
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						(    snake_case				:List[str]    , snake_case				:List[Any]		)				->     int:
 __UpperCamelCase      =	'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
 __UpperCamelCase      =	Image.open(requests.get(snake_case    , stream=snake_case		).raw		).convert('RGB'		)

 __UpperCamelCase      =	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)		),
     ]		)
 __UpperCamelCase      =	transform(snake_case		).unsqueeze(0		).to(snake_case		)
 return image






def 							A						(    snake_case				:List[Any]		)				->     List[str]:
 if "visual_encoder" in key:
  __UpperCamelCase      =	re.sub('visual_encoder*'    , 'vision_model.encoder'    , snake_case		)
 if "blocks" in key:
  __UpperCamelCase      =	re.sub(r'blocks'    , 'layers'    , snake_case		)
 if "attn" in key:
  __UpperCamelCase      =	re.sub(r'attn'    , 'self_attn'    , snake_case		)
 if "norm1" in key:
  __UpperCamelCase      =	re.sub(r'norm1'    , 'layer_norm1'    , snake_case		)
 if "norm2" in key:
  __UpperCamelCase      =	re.sub(r'norm2'    , 'layer_norm2'    , snake_case		)
 if "encoder.norm" in key:
  __UpperCamelCase      =	re.sub(r'encoder.norm'    , 'post_layernorm'    , snake_case		)
 if "encoder.patch_embed.proj" in key:
  __UpperCamelCase      =	re.sub(r'encoder.patch_embed.proj'    , 'embeddings.patch_embedding'    , snake_case		)

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

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

 return key






@torch.no_grad()
def 							A						(    snake_case				:Any    , snake_case				:Tuple=None		)				->     Optional[Any]:
 if config_path is not None:
  __UpperCamelCase      =	BlipConfig.from_pretrained(snake_case		)
 else:
  __UpperCamelCase      =	BlipConfig(projection_dim=5_1_2    , text_config={}    , vision_config={}		)

 __UpperCamelCase      =	BlipForConditionalGeneration(snake_case		).eval()

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

 __UpperCamelCase      =	blip_decoder(pretrained=snake_case    , image_size=3_8_4    , vit='base'		)
 __UpperCamelCase      =	pt_model.eval()

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

 hf_model.load_state_dict(snake_case		)

 __UpperCamelCase      =	3_8_4
 __UpperCamelCase      =	load_demo_image(image_size=snake_case    , device='cpu'		)
 __UpperCamelCase      =	BertTokenizer.from_pretrained('bert-base-uncased'		)
 __UpperCamelCase      =	tokenizer(['a picture of']		).input_ids

 __UpperCamelCase      =	hf_model.generate(snake_case    , snake_case		)

 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      =	hf_model.generate(snake_case		)

 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(snake_case		)

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

 __UpperCamelCase      =	blip_vqa(pretrained=snake_case    , image_size=snake_case    , vit='base'		)
 vqa_model.eval()

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

 __UpperCamelCase      =	BlipForQuestionAnswering(snake_case		)

 hf_vqa_model.load_state_dict(snake_case		)

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

 __UpperCamelCase      =	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'		)

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

 __UpperCamelCase      =	blip_itm(pretrained=snake_case    , image_size=snake_case    , vit='base'		)
 itm_model.eval()

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

 __UpperCamelCase      =	BlipForImageTextRetrieval(snake_case		)

 __UpperCamelCase      =	['A picture of a woman with a dog sitting in a beach']
 __UpperCamelCase      =	tokenizer(
     snake_case    , return_tensors='pt'    , padding='max_length'    , truncation=snake_case    , max_length=3_5    , ).input_ids

 hf_itm_model.load_state_dict(snake_case		)
 hf_itm_model.eval()

 __UpperCamelCase      =	hf_itm_model(snake_case    , snake_case    , use_itm_head=snake_case		)
 __UpperCamelCase      =	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__":
      UpperCamelCase     :    Dict       =			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")
      UpperCamelCase     :    Dict       =			parser.parse_args()

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





"""simple docstring"""

import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
      if is_torch_available():
            import torch
      if is_tf_available():
            import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    List[str]       =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase     :    List[Any]       =			{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase     :    List[Any]       =			{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						CodeGenTokenizer





     def __init__(     self     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase="<|endoftext|>"     ,					__UpperCAmelCase=False     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase     ,					__UpperCAmelCase     ,					tokenizer_file=__UpperCAmelCase     ,					unk_token=__UpperCAmelCase     ,					bos_token=__UpperCAmelCase     ,					eos_token=__UpperCAmelCase     ,					add_prefix_space=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      if kwargs.pop('add_bos_token'     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	kwargs.pop('name_or_path'     ,					''				)
       raise ValueError(
           'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
           'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
           F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
           F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
           'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
           ' so that the fast tokenizer works correctly.'				)

      __UpperCamelCase      =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
      if pre_tok_state.get('add_prefix_space'     ,					__UpperCAmelCase				) != add_prefix_space:
       __UpperCamelCase      =	getattr(__UpperCAmelCase     ,					pre_tok_state.pop('type'				)				)
       __UpperCamelCase      =	add_prefix_space
       __UpperCamelCase      =	pre_tok_class(**__UpperCAmelCase				)

      __UpperCamelCase      =	add_prefix_space





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	kwargs.get('is_split_into_words'     ,					__UpperCAmelCase				)
      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	kwargs.get('is_split_into_words'     ,					__UpperCAmelCase				)

      assert self.add_prefix_space or not is_split_into_words, (
          F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self._tokenizer.model.save(__UpperCAmelCase     ,					name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	super().decode(
          token_ids=__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase     ,					clean_up_tokenization_spaces=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)

      if truncate_before_pattern is not None and len(__UpperCAmelCase				) > 0:
       __UpperCamelCase      =	self.truncate(__UpperCAmelCase     ,					__UpperCAmelCase				)

      return decoded_text







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      def find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	pattern.search(__UpperCAmelCase     ,					__UpperCAmelCase				)
       return m.start() if m else -1

      __UpperCamelCase      =	[re.compile(__UpperCAmelCase     ,					re.MULTILINE				) for pattern in truncate_before_pattern]

      __UpperCamelCase      =	list(re.finditer('^print'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: prints[1].start()]

      __UpperCamelCase      =	list(re.finditer('^def'     ,					__UpperCAmelCase     ,					re.MULTILINE				)				)

      if len(__UpperCAmelCase				) > 1:
       __UpperCamelCase      =	completion[: defs[1].start()]

      __UpperCamelCase      =	0

      __UpperCamelCase      =	[
          pos for pos in [find_re(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				) for terminal in terminals] if pos != -1
      ]

      if len(__UpperCAmelCase				) > 0:
       return completion[: min(__UpperCAmelCase				)]
      else:
       return completion



