



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				(      snake_case	,						snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
					_lowerCAmelCase						=    Image.open(requests.get(snake_case	,						stream=snake_case			).raw			).convert("""RGB"""			)

					_lowerCAmelCase						=    transforms.Compose(
					    [
					        transforms.Resize((image_size, image_size)	,						interpolation=InterpolationMode.BICUBIC			),
					        transforms.ToTensor(),
					        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)	,						(0.26_862_954, 0.26_130_258, 0.27_577_711)			),
					    ]			)
					_lowerCAmelCase						=    transform(snake_case			).unsqueeze(0			).to(snake_case			)
					return image


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					if "visual_encoder" in key:
										_lowerCAmelCase						=    re.sub("""visual_encoder*"""	,						"""vision_model.encoder"""	,						snake_case			)
					if "blocks" in key:
										_lowerCAmelCase						=    re.sub(R"""blocks"""	,						"""layers"""	,						snake_case			)
					if "attn" in key:
										_lowerCAmelCase						=    re.sub(R"""attn"""	,						"""self_attn"""	,						snake_case			)
					if "norm1" in key:
										_lowerCAmelCase						=    re.sub(R"""norm1"""	,						"""layer_norm1"""	,						snake_case			)
					if "norm2" in key:
										_lowerCAmelCase						=    re.sub(R"""norm2"""	,						"""layer_norm2"""	,						snake_case			)
					if "encoder.norm" in key:
										_lowerCAmelCase						=    re.sub(R"""encoder.norm"""	,						"""post_layernorm"""	,						snake_case			)
					if "encoder.patch_embed.proj" in key:
										_lowerCAmelCase						=    re.sub(R"""encoder.patch_embed.proj"""	,						"""embeddings.patch_embedding"""	,						snake_case			)

					if "encoder.pos_embed" in key:
										_lowerCAmelCase						=    re.sub(R"""encoder.pos_embed"""	,						"""embeddings.position_embedding"""	,						snake_case			)
					if "encoder.cls_token" in key:
										_lowerCAmelCase						=    re.sub(R"""encoder.cls_token"""	,						"""embeddings.class_embedding"""	,						snake_case			)

					if "self_attn" in key:
										_lowerCAmelCase						=    re.sub(R"""self_attn.proj"""	,						"""self_attn.projection"""	,						snake_case			)

					return key


@torch.no_grad()
def 		_UpperCAmelCase				(      snake_case	,						snake_case=None			):





					"""simple docstring"""

					if config_path is not None:
										_lowerCAmelCase						=    BlipConfig.from_pretrained(snake_case			)
					else:
										_lowerCAmelCase						=    BlipConfig(projection_dim=5_12	,						text_config={}	,						vision_config={}			)

					_lowerCAmelCase						=    BlipForConditionalGeneration(snake_case			).eval()

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

					_lowerCAmelCase						=    blip_decoder(pretrained=snake_case	,						image_size=3_84	,						vit="""base"""			)
					_lowerCAmelCase						=    pt_model.eval()

					_lowerCAmelCase						=    pt_model.state_dict()
					for key in modified_state_dict.copy():
										_lowerCAmelCase						=    modified_state_dict.pop(snake_case			)
										_lowerCAmelCase						=    rename_key(snake_case			)
										_lowerCAmelCase						=    value

					hf_model.load_state_dict(snake_case			)

					_lowerCAmelCase						=    3_84
					_lowerCAmelCase						=    load_demo_image(image_size=snake_case	,						device="""cpu"""			)
					_lowerCAmelCase						=    BertTokenizer.from_pretrained("""bert-base-uncased"""			)
					_lowerCAmelCase						=    tokenizer(["""a picture of"""]			).input_ids

					_lowerCAmelCase						=    hf_model.generate(snake_case	,						snake_case			)

					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]

					_lowerCAmelCase						=    hf_model.generate(snake_case			)

					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(snake_case			)

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

					_lowerCAmelCase						=    blip_vqa(pretrained=snake_case	,						image_size=snake_case	,						vit="""base"""			)
					vqa_model.eval()

