


"""simple docstring"""







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      s.rsplit(A_  ,       A_	)
  return new.join(A_	)


def 					lowercase      (    A_	)->				str:
  '''simple docstring'''







  return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()	)


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Any											=      {}

  a						:		Optional[int]											=      ["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:
        a						:		List[Any]											=      key.replace(F'''{group_key}.'''  ,       F'''{group_key}.group.'''	)

    if "res_path" in key:
      a						:		int											=      key.replace("res_path."  ,       "res_path.path."	)

    if key.endswith(".w"	):
      a						:		Dict											=      rreplace(A_  ,       ".w"  ,       ".weight"  ,       1	)
    if key.endswith(".b"	):
      a						:		Dict											=      rreplace(A_  ,       ".b"  ,       ".bias"  ,       1	)

    a						:		Tuple											=      value.float()

  return upgrade


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_=None  ,       A_=True	)->				Any:
  '''simple docstring'''







  from dall_e import Encoder

  a						:		Optional[int]											=      Encoder()
  if os.path.exists(A_	):
    a						:		Any											=      torch.load(A_	)
  else:
    a						:		Union[str, Any]											=      torch.hub.load_state_dict_from_url(A_	)

  if isinstance(A_  ,       A_	):
    a						:		Union[str, Any]											=      ckpt.state_dict()
  encoder.load_state_dict(A_	)

  if config_path is not None:
    a						:		int											=      FlavaImageCodebookConfig.from_pretrained(A_	)
  else:
    a						:		Tuple											=      FlavaImageCodebookConfig()

  a						:		Any											=      FlavaImageCodebook(A_	).eval()
  a						:		Union[str, Any]											=      encoder.state_dict()

  a						:		Optional[Any]											=      upgrade_state_dict(A_	)
  hf_model.load_state_dict(A_	)
  a						:		List[Any]											=      hf_model.state_dict()
  a						:		Optional[int]											=      count_parameters(A_	)
  a						:		Tuple											=      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__":
 __lowercase    =				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""")
 __lowercase    =				parser.parse_args()

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










"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






