

'''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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : List[str]    ):
  UpperCAmelCase    				=						'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
  UpperCAmelCase    				=						Image.open(requests.get(__UpperCAmelCase							, stream=__UpperCAmelCase    ).raw    ).convert('RGB'    )

  UpperCAmelCase    				=						transforms.Compose(
      [
          transforms.Resize((image_size, image_size)							, interpolation=InterpolationMode.BICUBIC    ),
          transforms.ToTensor(),
          transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)							, (0.2686_2954, 0.2613_0258, 0.2757_7711)    ),
      ]    )
  UpperCAmelCase    				=						transform(__UpperCAmelCase    ).unsqueeze(0    ).to(__UpperCAmelCase    )
  return image


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]    ):

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

  if "encoder.pos_embed" in key:
    UpperCAmelCase    				=						re.sub(R'encoder.pos_embed'							, 'embeddings.position_embedding'							, __UpperCAmelCase    )
  if "encoder.cls_token" in key:
    UpperCAmelCase    				=						re.sub(R'encoder.cls_token'							, 'embeddings.class_embedding'							, __UpperCAmelCase    )

  if "self_attn" in key:
    UpperCAmelCase    				=						re.sub(R'self_attn.proj'							, 'self_attn.projection'							, __UpperCAmelCase    )

  return key


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[Any]=None    ):

  if config_path is not None:
    UpperCAmelCase    				=						BlipConfig.from_pretrained(__UpperCAmelCase    )
  else:
    UpperCAmelCase    				=						BlipConfig(projection_dim=512							, text_config={}							, vision_config={}    )

  UpperCAmelCase    				=						BlipForConditionalGeneration(__UpperCAmelCase    ).eval()

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

  UpperCAmelCase    				=						blip_decoder(pretrained=__UpperCAmelCase							, image_size=384							, vit='base'    )
  UpperCAmelCase    				=						pt_model.eval()

  UpperCAmelCase    				=						pt_model.state_dict()
  for key in modified_state_dict.copy():
    UpperCAmelCase    				=						modified_state_dict.pop(__UpperCAmelCase    )
    UpperCAmelCase    				=						rename_key(__UpperCAmelCase    )
    UpperCAmelCase    				=						value

  hf_model.load_state_dict(__UpperCAmelCase    )

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

  UpperCAmelCase    				=						hf_model.generate(__UpperCAmelCase							, __UpperCAmelCase    )

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

  UpperCAmelCase    				=						hf_model.generate(__UpperCAmelCase    )

  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(__UpperCAmelCase    )

  # 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=__UpperCAmelCase							, image_size=__UpperCAmelCase							, vit='base'    )
  vqa_model.eval()

  UpperCAmelCase    				=						vqa_model.state_dict()
  for key in modified_state_dict.copy():
    UpperCAmelCase    				=						modified_state_dict.pop(__UpperCAmelCase    )
    UpperCAmelCase    				=						rename_key(__UpperCAmelCase    )
    UpperCAmelCase    				=						value

  UpperCAmelCase    				=						BlipForQuestionAnswering(__UpperCAmelCase    )

  hf_vqa_model.load_state_dict(__UpperCAmelCase    )

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

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

  UpperCAmelCase    				=						blip_itm(pretrained=__UpperCAmelCase							, image_size=__UpperCAmelCase							, vit='base'    )
  itm_model.eval()

  UpperCAmelCase    				=						itm_model.state_dict()
  for key in modified_state_dict.copy():
    UpperCAmelCase    				=						modified_state_dict.pop(__UpperCAmelCase    )
    UpperCAmelCase    				=						rename_key(__UpperCAmelCase    )
    UpperCAmelCase    				=						value

  UpperCAmelCase    				=						BlipForImageTextRetrieval(__UpperCAmelCase    )

  UpperCAmelCase    				=						['A picture of a woman with a dog sitting in a beach']
  UpperCAmelCase    				=						tokenizer(
      __UpperCAmelCase							, return_tensors='pt'							, padding='max_length'							, truncation=__UpperCAmelCase							, max_length=35							, ).input_ids

  hf_itm_model.load_state_dict(__UpperCAmelCase    )
  hf_itm_model.eval()

  UpperCAmelCase    				=						hf_itm_model(__UpperCAmelCase							, __UpperCAmelCase							, use_itm_head=__UpperCAmelCase    )
  UpperCAmelCase    				=						hf_itm_model(__UpperCAmelCase							, __UpperCAmelCase							, use_itm_head=__UpperCAmelCase    )

  assert out[0].item() == 0.2110_6874_9427_7954
  assert torch.nn.functional.softmax(out_itm[0]							, dim=1    )[:, 1].item() == 0.4_5698_8453_8650_5127

  if pytorch_dump_folder_path is not None:
    hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'    )


if __name__ == "__main__":
  _a					:			Tuple							=    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					:			Tuple							=    parser.parse_args()

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








'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

														self.builder.download_and_prepare(
														    download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
														snake_case_		     =							self.builder.as_dataset(
														    split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory   )
									return dataset







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0			,							len(self.dataset   )			,							self.batch_size   )			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