					_lowerCAmelCase						=    vqa_model.state_dict()
					for key in modified_state_dict.copy():
										_lowerCAmelCase						=    modified_state_dict.pop(snake_case			)
										_lowerCAmelCase						=    rename_key(snake_case			)
										_lowerCAmelCase						=    value

					_lowerCAmelCase						=    BlipForQuestionAnswering(snake_case			)

					hf_vqa_model.load_state_dict(snake_case			)

					_lowerCAmelCase						=    ["""How many dogs are in this image?"""]
					_lowerCAmelCase						=    tokenizer(snake_case	,						return_tensors="""pt"""			).input_ids

					_lowerCAmelCase						=    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"""			)

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

					_lowerCAmelCase						=    blip_itm(pretrained=snake_case	,						image_size=snake_case	,						vit="""base"""			)
					itm_model.eval()

					_lowerCAmelCase						=    itm_model.state_dict()
					for key in modified_state_dict.copy():
										_lowerCAmelCase						=    modified_state_dict.pop(snake_case			)
										_lowerCAmelCase						=    rename_key(snake_case			)
										_lowerCAmelCase						=    value

					_lowerCAmelCase						=    BlipForImageTextRetrieval(snake_case			)

					_lowerCAmelCase						=    ["""A picture of a woman with a dog sitting in a beach"""]
					_lowerCAmelCase						=    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()

					_lowerCAmelCase						=    hf_itm_model(snake_case	,						snake_case	,						use_itm_head=snake_case			)
					_lowerCAmelCase						=    hf_itm_model(snake_case	,						snake_case	,						use_itm_head=snake_case			)

					assert out[0].item() == 0.2_110_687_494_277_954
					assert torch.nn.functional.softmax(out_itm[0]	,						dim=1			)[:, 1].item() == 0.45_698_845_386_505_127

					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)










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_albert import AlbertTokenizer
else:
					A    :					Tuple =      None

A    :					Optional[Any] =      logging.get_logger(__name__)
A    :					Tuple =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A    :					List[str] =      {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

A    :					List[str] =      {
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

A    :					Optional[int] =      '''▁'''


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       List[Any]							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       Dict							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       List[str]							=    AlbertTokenizer






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    List[str]=None							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    str=False							,	__lowerCAmelCase   :    Union[str, Any]="[CLS]"							,	__lowerCAmelCase   :    int="[SEP]"							,	__lowerCAmelCase   :    Dict="<unk>"							,	__lowerCAmelCase   :    Dict="[SEP]"							,	__lowerCAmelCase   :    Union[str, Any]="<pad>"							,	__lowerCAmelCase   :    str="[CLS]"							,	__lowerCAmelCase   :    int="[MASK]"							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Optional[Any]:







					"""simple docstring"""






					A__        =   (
					    AddedToken(__lowerCAmelCase							,	lstrip=__lowerCAmelCase							,	rstrip=__lowerCAmelCase							,	normalized=__lowerCAmelCase   )
					    if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   )
					    else mask_token
					)

					super().__init__(
					    __lowerCAmelCase							,	tokenizer_file=__lowerCAmelCase							,	do_lower_case=__lowerCAmelCase							,	remove_space=__lowerCAmelCase							,	keep_accents=__lowerCAmelCase							,	bos_token=__lowerCAmelCase							,	eos_token=__lowerCAmelCase							,	unk_token=__lowerCAmelCase							,	sep_token=__lowerCAmelCase							,	pad_token=__lowerCAmelCase							,	cls_token=__lowerCAmelCase							,	mask_token=__lowerCAmelCase							,	**__lowerCAmelCase							,	)

					A__        =   do_lower_case
					A__        =   remove_space
					A__        =   keep_accents
					A__        =   vocab_file
					A__        =   False if not self.vocab_file else True






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [self.cls_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[str] = None   )	->	Tuple[str]:







					"""simple docstring"""






					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""   )

					if not os.path.isdir(__lowerCAmelCase   ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
							return
					A__        =   os.path.join(
					    __lowerCAmelCase							,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

					if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowerCAmelCase   ):
							copyfile(self.vocab_file							,	__lowerCAmelCase   )

					return (out_vocab_file,)



