





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def    __UpperCamelCase      (    a,     a,     a,     a) ->int:
					lowerCamelCase__       =       s.rsplit(a,     a)
					return new.join(a)





def    __UpperCamelCase      (    a) ->int:
					# 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    __UpperCamelCase      (    a) ->List[str]:
					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    __UpperCamelCase      (    a,     a,     a=None,     a=True) ->Union[str, Any]:
					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__":
						A_												=     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")
						A_												=     parser.parse_args()

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








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


A_												=     logging.get_logger(__name__)

A_												=     {
    "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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "longformer"





					def __init__( self	,  _lowerCAmelCase = 512	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 1	,  _lowerCAmelCase = 0	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 3_0522	,  _lowerCAmelCase = 768	,  _lowerCAmelCase = 12	,  _lowerCAmelCase = 12	,  _lowerCAmelCase = 3072	,  _lowerCAmelCase = "gelu"	,  _lowerCAmelCase = 0.1	,  _lowerCAmelCase = 0.1	,  _lowerCAmelCase = 512	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 0.02	,  _lowerCAmelCase = 1E-12	,  _lowerCAmelCase = False	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       attention_window
										lowerCamelCase__       =       sep_token_id
										lowerCamelCase__       =       bos_token_id
										lowerCamelCase__       =       eos_token_id
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       onnx_export

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = "default"	,  _lowerCAmelCase = None	):
										super().__init__(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       True





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





					@property
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       super().outputs
										if self.task == "default":
															lowerCamelCase__       =       {0: "batch"}
										return outputs





					@property
					def 					__magic_name__ ( self	):
										return 1E-4





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






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  ):
										lowerCamelCase__       =       super().generate_dummy_inputs(
										    preprocessor=_lowerCAmelCase	,  batch_size=_lowerCAmelCase	,  seq_length=_lowerCAmelCase	,  is_pair=_lowerCAmelCase	,  framework=_lowerCAmelCase	)
										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
										lowerCamelCase__       =       torch.zeros_like(inputs["input_ids"]	)
										# make every second token global
										lowerCamelCase__       =       1

										return inputs

