




"""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     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
			lowerCamelCase			      =					Image.open(requests.get(snake_case__       , stream=snake_case__				).raw				).convert("""RGB"""				)

			lowerCamelCase			      =					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)				),
			    ]				)
			lowerCamelCase			      =					transform(snake_case__				).unsqueeze(0				).to(snake_case__				)
			return image



def     a__ (				snake_case__				)						->					List[Any]:
			if "visual_encoder" in key:
						lowerCamelCase			      =					re.sub("""visual_encoder*"""       , """vision_model.encoder"""       , snake_case__				)
			if "blocks" in key:
						lowerCamelCase			      =					re.sub(R"""blocks"""       , """layers"""       , snake_case__				)
			if "attn" in key:
						lowerCamelCase			      =					re.sub(R"""attn"""       , """self_attn"""       , snake_case__				)
			if "norm1" in key:
						lowerCamelCase			      =					re.sub(R"""norm1"""       , """layer_norm1"""       , snake_case__				)
			if "norm2" in key:
						lowerCamelCase			      =					re.sub(R"""norm2"""       , """layer_norm2"""       , snake_case__				)
			if "encoder.norm" in key:
						lowerCamelCase			      =					re.sub(R"""encoder.norm"""       , """post_layernorm"""       , snake_case__				)
			if "encoder.patch_embed.proj" in key:
						lowerCamelCase			      =					re.sub(R"""encoder.patch_embed.proj"""       , """embeddings.patch_embedding"""       , snake_case__				)

			if "encoder.pos_embed" in key:
						lowerCamelCase			      =					re.sub(R"""encoder.pos_embed"""       , """embeddings.position_embedding"""       , snake_case__				)
			if "encoder.cls_token" in key:
						lowerCamelCase			      =					re.sub(R"""encoder.cls_token"""       , """embeddings.class_embedding"""       , snake_case__				)

			if "self_attn" in key:
						lowerCamelCase			      =					re.sub(R"""self_attn.proj"""       , """self_attn.projection"""       , snake_case__				)

			return key



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__=None				)						->					Any:
			if config_path is not None:
						lowerCamelCase			      =					BlipConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					BlipConfig(projection_dim=5_12       , text_config={}       , vision_config={}				)

			lowerCamelCase			      =					BlipForConditionalGeneration(snake_case__				).eval()

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

			lowerCamelCase			      =					blip_decoder(pretrained=snake_case__       , image_size=3_84       , vit="""base"""				)
			lowerCamelCase			      =					pt_model.eval()

			lowerCamelCase			      =					pt_model.state_dict()
			for key in modified_state_dict.copy():
						lowerCamelCase			      =					modified_state_dict.pop(snake_case__				)
						lowerCamelCase			      =					rename_key(snake_case__				)
						lowerCamelCase			      =					value

			hf_model.load_state_dict(snake_case__				)

			lowerCamelCase			      =					3_84
			lowerCamelCase			      =					load_demo_image(image_size=snake_case__       , device="""cpu"""				)
			lowerCamelCase			      =					BertTokenizer.from_pretrained("""bert-base-uncased"""				)
			lowerCamelCase			      =					tokenizer(["""a picture of"""]				).input_ids

			lowerCamelCase			      =					hf_model.generate(snake_case__       , snake_case__				)

			assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

			lowerCamelCase			      =					hf_model.generate(snake_case__				)

			assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

			lowerCamelCase			      =					blip_vqa(pretrained=snake_case__       , image_size=snake_case__       , vit="""base"""				)
			vqa_model.eval()

			lowerCamelCase			      =					vqa_model.state_dict()
			for key in modified_state_dict.copy():
						lowerCamelCase			      =					modified_state_dict.pop(snake_case__				)
						lowerCamelCase			      =					rename_key(snake_case__				)
						lowerCamelCase			      =					value

			lowerCamelCase			      =					BlipForQuestionAnswering(snake_case__				)

			hf_vqa_model.load_state_dict(snake_case__				)

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

			lowerCamelCase			      =					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"""				)

