






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_xlnet import XLNetTokenizer
else:
		A__			:    Dict         =				None


A__			:    int         =				logging.get_logger(__name__)

A__			:    int         =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__			:    str         =				{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

A__			:    Any         =				{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

A__			:    Optional[int]         =				'▁'

# Segments (not really needed)
A__			:    Tuple         =				0
A__			:    Tuple         =				1
A__			:    Dict         =				2
A__			:    Any         =				3
A__			:    Any         =				4
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= """left"""
				lowercase__       							= XLNetTokenizer




				def __init__(  self		:					Optional[Any],						lowerCamelCase		:					Tuple=None,						lowerCamelCase		:					Union[str, Any]=None,						lowerCamelCase		:					Tuple=False,						lowerCamelCase		:					List[Any]=True,						lowerCamelCase		:					Optional[int]=False,						lowerCamelCase		:					Optional[int]="<s>",						lowerCamelCase		:					Any="</s>",						lowerCamelCase		:					Dict="<unk>",						lowerCamelCase		:					Optional[Any]="<sep>",						lowerCamelCase		:					Optional[Any]="<pad>",						lowerCamelCase		:					List[Any]="<cls>",						lowerCamelCase		:					Optional[int]="<mask>",						lowerCamelCase		:					List[Any]=["<eop>", "<eod>"],						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase						) if isinstance(lowerCamelCase,						lowerCamelCase						) else mask_token

								super().__init__(
								    vocab_file=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,						additional_special_tokens=lowerCamelCase,						**lowerCamelCase,						)

								lowercase__     				=    3
								lowercase__     				=    do_lower_case
								lowercase__     				=    remove_space
								lowercase__     				=    keep_accents
								lowercase__     				=    vocab_file
								lowercase__     				=    False if not self.vocab_file else True




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None						):

								'''simple docstring'''
								lowercase__     				=    [self.sep_token_id]
								lowercase__     				=    [self.cls_token_id]
								if token_ids_a is None:
												return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None						):

								'''simple docstring'''
								lowercase__     				=    [self.sep_token_id]
								lowercase__     				=    [2]

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




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[str] = None						):

								'''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
								lowercase__     				=    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,)












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    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
				lowercase__     				=    Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_       ).raw       ).convert('''RGB'''       )

				lowercase__     				=    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)       ),
				    ]       )
				lowercase__     				=    transform(lowerCamelCase_       ).unsqueeze(0       ).to(lowerCamelCase_       )
				return image






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if "visual_encoder" in key:
								lowercase__     				=    re.sub('''visual_encoder*'''	,     '''vision_model.encoder'''	,     lowerCamelCase_       )
				if "blocks" in key:
								lowercase__     				=    re.sub(r'''blocks'''	,     '''layers'''	,     lowerCamelCase_       )
				if "attn" in key:
								lowercase__     				=    re.sub(r'''attn'''	,     '''self_attn'''	,     lowerCamelCase_       )
				if "norm1" in key:
								lowercase__     				=    re.sub(r'''norm1'''	,     '''layer_norm1'''	,     lowerCamelCase_       )
				if "norm2" in key:
								lowercase__     				=    re.sub(r'''norm2'''	,     '''layer_norm2'''	,     lowerCamelCase_       )
				if "encoder.norm" in key:
								lowercase__     				=    re.sub(r'''encoder.norm'''	,     '''post_layernorm'''	,     lowerCamelCase_       )
				if "encoder.patch_embed.proj" in key:
								lowercase__     				=    re.sub(r'''encoder.patch_embed.proj'''	,     '''embeddings.patch_embedding'''	,     lowerCamelCase_       )

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

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

				return key






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				if config_path is not None:
								lowercase__     				=    BlipConfig.from_pretrained(lowerCamelCase_       )
				else:
								lowercase__     				=    BlipConfig(projection_dim=512	,     text_config={}	,     vision_config={}       )

				lowercase__     				=    BlipForConditionalGeneration(lowerCamelCase_       ).eval()

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

				lowercase__     				=    blip_decoder(pretrained=lowerCamelCase_	,     image_size=384	,     vit='''base'''       )
				lowercase__     				=    pt_model.eval()

				lowercase__     				=    pt_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				hf_model.load_state_dict(lowerCamelCase_       )

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

				lowercase__     				=    hf_model.generate(lowerCamelCase_	,     lowerCamelCase_       )

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

				lowercase__     				=    hf_model.generate(lowerCamelCase_       )

				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(lowerCamelCase_       )

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

				lowercase__     				=    blip_vqa(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				vqa_model.eval()

				lowercase__     				=    vqa_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForQuestionAnswering(lowerCamelCase_       )

				hf_vqa_model.load_state_dict(lowerCamelCase_       )

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

				lowercase__     				=    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'''       )

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

				lowercase__     				=    blip_itm(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				itm_model.eval()

				lowercase__     				=    itm_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForImageTextRetrieval(lowerCamelCase_       )

				lowercase__     				=    ['''A picture of a woman with a dog sitting in a beach''']
				lowercase__     				=    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()

				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )
				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )

				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__			:    Optional[int]         =				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__			:    List[Any]         =				parser.parse_args()

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




