


'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= s.rsplit(a_, a_)
						return new.join(a_)

def 							__magic_name__(  lowerCamelCase):
						# encoder.embeddings are double copied in original FLAVA
						return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items())

def 							__magic_name__(  lowerCamelCase):
						__lowerCAmelCase											= {}

						__lowerCAmelCase											= ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
						for key, value in state_dict.items():
												for group_key in group_keys:
																		if group_key in key:
																								__lowerCAmelCase											= key.replace(F"""{group_key}.""", F"""{group_key}.group.""")

												if "res_path" in key:
																		__lowerCAmelCase											= key.replace('''res_path.''', '''res_path.path.''')

												if key.endswith('''.w'''):
																		__lowerCAmelCase											= rreplace(a_, '''.w''', '''.weight''', 1)
												if key.endswith('''.b'''):
																		__lowerCAmelCase											= rreplace(a_, '''.b''', '''.bias''', 1)

												__lowerCAmelCase											= value.float()

						return upgrade

@torch.no_grad()
def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=True):
						from dall_e import Encoder

						__lowerCAmelCase											= Encoder()
						if os.path.exists(a_):
												__lowerCAmelCase											= torch.load(a_)
						else:
												__lowerCAmelCase											= torch.hub.load_state_dict_from_url(a_)

						if isinstance(a_, a_):
												__lowerCAmelCase											= ckpt.state_dict()
						encoder.load_state_dict(a_)

						if config_path is not None:
												__lowerCAmelCase											= FlavaImageCodebookConfig.from_pretrained(a_)
						else:
												__lowerCAmelCase											= FlavaImageCodebookConfig()

						__lowerCAmelCase											= FlavaImageCodebook(a_).eval()
						__lowerCAmelCase											= encoder.state_dict()

						__lowerCAmelCase											= upgrade_state_dict(a_)
						hf_model.load_state_dict(a_)
						__lowerCAmelCase											= hf_model.state_dict()
						__lowerCAmelCase											= count_parameters(a_)
						__lowerCAmelCase											= count_parameters(a_)

						assert torch.allclose(a_, a_, atol=1E-3)

						if save_checkpoint:
												hf_model.save_pretrained(a_)
						else:
												return hf_state_dict


if __name__ == "__main__":
	_UpperCAmelCase :       Tuple										= argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	_UpperCAmelCase :       Any										= parser.parse_args()

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








"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_								=		{
    "configuration_blip_2": [
        "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Blip2Config",
        "Blip2QFormerConfig",
        "Blip2VisionConfig",
    ],
    "processing_blip_2": ["Blip2Processor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Blip2Model",
      "Blip2QFormerModel",
      "Blip2PreTrainedModel",
      "Blip2ForConditionalGeneration",
      "Blip2VisionModel",
  ]

if TYPE_CHECKING:
  from .configuration_blip_a import (
      BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlipaConfig,
      BlipaQFormerConfig,
      BlipaVisionConfig,
  )
  from .processing_blip_a import BlipaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip_a import (
        BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipaForConditionalGeneration,
        BlipaModel,
        BlipaPreTrainedModel,
        BlipaQFormerModel,
        BlipaVisionModel,
    )

else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)