"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					s.rsplit(lowercase      ,lowercase						)
  return new.join(lowercase						)






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  # 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    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					{}

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

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

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

    _UpperCAmelCase         =					value.float()

  return upgrade






@torch.no_grad()
def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase=None      ,lowercase=True						):







  """simple docstring"""
  from dall_e import Encoder

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

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

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

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

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

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

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


if __name__ == "__main__":
    UpperCAmelCase__     						=  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__     						=  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


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Any   							=							'efficientnet'


    def __init__(    self		:     Any      , __lowerCAmelCase		:     int = 3      , __lowerCAmelCase		:     int = 600      , __lowerCAmelCase		:     float = 2.0      , __lowerCAmelCase		:     float = 3.1      , __lowerCAmelCase		:     int = 8      , __lowerCAmelCase		:     List[int] = [3, 3, 5, 3, 5, 5, 3]      , __lowerCAmelCase		:     List[int] = [32, 16, 24, 40, 80, 112, 192]      , __lowerCAmelCase		:     List[int] = [16, 24, 40, 80, 112, 192, 320]      , __lowerCAmelCase		:     List[int] = []      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 2, 1, 2, 1]      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 3, 3, 4, 1]      , __lowerCAmelCase		:     List[int] = [1, 6, 6, 6, 6, 6, 6]      , __lowerCAmelCase		:     float = 0.25      , __lowerCAmelCase		:     str = "swish"      , __lowerCAmelCase		:     int = 2560      , __lowerCAmelCase		:     str = "mean"      , __lowerCAmelCase		:     float = 0.02      , __lowerCAmelCase		:     float = 0.001      , __lowerCAmelCase		:     float = 0.99      , __lowerCAmelCase		:     float = 0.5      , __lowerCAmelCase		:     float = 0.2      , **__lowerCAmelCase		:     List[Any]      , ):
      super().__init__(**__lowerCAmelCase    )

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



class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Dict   							=							version.parse('1.11'      )


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      return 1e-5
