






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    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					List[Any],						lowerCamelCase		:					NestedDataStructureLike[PathLike],						lowerCamelCase		:					Optional[NamedSplit] = None,						lowerCamelCase		:					Optional[Features] = None,						lowerCamelCase		:					str = None,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[int] = None,						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    lowerCamelCase,						split=lowerCamelCase,						features=lowerCamelCase,						cache_dir=lowerCamelCase,						keep_in_memory=lowerCamelCase,						streaming=lowerCamelCase,						num_proc=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    field
								lowercase__     				=    path_or_paths if isinstance(lowerCamelCase,						lowerCamelCase						) else {self.split: path_or_paths}
								lowercase__     				=    Json(
								    cache_dir=lowerCamelCase,						data_files=lowerCamelCase,						features=lowerCamelCase,						field=lowerCamelCase,						**lowerCamelCase,						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								# Build iterable dataset
								if self.streaming:
												lowercase__     				=    self.builder.as_streaming_dataset(split=self.split						)
								# Build regular (map-style) dataset
								else:
												lowercase__     				=    None
												lowercase__     				=    None
												lowercase__     				=    None
												lowercase__     				=    None

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



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					Dataset,						lowerCamelCase		:					Union[PathLike, BinaryIO],						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[int] = None,						**lowerCamelCase		:					int,						):

								'''simple docstring'''
								if num_proc is not None and num_proc <= 0:
												raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""						)

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




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.to_json_kwargs.pop('''path_or_buf''',						lowerCamelCase						)
								lowercase__     				=    self.to_json_kwargs.pop('''orient''',						'''records'''						)
								lowercase__     				=    self.to_json_kwargs.pop('''lines''',						True if orient == '''records''' else False						)
								lowercase__     				=    self.to_json_kwargs.pop('''index''',						False if orient in ['''split''', '''table'''] else True						)
								lowercase__     				=    self.to_json_kwargs.pop('''compression''',						lowerCamelCase						)

								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=lowerCamelCase						) as buffer:
																lowercase__     				=    self._write(file_obj=lowerCamelCase,						orient=lowerCamelCase,						lines=lowerCamelCase,						index=lowerCamelCase,						**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.'''						)
												lowercase__     				=    self._write(
												    file_obj=self.path_or_buf,						orient=lowerCamelCase,						lines=lowerCamelCase,						index=lowerCamelCase,						**self.to_json_kwargs						)
								return written




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__				,		lowercase__				,		lowercase__				,		lowercase__				,		lowercase__     				=    args

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




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					BinaryIO,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Optional[Any],						**lowerCamelCase		:					Any,						):

								'''simple docstring'''
								lowercase__     				=    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''',						):
																lowercase__     				=    self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
																written += file_obj.write(lowerCamelCase						)
								else:
												lowercase__				,		lowercase__     				=    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,						lowerCamelCase,						lowerCamelCase						)],						),						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(lowerCamelCase						)

								return written












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)




