"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Union[str, Any]		) ->    Dict:
      '''simple docstring'''


      lowercase														=	s.rsplit(lowerCAmelCase__					,		lowerCAmelCase__		)
      return new.join(lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any		) ->    List[str]:
      '''simple docstring'''


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]		) ->    Dict:
      '''simple docstring'''


      lowercase														=	{}

      lowercase														=	["""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:
                        lowercase														=	key.replace(f'{group_key}.'					,		f'{group_key}.group.'		)

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

            if key.endswith(""".w"""		):
                  lowercase														=	rreplace(lowerCAmelCase__					,		""".w"""					,		""".weight"""					,		1		)
            if key.endswith(""".b"""		):
                  lowercase														=	rreplace(lowerCAmelCase__					,		""".b"""					,		""".bias"""					,		1		)

            lowercase														=	value.float()

      return upgrade


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Dict=None					,		lowerCAmelCase__     :str=True		) ->    str:
      '''simple docstring'''


      from dall_e import Encoder

      lowercase														=	Encoder()
      if os.path.exists(lowerCAmelCase__		):
            lowercase														=	torch.load(lowerCAmelCase__		)
      else:
            lowercase														=	torch.hub.load_state_dict_from_url(lowerCAmelCase__		)

      if isinstance(lowerCAmelCase__					,		lowerCAmelCase__		):
            lowercase														=	ckpt.state_dict()
      encoder.load_state_dict(lowerCAmelCase__		)

      if config_path is not None:
            lowercase														=	FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase__		)
      else:
            lowercase														=	FlavaImageCodebookConfig()

      lowercase														=	FlavaImageCodebook(lowerCAmelCase__		).eval()
      lowercase														=	encoder.state_dict()

      lowercase														=	upgrade_state_dict(lowerCAmelCase__		)
      hf_model.load_state_dict(lowerCAmelCase__		)
      lowercase														=	hf_model.state_dict()
      lowercase														=	count_parameters(lowerCAmelCase__		)
      lowercase														=	count_parameters(lowerCAmelCase__		)

      assert torch.allclose(lowerCAmelCase__					,		lowerCAmelCase__					,		atol=1e-3		)

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


if __name__ == "__main__":
       __lowerCAmelCase    :       List[str]					     =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""")
       __lowerCAmelCase    :       Tuple					     =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 List, Mapping

from packaging import version

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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	'efficientnet'

      def __init__(						self		,       __lowerCAmelCase = 3		,       __lowerCAmelCase = 600		,       __lowerCAmelCase = 2.0		,       __lowerCAmelCase = 3.1		,       __lowerCAmelCase = 8		,       __lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3]		,       __lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192]		,       __lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320]		,       __lowerCAmelCase = []		,       __lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1]		,       __lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1]		,       __lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6]		,       __lowerCAmelCase = 0.2_5		,       __lowerCAmelCase = "swish"		,       __lowerCAmelCase = 2560		,       __lowerCAmelCase = "mean"		,       __lowerCAmelCase = 0.0_2		,       __lowerCAmelCase = 0.0_0_1		,       __lowerCAmelCase = 0.9_9		,       __lowerCAmelCase = 0.5		,       __lowerCAmelCase = 0.2		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)

            lowercase														=	num_channels
            lowercase														=	image_size
            lowercase														=	width_coefficient
            lowercase														=	depth_coefficient
            lowercase														=	depth_divisor
            lowercase														=	kernel_sizes
            lowercase														=	in_channels
            lowercase														=	out_channels
            lowercase														=	depthwise_padding
            lowercase														=	strides
            lowercase														=	num_block_repeats
            lowercase														=	expand_ratios
            lowercase														=	squeeze_expansion_ratio
            lowercase														=	hidden_act
            lowercase														=	hidden_dim
            lowercase														=	pooling_type
            lowercase														=	initializer_range
            lowercase														=	batch_norm_eps
            lowercase														=	batch_norm_momentum
            lowercase														=	dropout_rate
            lowercase														=	drop_connect_rate
            lowercase														=	sum(__lowerCAmelCase						) * 4






class  _A						(       lowerCAmelCase	):
      snake_case__			:   Dict							  =	version.parse('1.11'	)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ]						)







      @property
      def 				A__       (						self						):







            """simple docstring"""



            return 1E-5



