'''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 							snake_case__				(     _A:   str ,  _A:   int					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
		lowerCAmelCase     =    Image.open(requests.get(_A ,  stream=_A					).raw					).convert("""RGB"""					)

		lowerCAmelCase     =    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)					),
		    ]					)
		lowerCAmelCase     =    transform(_A					).unsqueeze(0					).to(_A					)
		return image






def 							snake_case__				(     _A:   Any					)       ->      Optional[Any]:

		'''simple docstring'''


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

		if "encoder.pos_embed" in key:
				lowerCAmelCase     =    re.sub(r"""encoder.pos_embed""" ,  """embeddings.position_embedding""" ,  _A					)
		if "encoder.cls_token" in key:
				lowerCAmelCase     =    re.sub(r"""encoder.cls_token""" ,  """embeddings.class_embedding""" ,  _A					)

		if "self_attn" in key:
				lowerCAmelCase     =    re.sub(r"""self_attn.proj""" ,  """self_attn.projection""" ,  _A					)

		return key






@torch.no_grad()
def 							snake_case__				(     _A:   Union[str, Any] ,  _A:   List[str]=None					)       ->      Optional[int]:

		'''simple docstring'''


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

		lowerCAmelCase     =    BlipForConditionalGeneration(_A					).eval()

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

		lowerCAmelCase     =    blip_decoder(pretrained=_A ,  image_size=384 ,  vit="""base"""					)
		lowerCAmelCase     =    pt_model.eval()

		lowerCAmelCase     =    pt_model.state_dict()
		for key in modified_state_dict.copy():
				lowerCAmelCase     =    modified_state_dict.pop(_A					)
				lowerCAmelCase     =    rename_key(_A					)
				lowerCAmelCase     =    value

		hf_model.load_state_dict(_A					)

		lowerCAmelCase     =    384
		lowerCAmelCase     =    load_demo_image(image_size=_A ,  device="""cpu"""					)
		lowerCAmelCase     =    BertTokenizer.from_pretrained("""bert-base-uncased"""					)
		lowerCAmelCase     =    tokenizer(["""a picture of"""]					).input_ids

		lowerCAmelCase     =    hf_model.generate(_A ,  _A					)

		assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

		lowerCAmelCase     =    hf_model.generate(_A					)

		assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

		if pytorch_dump_folder_path is not None:
				hf_model.save_pretrained(_A					)

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

		lowerCAmelCase     =    blip_vqa(pretrained=_A ,  image_size=_A ,  vit="""base"""					)
		vqa_model.eval()

		lowerCAmelCase     =    vqa_model.state_dict()
		for key in modified_state_dict.copy():
				lowerCAmelCase     =    modified_state_dict.pop(_A					)
				lowerCAmelCase     =    rename_key(_A					)
				lowerCAmelCase     =    value

		lowerCAmelCase     =    BlipForQuestionAnswering(_A					)

		hf_vqa_model.load_state_dict(_A					)

		lowerCAmelCase     =    ["""How many dogs are in this image?"""]
		lowerCAmelCase     =    tokenizer(_A ,  return_tensors="""pt"""					).input_ids

		lowerCAmelCase     =    hf_vqa_model.generate(_A ,  _A					)
		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"""					)

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

		lowerCAmelCase     =    blip_itm(pretrained=_A ,  image_size=_A ,  vit="""base"""					)
		itm_model.eval()

		lowerCAmelCase     =    itm_model.state_dict()
		for key in modified_state_dict.copy():
				lowerCAmelCase     =    modified_state_dict.pop(_A					)
				lowerCAmelCase     =    rename_key(_A					)
				lowerCAmelCase     =    value

		lowerCAmelCase     =    BlipForImageTextRetrieval(_A					)

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

		hf_itm_model.load_state_dict(_A					)
		hf_itm_model.eval()

		lowerCAmelCase     =    hf_itm_model(_A ,  _A ,  use_itm_head=_A					)
		lowerCAmelCase     =    hf_itm_model(_A ,  _A ,  use_itm_head=_A					)

		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__":
						__lowercase        =    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''')
						__lowercase        =    parser.parse_args()

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





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__lowercase        =    logging.get_logger(__name__)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = True			,       __lowerCAmelCase = 1 / 255			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 384, """width""": 384}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_rescale
					lowerCAmelCase     =    rescale_factor
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCAmelCase     =    image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCAmelCase     =    do_convert_rgb



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
					lowerCAmelCase     =    (size["""height"""], size["""width"""])
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return rescale(__lowerCAmelCase			,       scale=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return normalize(__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase     =    rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase     =    image_std if image_std is not None else self.image_std
					lowerCAmelCase     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray.""")

					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True.""")

					if do_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							lowerCAmelCase     =    [convert_to_rgb(__lowerCAmelCase) for image in images]

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_rescale:
							lowerCAmelCase     =    [self.rescale(image=__lowerCAmelCase			,       scale=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase) for image in images]

					lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    BatchFeature(data={"""pixel_values""": images}			,       tensor_type=__lowerCAmelCase)

					return encoded_outputs




