

"""simple docstring"""




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


SCREAMING_SNAKE_CASE_     :			List[str]	  =			logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int:




   """simple docstring"""
   try:
      with open(A__	,    "rb"   ) as flax_state_f:
         lowerCamelCase__						=			from_bytes(A__	,    flax_state_f.read()   )
   except UnpicklingError as e:
      try:
         with open(A__   ) as f:
            if f.read().startswith("version"   ):
               raise OSError(
                   "You seem to have cloned a repository without having git-lfs installed. Please"
                   " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
                   " folder you cloned."   )
            else:
               raise ValueError from e
      except (UnicodeDecodeError, ValueError):
         raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '   )

   return load_flax_weights_in_pytorch_model(A__	,    A__   )



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int:




   """simple docstring"""

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
          " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
          " instructions."   )
      raise

   # check if we have bf16 weights
   lowerCamelCase__						=			flatten_dict(jax.tree_util.tree_map(lambda A__   : x.dtype == jnp.bfloataa	,    A__   )   ).values()
   if any(A__   ):
      # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

      # and bf16 is not fully supported in PT yet.
      logger.warning(
          "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
          "before loading those in PyTorch model."   )
      lowerCamelCase__						=			jax.tree_util.tree_map(
          lambda A__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params	,    A__   )

   lowerCamelCase__						=			""

   lowerCamelCase__						=			flatten_dict(A__	,    sep="."   )
   lowerCamelCase__						=			pt_model.state_dict()

   # keep track of unexpected & missing keys
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			set(pt_model_dict.keys()   )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      lowerCamelCase__						=			flax_key_tuple.split("."   )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         lowerCamelCase__						=			flax_key_tuple_array[:-1] + ["weight"]
         lowerCamelCase__						=			jnp.transpose(A__	,    (3, 2, 0, 1)   )
      elif flax_key_tuple_array[-1] == "kernel":
         lowerCamelCase__						=			flax_key_tuple_array[:-1] + ["weight"]
         lowerCamelCase__						=			flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         lowerCamelCase__						=			flax_key_tuple_array[:-1] + ["weight"]

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(A__   ):
            lowerCamelCase__						=			(
                flax_key_tuple_string.replace("_0"	,    ".0"   )
                .replace("_1"	,    ".1"   )
                .replace("_2"	,    ".2"   )
                .replace("_3"	,    ".3"   )
                .replace("_4"	,    ".4"   )
                .replace("_5"	,    ".5"   )
                .replace("_6"	,    ".6"   )
                .replace("_7"	,    ".7"   )
                .replace("_8"	,    ".8"   )
                .replace("_9"	,    ".9"   )
            )

      lowerCamelCase__						=			".".join(A__   )

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'   )
         else:
            # add weight to pytorch dict
            lowerCamelCase__						=			np.asarray(A__   ) if not isinstance(A__	,    np.ndarray   ) else flax_tensor
            lowerCamelCase__						=			torch.from_numpy(A__   )
            # remove from missing keys
            missing_keys.remove(A__   )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(A__   )

   pt_model.load_state_dict(A__   )

   # re-transform missing_keys to list
   lowerCamelCase__						=			list(A__   )

   if len(A__   ) > 0:
      logger.warning(
          "Some weights of the Flax model were not used when initializing the PyTorch model"
          f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
          f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
          " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
          f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
          " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
          " FlaxBertForSequenceClassification model)."   )
   if len(A__   ) > 0:
      logger.warning(
          f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
          f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
          " use it for predictions and inference."   )

   return pt_model







"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_     :			Dict	  =			logging.get_logger(__name__)


class 		_A       (			__a					):
      __a								=	'encoder-decoder'
      __a								=	True





      def __init__(  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         super().__init__(**SCREAMING_SNAKE_CASE__		)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         lowerCamelCase__						=			kwargs.pop("encoder"		)
         lowerCamelCase__						=			encoder_config.pop("model_type"		)
         lowerCamelCase__						=			kwargs.pop("decoder"		)
         lowerCamelCase__						=			decoder_config.pop("model_type"		)

         from ..auto.configuration_auto import AutoConfig

         lowerCamelCase__						=			AutoConfig.for_model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			AutoConfig.for_model(SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			True





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					PretrainedConfig:
         logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
         lowerCamelCase__						=			True
         lowerCamelCase__						=			True

         return cls(encoder=encoder_config.to_dict()    ,						decoder=decoder_config.to_dict()    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			copy.deepcopy(self.__dict__		)
         lowerCamelCase__						=			self.encoder.to_dict()
         lowerCamelCase__						=			self.decoder.to_dict()
         lowerCamelCase__						=			self.__class__.model_type
         return output




