
'''simple docstring'''

import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import jax
     import jaxlib

UpperCamelCase_   	=				get_logger()

UpperCamelCase_   	=				None


class      a_     (TensorFormatter[Mapping, """jax.Array""", Mapping]			):




       def __init__(   self      ,      snake_case_=None      ,      snake_case_=None      ,      **snake_case_     ):
         super().__init__(features=SCREAMING_SNAKE_CASE__     )
         import jax
         from jaxlib.xla_client import Device

         if isinstance(SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__     ):
           raise ValueError(
               f'Expected {device} to be a `str` not {type(SCREAMING_SNAKE_CASE__     )}, as `jaxlib.xla_extension.Device` '
               """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
               """the device with `str()` to get its string identifier that will be internally mapped """
               """to the actual `jaxlib.xla_extension.Device`."""     )
         _lowerCAmelCase				:     Dict       =    device if isinstance(SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__     ) else str(jax.devices()[0]     )
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           _lowerCAmelCase				:     Union[str, Any]       =    self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()     ):
           logger.warning(
               f'Device with string identifier {self.device} not listed among the available '
               f'devices: {list(DEVICE_MAPPING.keys()     )}, so falling back to the default '
               f'device: {str(jax.devices()[0]     )}.'     )
           _lowerCAmelCase				:     List[Any]       =    str(jax.devices()[0]     )
         _lowerCAmelCase				:     List[str]       =    jnp_array_kwargs




       @staticmethod
       def 							__UpperCamelCase				(   ):
         import jax

         return {str(SCREAMING_SNAKE_CASE__     ): device for device in jax.devices()}




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         import jax
         import jax.numpy as jnp

         if isinstance(SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__     ) and column:
           if all(
               isinstance(SCREAMING_SNAKE_CASE__      ,      jax.Array     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
             return jnp.stack(SCREAMING_SNAKE_CASE__      ,      axis=0     )
         return column




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         import jax
         import jax.numpy as jnp

         if isinstance(SCREAMING_SNAKE_CASE__      ,      (str, bytes, type(SCREAMING_SNAKE_CASE__     ))     ):
           return value
         elif isinstance(SCREAMING_SNAKE_CASE__      ,      (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,      np.character     ):
           return value.tolist()

         _lowerCAmelCase				:     Optional[int]       =    {}

         if isinstance(SCREAMING_SNAKE_CASE__      ,      (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,      np.integer     ):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
             _lowerCAmelCase				:     int       =    {'''dtype''': jnp.intaa}
           else:
             _lowerCAmelCase				:     List[Any]       =    {'''dtype''': jnp.intaa}
         elif isinstance(SCREAMING_SNAKE_CASE__      ,      (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,      np.floating     ):
           _lowerCAmelCase				:     List[Any]       =    {'''dtype''': jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(SCREAMING_SNAKE_CASE__      ,      PIL.Image.Image     ):
             _lowerCAmelCase				:     Tuple       =    np.asarray(SCREAMING_SNAKE_CASE__     )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           _lowerCAmelCase				:     Tuple       =    self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]     ):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(SCREAMING_SNAKE_CASE__      ,      **{**default_dtype, **self.jnp_array_kwargs}     )




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(SCREAMING_SNAKE_CASE__      ,      torch.Tensor     ):
             return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
         if hasattr(SCREAMING_SNAKE_CASE__      ,      """__array__"""     ) and not isinstance(SCREAMING_SNAKE_CASE__      ,      jax.Array     ):
           _lowerCAmelCase				:     int       =    data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(SCREAMING_SNAKE_CASE__      ,      np.ndarray     ):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__     ) for substruct in data_struct]     )
         elif isinstance(SCREAMING_SNAKE_CASE__      ,      (list, tuple)     ):
           return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__     ) for substruct in data_struct]     )
         return self._tensorize(SCREAMING_SNAKE_CASE__     )




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         return map_nested(self._recursive_tensorize      ,      SCREAMING_SNAKE_CASE__      ,      map_list=SCREAMING_SNAKE_CASE__     )




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         _lowerCAmelCase				:     Any       =    self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE__     )
         _lowerCAmelCase				:     Union[str, Any]       =    self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE__     )
         return self.recursive_tensorize(SCREAMING_SNAKE_CASE__     )




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         _lowerCAmelCase				:     int       =    self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE__     )
         _lowerCAmelCase				:     Any       =    self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE__      ,      pa_table.column_names[0]     )
         _lowerCAmelCase				:     Tuple       =    self.recursive_tensorize(SCREAMING_SNAKE_CASE__     )
         _lowerCAmelCase				:     List[str]       =    self._consolidate(SCREAMING_SNAKE_CASE__     )
         return column







       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         _lowerCAmelCase				:     Union[str, Any]       =    self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE__     )
         _lowerCAmelCase				:     Optional[int]       =    self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE__     )
         _lowerCAmelCase				:     List[Any]       =    self.recursive_tensorize(SCREAMING_SNAKE_CASE__     )
         for column_name in batch:
           _lowerCAmelCase				:     Dict       =    self._consolidate(batch[column_name]     )
         return batch









"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict ,   a_     :		Any ,   a_     :		str							)			->      str:
      __SCREAMING_SNAKE_CASE   :int						=  s.rsplit(a_ ,   a_							)
      return new.join(a_							)



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Dict:
      # 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      __lowerCamelCase   (							a_     :		Optional[int]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  {}

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  ['''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:
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  key.replace(f'''{group_key}.''' ,   f'''{group_key}.group.'''							)

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

            if key.endswith('''.w'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.w''' ,   '''.weight''' ,   1							)
            if key.endswith('''.b'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.b''' ,   '''.bias''' ,   1							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  value.float()

      return upgrade



@torch.no_grad()
def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[int] ,   a_     :		Optional[int]=None ,   a_     :		Dict=True							)			->      Union[str, Any]:
      from dall_e import Encoder

      __SCREAMING_SNAKE_CASE   :int						=  Encoder()
      if os.path.exists(a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.load(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.hub.load_state_dict_from_url(a_							)

      if isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  ckpt.state_dict()
      encoder.load_state_dict(a_							)

      if config_path is not None:
            __SCREAMING_SNAKE_CASE   :Any						=  FlavaImageCodebookConfig.from_pretrained(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  FlavaImageCodebookConfig()

      __SCREAMING_SNAKE_CASE   :Tuple						=  FlavaImageCodebook(a_							).eval()
      __SCREAMING_SNAKE_CASE   :List[str]						=  encoder.state_dict()

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  upgrade_state_dict(a_							)
      hf_model.load_state_dict(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hf_model.state_dict()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  count_parameters(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  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__":
  lowerCamelCase_								=		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_								=		parser.parse_args()

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