


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__	(      a	:   List[str]	,  a	:   Tuple				)    ->	Dict:





			"""simple docstring"""
			a__ :Optional[Any]						  =    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
			a__ :List[str]						  =    Image.open(requests.get(_snake_case	,  stream=_snake_case				).raw				).convert("RGB"				)

			a__ :str						  =    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)				),
			    ]				)
			a__ :Optional[int]						  =    transform(_snake_case				).unsqueeze(0				).to(_snake_case				)
			return image


def 				lowerCamelCase__	(      a	:   Any				)    ->	List[str]:





			"""simple docstring"""






			if "visual_encoder" in key:
						a__ :Union[str, Any]						  =    re.sub("visual_encoder*"	,  "vision_model.encoder"	,  _snake_case				)
			if "blocks" in key:
						a__ :int						  =    re.sub(r"blocks"	,  "layers"	,  _snake_case				)
			if "attn" in key:
						a__ :Tuple						  =    re.sub(r"attn"	,  "self_attn"	,  _snake_case				)
			if "norm1" in key:
						a__ :str						  =    re.sub(r"norm1"	,  "layer_norm1"	,  _snake_case				)
			if "norm2" in key:
						a__ :int						  =    re.sub(r"norm2"	,  "layer_norm2"	,  _snake_case				)
			if "encoder.norm" in key:
						a__ :Any						  =    re.sub(r"encoder.norm"	,  "post_layernorm"	,  _snake_case				)
			if "encoder.patch_embed.proj" in key:
						a__ :Tuple						  =    re.sub(r"encoder.patch_embed.proj"	,  "embeddings.patch_embedding"	,  _snake_case				)

			if "encoder.pos_embed" in key:
						a__ :str						  =    re.sub(r"encoder.pos_embed"	,  "embeddings.position_embedding"	,  _snake_case				)
			if "encoder.cls_token" in key:
						a__ :Tuple						  =    re.sub(r"encoder.cls_token"	,  "embeddings.class_embedding"	,  _snake_case				)

			if "self_attn" in key:
						a__ :List[Any]						  =    re.sub(r"self_attn.proj"	,  "self_attn.projection"	,  _snake_case				)

			return key


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Dict	,  a	:   Tuple=None				)    ->	List[Any]:





			"""simple docstring"""






			if config_path is not None:
						a__ :List[str]						  =    BlipConfig.from_pretrained(_snake_case				)
			else:
						a__ :List[str]						  =    BlipConfig(projection_dim=512	,  text_config={}	,  vision_config={}				)

			a__ :Dict						  =    BlipForConditionalGeneration(_snake_case				).eval()

			a__ :List[str]						  =    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

			a__ :Any						  =    blip_decoder(pretrained=_snake_case	,  image_size=384	,  vit="base"				)
			a__ :Optional[Any]						  =    pt_model.eval()

			a__ :Dict						  =    pt_model.state_dict()
			for key in modified_state_dict.copy():
						a__ :Dict						  =    modified_state_dict.pop(_snake_case				)
						a__ :Any						  =    rename_key(_snake_case				)
						a__ :Optional[Any]						  =    value

			hf_model.load_state_dict(_snake_case				)

			a__ :Dict						  =    384
			a__ :Optional[int]						  =    load_demo_image(image_size=_snake_case	,  device="cpu"				)
			a__ :Optional[int]						  =    BertTokenizer.from_pretrained("bert-base-uncased"				)
			a__ :Union[str, Any]						  =    tokenizer(["a picture of"]				).input_ids

			a__ :Dict						  =    hf_model.generate(_snake_case	,  _snake_case				)

			assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

			a__ :Any						  =    hf_model.generate(_snake_case				)

			assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

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

			a__ :Optional[Any]						  =    blip_vqa(pretrained=_snake_case	,  image_size=_snake_case	,  vit="base"				)
			vqa_model.eval()

			a__ :int						  =    vqa_model.state_dict()
			for key in modified_state_dict.copy():
						a__ :Union[str, Any]						  =    modified_state_dict.pop(_snake_case				)
						a__ :Optional[int]						  =    rename_key(_snake_case				)
						a__ :str						  =    value

			a__ :Any						  =    BlipForQuestionAnswering(_snake_case				)

			hf_vqa_model.load_state_dict(_snake_case				)

			a__ :Any						  =    ["How many dogs are in this image?"]
			a__ :Any						  =    tokenizer(_snake_case	,  return_tensors="pt"				).input_ids

			a__ :int						  =    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"				)

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

			a__ :Tuple						  =    blip_itm(pretrained=_snake_case	,  image_size=_snake_case	,  vit="base"				)
			itm_model.eval()

			a__ :Optional[Any]						  =    itm_model.state_dict()
			for key in modified_state_dict.copy():
						a__ :Tuple						  =    modified_state_dict.pop(_snake_case				)
						a__ :Union[str, Any]						  =    rename_key(_snake_case				)
						a__ :str						  =    value

			a__ :List[str]						  =    BlipForImageTextRetrieval(_snake_case				)

			a__ :Optional[Any]						  =    ["A picture of a woman with a dog sitting in a beach"]
			a__ :Any						  =    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()

			a__ :List[Any]						  =    hf_itm_model(_snake_case	,  _snake_case	,  use_itm_head=_snake_case				)
			a__ :Any						  =    hf_itm_model(_snake_case	,  _snake_case	,  use_itm_head=_snake_case				)

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

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






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


snake_case__			 =						[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple=None	,  a	:   int=None	,  a	:   List[str]=None				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    True
			while ask_again:
						a__ :str						  =    input(a				)
						try:
									if default is not None and len(a				) == 0:
												return default
									return convert_value(a				) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(a				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Union[str, Any]=[]	,  a	:   str=None	,  a	:   str=0				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    BulletMenu(a	,  a				)
			a__ :int						  =    menu.run(default_choice=a				)
			return convert_value(a				) if convert_value is not None else result


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :str						  =    int(a				)
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    int(a				)
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	str:





			"""simple docstring"""
			a__ :int						  =    int(a				)
			return DynamoBackend(DYNAMO_BACKENDS[value]				).value


def 				lowerCamelCase__	(      a	:   Any				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	List[str]:





			"""simple docstring"""
			return {"yes": True, "no": False}[value.lower()]







class 					lowerCAmelCase_ (       argparse.RawDescriptionHelpFormatter):




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   Tuple   ,     __A						:   Any						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    super()._format_usage(__A   ,     __A   ,     __A   ,     __A						)
									a__ :Any						  =    usage.replace("<command> [<args>] "   ,     ""						)
									return usage


