


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__:		str      ,  a__:		List[Any]	)  ->		Optional[int]:


       '''simple docstring'''





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

       _UpperCAmelCase					=      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)	),
           ]	)
       _UpperCAmelCase					=      transform(a__	).unsqueeze(0	).to(a__	)
       return image

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		int:


       '''simple docstring'''





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

       if "encoder.pos_embed" in key:
              _UpperCAmelCase					=      re.sub(R'encoder.pos_embed'      ,  'embeddings.position_embedding'      ,  a__	)
       if "encoder.cls_token" in key:
              _UpperCAmelCase					=      re.sub(R'encoder.cls_token'      ,  'embeddings.class_embedding'      ,  a__	)

       if "self_attn" in key:
              _UpperCAmelCase					=      re.sub(R'self_attn.proj'      ,  'self_attn.projection'      ,  a__	)

       return key

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]=None	)  ->		Optional[Any]:


       '''simple docstring'''





       if config_path is not None:
              _UpperCAmelCase					=      BlipConfig.from_pretrained(a__	)
       else:
              _UpperCAmelCase					=      BlipConfig(projection_dim=5_1_2      ,  text_config={}      ,  vision_config={}	)

       _UpperCAmelCase					=      BlipForConditionalGeneration(a__	).eval()

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

       _UpperCAmelCase					=      blip_decoder(pretrained=a__      ,  image_size=3_8_4      ,  vit='base'	)
       _UpperCAmelCase					=      pt_model.eval()

       _UpperCAmelCase					=      pt_model.state_dict()
       for key in modified_state_dict.copy():
              _UpperCAmelCase					=      modified_state_dict.pop(a__	)
              _UpperCAmelCase					=      rename_key(a__	)
              _UpperCAmelCase					=      value

       hf_model.load_state_dict(a__	)

       _UpperCAmelCase					=      3_8_4
       _UpperCAmelCase					=      load_demo_image(image_size=a__      ,  device='cpu'	)
       _UpperCAmelCase					=      BertTokenizer.from_pretrained('bert-base-uncased'	)
       _UpperCAmelCase					=      tokenizer(['a picture of']	).input_ids

       _UpperCAmelCase					=      hf_model.generate(a__      ,  a__	)

       assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

       _UpperCAmelCase					=      hf_model.generate(a__	)

       assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

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

       _UpperCAmelCase					=      blip_vqa(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
       vqa_model.eval()

       _UpperCAmelCase					=      vqa_model.state_dict()
       for key in modified_state_dict.copy():
              _UpperCAmelCase					=      modified_state_dict.pop(a__	)
              _UpperCAmelCase					=      rename_key(a__	)
              _UpperCAmelCase					=      value

       _UpperCAmelCase					=      BlipForQuestionAnswering(a__	)

       hf_vqa_model.load_state_dict(a__	)

       _UpperCAmelCase					=      ['How many dogs are in this image?']
       _UpperCAmelCase					=      tokenizer(a__      ,  return_tensors='pt'	).input_ids

       _UpperCAmelCase					=      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'	)

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

       _UpperCAmelCase					=      blip_itm(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
       itm_model.eval()

       _UpperCAmelCase					=      itm_model.state_dict()
       for key in modified_state_dict.copy():
              _UpperCAmelCase					=      modified_state_dict.pop(a__	)
              _UpperCAmelCase					=      rename_key(a__	)
              _UpperCAmelCase					=      value

       _UpperCAmelCase					=      BlipForImageTextRetrieval(a__	)

       _UpperCAmelCase					=      ['A picture of a woman with a dog sitting in a beach']
       _UpperCAmelCase					=      tokenizer(
           a__      ,  return_tensors='pt'      ,  padding='max_length'      ,  truncation=a__      ,  max_length=3_5      ,  ).input_ids

       hf_itm_model.load_state_dict(a__	)
       hf_itm_model.eval()

       _UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)
       _UpperCAmelCase					=      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__":
  lowerCAmelCase__  :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''')
  lowerCAmelCase__  :List[Any]             =							parser.parse_args()

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




# Copyright 2022 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
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      parser.add_argument_group(
							    'Config Arguments'      ,  'Arguments that can be configured through `accelerate config`.'	)
							config_args.add_argument(
							    '--config_file'      ,  type=a__      ,  default=a__      ,  help='Path to the config file to use for accelerate.'      ,  )
							config_args.add_argument(
							    '--tpu_name'      ,  default=a__      ,  help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'      ,  )
							config_args.add_argument(
							    '--tpu_zone'      ,  default=a__      ,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'      ,  )
							_UpperCAmelCase					=      parser.add_argument_group('TPU Arguments'      ,  'Arguments for options ran inside the TPU.'	)
							pod_args.add_argument(
							    '--use_alpha'      ,  action='store_true'      ,  help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'      ,  )
							pod_args.add_argument(
							    '--command_file'      ,  default=a__      ,  help='The path to the file containing the commands to run on the pod on startup.'      ,  )
							pod_args.add_argument(
							    '--command'      ,  action='append'      ,  nargs='+'      ,  help='A command to run on the pod. Can be passed multiple times.'      ,  )
							pod_args.add_argument(
							    '--install_accelerate'      ,  action='store_true'      ,  help='Whether to install accelerate on the pod. Defaults to False.'      ,  )
							pod_args.add_argument(
							    '--accelerate_version'      ,  default='latest'      ,  help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'      ,  )
							pod_args.add_argument(
							    '--debug'      ,  action='store_true'      ,  help='If set, will print the command that would be run instead of running it.'	)

							if subparsers is not None:
														parser.set_defaults(func=a__	)
							return parser

def 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(a__	):
														_UpperCAmelCase					=      load_config_from_file(args.config_file	)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					_UpperCAmelCase					=      defaults.command_file
														if not args.command and defaults.commands is not None:
																					_UpperCAmelCase					=      defaults.commands
														if not args.tpu_name:
																					_UpperCAmelCase					=      defaults.tpu_name
														if not args.tpu_zone:
																					_UpperCAmelCase					=      defaults.tpu_zone
							if args.accelerate_version == "dev":
														_UpperCAmelCase					=      'git+https://github.com/huggingface/accelerate.git'
							elif args.accelerate_version == "latest":
														_UpperCAmelCase					=      'accelerate -U'
							elif isinstance(parse(args.accelerate_version	)      ,  a__	):
														_UpperCAmelCase					=      F'''accelerate=={args.accelerate_version}'''

							if not args.command_file and not args.command:
														raise ValueError('You must specify either a command file or a command to run on the pod.'	)

							if args.command_file:
														with open(args.command_file      ,  'r'	) as f:
																					_UpperCAmelCase					=      [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]      ,  a__	):
														_UpperCAmelCase					=      [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							_UpperCAmelCase					=      ['cd /usr/share']
							if args.install_accelerate:
														new_cmd += [F'''pip install {args.accelerate_version}''']
							new_cmd += args.command
							_UpperCAmelCase					=      '; '.join(a__	)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['gcloud']
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'''Running {" ".join(a__	)}'''	)
														return
							subprocess.run(a__	)
							print('Successfully setup pod.'	)







def 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
