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

 lowercase__    :   int						= 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)					),
     ]					)
 lowercase__    :   Optional[int]						= transform(UpperCAmelCase					).unsqueeze(0					).to(UpperCAmelCase					)
 return image
def       __UpperCamelCase	(       UpperCAmelCase					):
 if "visual_encoder" in key:
  lowercase__    :   Optional[int]						= re.sub('''visual_encoder*'''		,					'''vision_model.encoder'''		,					UpperCAmelCase					)
 if "blocks" in key:
  lowercase__    :   List[str]						= re.sub(r'''blocks'''		,					'''layers'''		,					UpperCAmelCase					)
 if "attn" in key:
  lowercase__    :   Optional[Any]						= re.sub(r'''attn'''		,					'''self_attn'''		,					UpperCAmelCase					)
 if "norm1" in key:
  lowercase__    :   List[Any]						= re.sub(r'''norm1'''		,					'''layer_norm1'''		,					UpperCAmelCase					)
 if "norm2" in key:
  lowercase__    :   Optional[int]						= re.sub(r'''norm2'''		,					'''layer_norm2'''		,					UpperCAmelCase					)
 if "encoder.norm" in key:
  lowercase__    :   Dict						= re.sub(r'''encoder.norm'''		,					'''post_layernorm'''		,					UpperCAmelCase					)
 if "encoder.patch_embed.proj" in key:
  lowercase__    :   str						= re.sub(r'''encoder.patch_embed.proj'''		,					'''embeddings.patch_embedding'''		,					UpperCAmelCase					)

 if "encoder.pos_embed" in key:
  lowercase__    :   str						= re.sub(r'''encoder.pos_embed'''		,					'''embeddings.position_embedding'''		,					UpperCAmelCase					)
 if "encoder.cls_token" in key:
  lowercase__    :   Union[str, Any]						= re.sub(r'''encoder.cls_token'''		,					'''embeddings.class_embedding'''		,					UpperCAmelCase					)

 if "self_attn" in key:
  lowercase__    :   Union[str, Any]						= re.sub(r'''self_attn.proj'''		,					'''self_attn.projection'''		,					UpperCAmelCase					)

 return key
@torch.no_grad()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=None					):
 if config_path is not None:
  lowercase__    :   int						= BlipConfig.from_pretrained(UpperCAmelCase					)
 else:
  lowercase__    :   str						= BlipConfig(projection_dim=512		,					text_config={}		,					vision_config={}					)

 lowercase__    :   Tuple						= BlipForConditionalGeneration(UpperCAmelCase					).eval()

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

 lowercase__    :   Dict						= blip_decoder(pretrained=UpperCAmelCase		,					image_size=384		,					vit='''base'''					)
 lowercase__    :   Union[str, Any]						= pt_model.eval()

 lowercase__    :   List[str]						= pt_model.state_dict()
 for key in modified_state_dict.copy():
  lowercase__    :   int						= modified_state_dict.pop(UpperCAmelCase					)
  lowercase__    :   List[str]						= rename_key(UpperCAmelCase					)
  lowercase__    :   str						= value

 hf_model.load_state_dict(UpperCAmelCase					)

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

 lowercase__    :   Optional[int]						= hf_model.generate(UpperCAmelCase		,					UpperCAmelCase					)

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

 lowercase__    :   List[Any]						= 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'
 lowercase__    :   Dict						= (
     '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
 )

 lowercase__    :   Dict						= blip_vqa(pretrained=UpperCAmelCase		,					image_size=UpperCAmelCase		,					vit='''base'''					)
 vqa_model.eval()

 lowercase__    :   str						= vqa_model.state_dict()
 for key in modified_state_dict.copy():
  lowercase__    :   List[str]						= modified_state_dict.pop(UpperCAmelCase					)
  lowercase__    :   Optional[int]						= rename_key(UpperCAmelCase					)
  lowercase__    :   List[Any]						= value

 lowercase__    :   Optional[Any]						= BlipForQuestionAnswering(UpperCAmelCase					)

 hf_vqa_model.load_state_dict(UpperCAmelCase					)

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

 lowercase__    :   Any						= 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'''					)

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

 lowercase__    :   int						= blip_itm(pretrained=UpperCAmelCase		,					image_size=UpperCAmelCase		,					vit='''base'''					)
 itm_model.eval()

 lowercase__    :   int						= itm_model.state_dict()
 for key in modified_state_dict.copy():
  lowercase__    :   Optional[int]						= modified_state_dict.pop(UpperCAmelCase					)
  lowercase__    :   str						= rename_key(UpperCAmelCase					)
  lowercase__    :   int						= value

 lowercase__    :   Optional[int]						= BlipForImageTextRetrieval(UpperCAmelCase					)

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

 lowercase__    :   List[Any]						= hf_itm_model(UpperCAmelCase		,					UpperCAmelCase		,					use_itm_head=UpperCAmelCase					)
 lowercase__    :   Any						= hf_itm_model(UpperCAmelCase		,					UpperCAmelCase		,					use_itm_head=UpperCAmelCase					)

