '''embeddings.position_embedding'''   ,       lowerCamelCase_)
				if "encoder.cls_token" in key:
								__a :						List[str]			       =  re.sub(R'''encoder.cls_token'''   ,       '''embeddings.class_embedding'''   ,       lowerCamelCase_)

				if "self_attn" in key:
								__a :						int			       =  re.sub(R'''self_attn.proj'''   ,       '''self_attn.projection'''   ,       lowerCamelCase_)

				return key

@torch.no_grad()
def     __A      (			a_						:Optional[Any]   ,       a_						:Dict=None)		->		Optional[int]:

				if config_path is not None:
								__a :						Union[str, Any]			       =  BlipConfig.from_pretrained(lowerCamelCase_)
				else:
								__a :						Dict			       =  BlipConfig(projection_dim=5_12   ,       text_config={}   ,       vision_config={})

				__a :						Optional[Any]			       =  BlipForConditionalGeneration(lowerCamelCase_).eval()

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

				__a :						int			       =  blip_decoder(pretrained=lowerCamelCase_   ,       image_size=3_84   ,       vit='''base''')
				__a :						List[str]			       =  pt_model.eval()

				__a :						str			       =  pt_model.state_dict()
				for key in modified_state_dict.copy():
								__a :						int			       =  modified_state_dict.pop(lowerCamelCase_)
								__a :						List[Any]			       =  rename_key(lowerCamelCase_)
								__a :						Tuple			       =  value

				hf_model.load_state_dict(lowerCamelCase_)

				__a :						Any			       =  3_84
				__a :						List[Any]			       =  load_demo_image(image_size=lowerCamelCase_   ,       device='''cpu''')
				__a :						Tuple			       =  BertTokenizer.from_pretrained('''bert-base-uncased''')
				__a :						Union[str, Any]			       =  tokenizer(['''a picture of''']).input_ids

				__a :						int			       =  hf_model.generate(lowerCamelCase_   ,       lowerCamelCase_)

				assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

				__a :						Optional[int]			       =  hf_model.generate(lowerCamelCase_)

				assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

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

				__a :						str			       =  blip_vqa(pretrained=lowerCamelCase_   ,       image_size=lowerCamelCase_   ,       vit='''base''')
				vqa_model.eval()

				__a :						Any			       =  vqa_model.state_dict()
				for key in modified_state_dict.copy():
								__a :						List[str]			       =  modified_state_dict.pop(lowerCamelCase_)
								__a :						Any			       =  rename_key(lowerCamelCase_)
								__a :						Union[str, Any]			       =  value

				__a :						Any			       =  BlipForQuestionAnswering(lowerCamelCase_)

				hf_vqa_model.load_state_dict(lowerCamelCase_)

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

				__a :						List[Any]			       =  hf_vqa_model.generate(lowerCamelCase_   ,       lowerCamelCase_)
				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 :						Any			       =  '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

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

				__a :						Union[str, Any]			       =  itm_model.state_dict()
				for key in modified_state_dict.copy():
								__a :						Any			       =  modified_state_dict.pop(lowerCamelCase_)
								__a :						List[Any]			       =  rename_key(lowerCamelCase_)
								__a :						List[str]			       =  value

				__a :						List[Any]			       =  BlipForImageTextRetrieval(lowerCamelCase_)

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

				hf_itm_model.load_state_dict(lowerCamelCase_)
				hf_itm_model.eval()

				__a :						Dict			       =  hf_itm_model(lowerCamelCase_   ,       lowerCamelCase_   ,       use_itm_head=lowerCamelCase_)
				__a :						Optional[int]			       =  hf_itm_model(lowerCamelCase_   ,       lowerCamelCase_   ,       use_itm_head=lowerCamelCase_)

				assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
				assert torch.nn.functional.softmax(out_itm[0]   ,       dim=1)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
		A 			=  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''')
		A 			=  parser.parse_args()

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A 			=  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A 			=  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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