


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__	(      a	:   Optional[Any]	,  a	:   Optional[int]				)    ->	List[str]:





			"""simple docstring"""
			a__ :int						  =    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
			a__ :Any						  =    Image.open(requests.get(lowercase_	,  stream=lowercase_				).raw				).convert("RGB"				)

			a__ :Dict						  =    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__ :int						  =    transform(lowercase_				).unsqueeze(0				).to(lowercase_				)
			return image


def 				lowerCamelCase__	(      a	:   str				)    ->	str:





			"""simple docstring"""

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

			if "encoder.pos_embed" in key:
						a__ :List[Any]						  =    re.sub(r"encoder.pos_embed"	,  "embeddings.position_embedding"	,  lowercase_				)
			if "encoder.cls_token" in key:
						a__ :int						  =    re.sub(r"encoder.cls_token"	,  "embeddings.class_embedding"	,  lowercase_				)

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

			return key


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[Any]=None				)    ->	Tuple:





			"""simple docstring"""

			if config_path is not None:
						a__ :Dict						  =    BlipConfig.from_pretrained(lowercase_				)
			else:
						a__ :List[str]						  =    BlipConfig(projection_dim=512	,  text_config={}	,  vision_config={}				)

			a__ :Optional[int]						  =    BlipForConditionalGeneration(lowercase_				).eval()

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

			a__ :Union[str, Any]						  =    blip_decoder(pretrained=lowercase_	,  image_size=384	,  vit="base"				)
			a__ :Union[str, Any]						  =    pt_model.eval()

			a__ :Optional[Any]						  =    pt_model.state_dict()
			for key in modified_state_dict.copy():
						a__ :List[Any]						  =    modified_state_dict.pop(lowercase_				)
						a__ :List[Any]						  =    rename_key(lowercase_				)
						a__ :Any						  =    value

			hf_model.load_state_dict(lowercase_				)

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

			a__ :Optional[int]						  =    hf_model.generate(lowercase_	,  lowercase_				)

			assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

			a__ :List[str]						  =    hf_model.generate(lowercase_				)

			assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

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

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

			a__ :List[str]						  =    blip_vqa(pretrained=lowercase_	,  image_size=lowercase_	,  vit="base"				)
			vqa_model.eval()

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

			a__ :List[Any]						  =    BlipForQuestionAnswering(lowercase_				)

			hf_vqa_model.load_state_dict(lowercase_				)

			a__ :Any						  =    ["How many dogs are in this image?"]
			a__ :int						  =    tokenizer(lowercase_	,  return_tensors="pt"				).input_ids

			a__ :Tuple						  =    hf_vqa_model.generate(lowercase_	,  lowercase_				)
			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__ :List[Any]						  =    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

			a__ :Optional[Any]						  =    blip_itm(pretrained=lowercase_	,  image_size=lowercase_	,  vit="base"				)
			itm_model.eval()

			a__ :str						  =    itm_model.state_dict()
			for key in modified_state_dict.copy():
						a__ :Optional[Any]						  =    modified_state_dict.pop(lowercase_				)
						a__ :Tuple						  =    rename_key(lowercase_				)
						a__ :List[str]						  =    value

			a__ :Union[str, Any]						  =    BlipForImageTextRetrieval(lowercase_				)

			a__ :Dict						  =    ["A picture of a woman with a dog sitting in a beach"]
			a__ :List[Any]						  =    tokenizer(
			    lowercase_	,  return_tensors="pt"	,  padding="max_length"	,  truncation=lowercase_	,  max_length=35	,  ).input_ids

			hf_itm_model.load_state_dict(lowercase_				)
			hf_itm_model.eval()

			a__ :Optional[Any]						  =    hf_itm_model(lowercase_	,  lowercase_	,  use_itm_head=lowercase_				)
			a__ :Any						  =    hf_itm_model(lowercase_	,  lowercase_	,  use_itm_head=lowercase_				)

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

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'speech_to_text_2'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]=10000   ,     __A						:   Tuple=6   ,     __A						:   Tuple=2048   ,     __A						:   Any=4   ,     __A						:   str=0.0   ,     __A						:   Tuple=True   ,     __A						:   List[Any]="relu"   ,     __A						:   Union[str, Any]=256   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]=0.0   ,     __A						:   Tuple=0.0   ,     __A						:   Any=0.02   ,     __A						:   int=2   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple=1   ,     __A						:   List[str]=0   ,     __A						:   List[str]=2   ,     __A						:   Union[str, Any]=1024   ,     **__A						:   Tuple   ,     )      ->List[str]:






									"""simple docstring"""






									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    d_model
									a__ :Optional[int]						  =    decoder_ffn_dim
									a__ :Optional[int]						  =    decoder_layers
									a__ :Optional[int]						  =    decoder_attention_heads
									a__ :Dict						  =    dropout
									a__ :Optional[Any]						  =    attention_dropout
									a__ :Any						  =    activation_dropout
									a__ :int						  =    activation_function
									a__ :Optional[int]						  =    init_std
									a__ :List[Any]						  =    decoder_layerdrop
									a__ :Optional[Any]						  =    use_cache
									a__ :List[str]						  =    decoder_layers
									a__ :int						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									a__ :Optional[int]						  =    max_target_positions

									super().__init__(
									    pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     decoder_start_token_id=__A   ,     **__A   ,     )