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

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


'''simple docstring'''

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:			Tuple	       =       datasets.utils.logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "utf-8"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True  # deprecated
       SCREAMING_SNAKE_CASE 		=   None  # deprecated
       SCREAMING_SNAKE_CASE 		=   1_0 << 2_0  # 10MB
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   JsonConfig

       def _lowerCAmelCase(						self			)  ->  Any:
        if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''			)
         lowercase__    :   Tuple						= self.config.block_size
        if self.config.use_threads is not True:
         logger.warning(
             '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''			)
        if self.config.newlines_in_values is not None:
         raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''			)
        return datasets.DatasetInfo(features=self.config.features			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""			)
        lowercase__    :   List[str]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   List[str]						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Union[str, Any]						= [files]
         lowercase__    :   Dict						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   str						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= [files]
         lowercase__    :   Optional[Any]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         splits.append(datasets.SplitGenerator(name=__lowerCAmelCase     ,       gen_kwargs={'''files''': files}			)			)
        return splits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  pa.Table:
        if self.config.features is not None:
         # adding missing columns
         for column_name in set(self.config.features			) - set(pa_table.column_names			):
          lowercase__    :   Optional[int]						= self.config.features.arrow_schema.field(__lowerCAmelCase			).type
          lowercase__    :   Union[str, Any]						= pa_table.append_column(__lowerCAmelCase     ,       pa.array([None] * len(__lowerCAmelCase			)     ,       type=__lowerCAmelCase			)			)
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowercase__    :   Dict						= table_cast(__lowerCAmelCase     ,       self.config.features.arrow_schema			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase			)			):
         # If the file is one json object and if we need to look at the list of items in one specific field
         if self.config.field is not None:
          with open(__lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
           lowercase__    :   Dict						= json.load(__lowerCAmelCase			)

          # We keep only the field we are interested in
          lowercase__    :   Any						= dataset[self.config.field]

          # We accept two format: a list of dicts or a dict of lists
          if isinstance(__lowerCAmelCase     ,       (list, tuple)			):
           lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
           lowercase__    :   List[Any]						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
          else:
           lowercase__    :   Union[str, Any]						= dataset
          lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
          yield file_idx, self._cast_table(__lowerCAmelCase			)

         # If the file has one json object per line
         else:
          with open(__lowerCAmelCase     ,       '''rb'''			) as f:
           lowercase__    :   Optional[int]						= 0
           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
           # Set a default minimum value of 16kB if the chunk size is really small
           lowercase__    :   List[str]						= max(self.config.chunksize // 32     ,       16 << 10			)
           lowercase__    :   Union[str, Any]						= (
               self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
           )
           while True:
            lowercase__    :   Tuple						= f.read(self.config.chunksize			)
            if not batch:
             break
            # Finish current line
            try:
             batch += f.readline()
            except (AttributeError, io.UnsupportedOperation):
             batch += readline(__lowerCAmelCase			)
            # PyArrow only accepts utf-8 encoded bytes
            if self.config.encoding != "utf-8":
             lowercase__    :   Dict						= batch.decode(self.config.encoding     ,       errors=__lowerCAmelCase			).encode('''utf-8'''			)
            try:
             while True:
              try:
               lowercase__    :   str						= paj.read_json(
                   io.BytesIO(__lowerCAmelCase			)     ,       read_options=paj.ReadOptions(block_size=__lowerCAmelCase			)			)
               break
              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
               if (
                   isinstance(__lowerCAmelCase     ,       pa.ArrowInvalid			)
                   and "straddling" not in str(__lowerCAmelCase			)
                   or block_size > len(__lowerCAmelCase			)
               ):
                raise
               else:
                # Increase the block size in case it was too small.
                # The block size will be reset for the next file.
                logger.debug(
                    F"""Batch of {len(__lowerCAmelCase			)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""			)
                block_size *= 2
            except pa.ArrowInvalid as e:
             try:
              with open(
                  __lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
               lowercase__    :   Optional[int]						= json.load(__lowerCAmelCase			)
             except json.JSONDecodeError:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise e
             # If possible, parse the file as a list of json objects and exit the loop
             if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):  # list is the only sequence type supported in JSON
              try:
               lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
               lowercase__    :   str						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
               lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
              except (pa.ArrowInvalid, AttributeError) as e:
               logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
               raise ValueError(F"""Not able to read records in the JSON file at {file}."""			) from None
              yield file_idx, self._cast_table(__lowerCAmelCase			)
              break
             else:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise ValueError(
                  F"""Not able to read records in the JSON file at {file}. """
                  F"""You should probably indicate the field of the JSON file containing your records. """
                  F"""This JSON file contain the following fields: {str(list(dataset.keys()			)			)}. """
                  F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """			) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
            yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase			)
            batch_idx += 1

