


'''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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
			UpperCAmelCase__					:   Any       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   ).convert("""RGB"""   )

			UpperCAmelCase__					:   Optional[int]       				=       transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)	, interpolation=InterpolationMode.BICUBIC   ),
			        transforms.ToTensor(),
			        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)	, (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)   ),
			    ]   )
			UpperCAmelCase__					:   List[Any]       				=       transform(UpperCamelCase__   ).unsqueeze(0   ).to(UpperCamelCase__   )
			return image







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

			if "encoder.pos_embed" in key:
						UpperCAmelCase__					:   Optional[Any]       				=       re.sub(R"""encoder.pos_embed"""	, """embeddings.position_embedding"""	, UpperCamelCase__   )
			if "encoder.cls_token" in key:
						UpperCAmelCase__					:   Optional[int]       				=       re.sub(R"""encoder.cls_token"""	, """embeddings.class_embedding"""	, UpperCamelCase__   )

			if "self_attn" in key:
						UpperCAmelCase__					:   Any       				=       re.sub(R"""self_attn.proj"""	, """self_attn.projection"""	, UpperCamelCase__   )

			return key







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=None   ):
			if config_path is not None:
						UpperCAmelCase__					:   List[Any]       				=       BlipConfig.from_pretrained(UpperCamelCase__   )
			else:
						UpperCAmelCase__					:   List[str]       				=       BlipConfig(projection_dim=5_1_2	, text_config={}	, vision_config={}   )

			UpperCAmelCase__					:   List[Any]       				=       BlipForConditionalGeneration(UpperCamelCase__   ).eval()

			UpperCAmelCase__					:   int       				=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

			UpperCAmelCase__					:   Any       				=       blip_decoder(pretrained=UpperCamelCase__	, image_size=3_8_4	, vit="""base"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       pt_model.eval()

			UpperCAmelCase__					:   Optional[Any]       				=       pt_model.state_dict()
			for key in modified_state_dict.copy():
						UpperCAmelCase__					:   int       				=       modified_state_dict.pop(UpperCamelCase__   )
						UpperCAmelCase__					:   Optional[Any]       				=       rename_key(UpperCamelCase__   )
						UpperCAmelCase__					:   Union[str, Any]       				=       value

			hf_model.load_state_dict(UpperCamelCase__   )

			UpperCAmelCase__					:   Any       				=       3_8_4
			UpperCAmelCase__					:   Any       				=       load_demo_image(image_size=UpperCamelCase__	, device="""cpu"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       BertTokenizer.from_pretrained("""bert-base-uncased"""   )
			UpperCAmelCase__					:   str       				=       tokenizer(["""a picture of"""]   ).input_ids

			UpperCAmelCase__					:   str       				=       hf_model.generate(UpperCamelCase__	, UpperCamelCase__   )

			assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

			UpperCAmelCase__					:   List[Any]       				=       hf_model.generate(UpperCamelCase__   )

			assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

			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'
			UpperCAmelCase__					:   Dict       				=       (
			    """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
			)

			UpperCAmelCase__					:   List[Any]       				=       blip_vqa(pretrained=UpperCamelCase__	, image_size=UpperCamelCase__	, vit="""base"""   )
			vqa_model.eval()

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

			UpperCAmelCase__					:   Dict       				=       BlipForQuestionAnswering(UpperCamelCase__   )

			hf_vqa_model.load_state_dict(UpperCamelCase__   )

			UpperCAmelCase__					:   Union[str, Any]       				=       ["""How many dogs are in this image?"""]
			UpperCAmelCase__					:   int       				=       tokenizer(UpperCamelCase__	, return_tensors="""pt"""   ).input_ids

			UpperCAmelCase__					:   Any       				=       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"""   )

			UpperCAmelCase__					:   Dict       				=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

			UpperCAmelCase__					:   Tuple       				=       blip_itm(pretrained=UpperCamelCase__	, image_size=UpperCamelCase__	, vit="""base"""   )
			itm_model.eval()

			UpperCAmelCase__					:   Union[str, Any]       				=       itm_model.state_dict()
			for key in modified_state_dict.copy():
						UpperCAmelCase__					:   Any       				=       modified_state_dict.pop(UpperCamelCase__   )
						UpperCAmelCase__					:   Dict       				=       rename_key(UpperCamelCase__   )
						UpperCAmelCase__					:   Dict       				=       value

			UpperCAmelCase__					:   Optional[int]       				=       BlipForImageTextRetrieval(UpperCamelCase__   )

			UpperCAmelCase__					:   Optional[Any]       				=       ["""A picture of a woman with a dog sitting in a beach"""]
			UpperCAmelCase__					:   Union[str, Any]       				=       tokenizer(
			    UpperCamelCase__	, return_tensors="""pt"""	, padding="""max_length"""	, truncation=UpperCamelCase__	, max_length=3_5	, ).input_ids

			hf_itm_model.load_state_dict(UpperCamelCase__   )
			hf_itm_model.eval()

			UpperCAmelCase__					:   int       				=       hf_itm_model(UpperCamelCase__	, UpperCamelCase__	, use_itm_head=UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       hf_itm_model(UpperCamelCase__	, UpperCamelCase__	, use_itm_head=UpperCamelCase__   )

			assert out[0].item() == 0.21_10_68_74_94_27_79_54
			assert torch.nn.functional.softmax(out_itm[0]	, dim=1   )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

			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_tokenizers_available, is_torch_available


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

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