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

			lowerCamelCase			      =					blip_itm(pretrained=snake_case__       , image_size=snake_case__       , vit="""base"""				)
			itm_model.eval()

			lowerCamelCase			      =					itm_model.state_dict()
			for key in modified_state_dict.copy():
						lowerCamelCase			      =					modified_state_dict.pop(snake_case__				)
						lowerCamelCase			      =					rename_key(snake_case__				)
						lowerCamelCase			      =					value

			lowerCamelCase			      =					BlipForImageTextRetrieval(snake_case__				)

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

			hf_itm_model.load_state_dict(snake_case__				)
			hf_itm_model.eval()

			lowerCamelCase			      =					hf_itm_model(snake_case__       , snake_case__       , use_itm_head=snake_case__				)
			lowerCamelCase			      =					hf_itm_model(snake_case__       , snake_case__       , use_itm_head=snake_case__				)

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

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









"""simple docstring"""




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:
			try:
						with open(snake_case__       , """rb"""				) as flax_state_f:
									lowerCamelCase			      =					from_bytes(snake_case__       , flax_state_f.read()				)
			except UnpicklingError as e:
						try:
									with open(snake_case__				) as f:
												if f.read().startswith("""version"""				):
															raise OSError(
															    """You seem to have cloned a repository without having git-lfs installed. Please"""
															    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
															    """ folder you cloned."""				)
												else:
															raise ValueError from e
						except (UnicodeDecodeError, ValueError):
									raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '				)

			return load_flax_weights_in_pytorch_model(snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:

			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""				)
						raise

			# check if we have bf16 weights
			lowerCamelCase			      =					flatten_dict(jax.tree_util.tree_map(lambda snake_case__				: x.dtype == jnp.bfloataa       , snake_case__				)				).values()
			if any(snake_case__				):
						# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
						    """before loading those in PyTorch model."""				)
						lowerCamelCase			      =					jax.tree_util.tree_map(
						    lambda snake_case__				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params       , snake_case__				)

			lowerCamelCase			      =					""""""

			lowerCamelCase			      =					flatten_dict(snake_case__       , sep="""."""				)
			lowerCamelCase			      =					pt_model.state_dict()

			# keep track of unexpected & missing keys
			lowerCamelCase			      =					[]
			lowerCamelCase			      =					set(pt_model_dict.keys()				)

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						lowerCamelCase			      =					flax_key_tuple.split("""."""				)

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					jnp.transpose(snake_case__       , (3, 2, 0, 1)				)
						elif flax_key_tuple_array[-1] == "kernel":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(snake_case__				):
												lowerCamelCase			      =					(
												    flax_key_tuple_string.replace("""_0"""       , """.0"""				)
												    .replace("""_1"""       , """.1"""				)
												    .replace("""_2"""       , """.2"""				)
												    .replace("""_3"""       , """.3"""				)
												    .replace("""_4"""       , """.4"""				)
												    .replace("""_5"""       , """.5"""				)
												    .replace("""_6"""       , """.6"""				)
												    .replace("""_7"""       , """.7"""				)
												    .replace("""_8"""       , """.8"""				)
												    .replace("""_9"""       , """.9"""				)
												)

						lowerCamelCase			      =					""".""".join(snake_case__				)

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'				)
									else:
												# add weight to pytorch dict
												lowerCamelCase			      =					np.asarray(snake_case__				) if not isinstance(snake_case__       , np.ndarray				) else flax_tensor
												lowerCamelCase			      =					torch.from_numpy(snake_case__				)
												# remove from missing keys
												missing_keys.remove(snake_case__				)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(snake_case__				)

			pt_model.load_state_dict(snake_case__				)

			# re-transform missing_keys to list
			lowerCamelCase			      =					list(snake_case__				)

			if len(snake_case__				) > 0:
						logger.warning(
						    """Some weights of the Flax model were not used when initializing the PyTorch model"""
						    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
						    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
						    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
						    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
						    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
						    """ FlaxBertForSequenceClassification model)."""				)
			if len(snake_case__				) > 0:
						logger.warning(
						    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
						    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
						    """ use it for predictions and inference."""				)

			return pt_model



