



'''simple docstring'''





import copy
import os
from typing import Union

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


_UpperCamelCase											=						logging.get_logger(__name__)

_UpperCamelCase											=						{
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""git_vision_model"""




  def __init__( self      :	str			,	_a      :	int=768			,	_a      :	str=3072			,	_a      :	int=12			,	_a      :	Optional[Any]=12			,	_a      :	Dict=3			,	_a      :	Optional[Any]=224			,	_a      :	Tuple=16			,	_a      :	List[str]="quick_gelu"			,	_a      :	Optional[int]=1e-5			,	_a      :	str=0.0			,	_a      :	Tuple=0.02			,	**_a      :	Optional[int]			,	)					->					Tuple:
    super().__init__(**_a   )

    __lowerCamelCase       :       str           = hidden_size
    __lowerCamelCase       :       Union[str, Any]           = intermediate_size
    __lowerCamelCase       :       Optional[Any]           = num_hidden_layers
    __lowerCamelCase       :       str           = num_attention_heads
    __lowerCamelCase       :       List[str]           = num_channels
    __lowerCamelCase       :       int           = patch_size
    __lowerCamelCase       :       Optional[Any]           = image_size
    __lowerCamelCase       :       Dict           = initializer_range
    __lowerCamelCase       :       Union[str, Any]           = attention_dropout
    __lowerCamelCase       :       int           = layer_norm_eps
    __lowerCamelCase       :       List[str]           = hidden_act




  @classmethod
  def 						_lowercase     ( cls      :	Optional[Any]			,	_a      :	Union[str, os.PathLike]			,	**_a      :	str   )					->					"PretrainedConfig":
    cls._set_token_in_kwargs(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       str           = cls.get_config_dict(_a			,	**_a   )

    # get the vision config dict if we are loading from GITConfig
    if config_dict.get('model_type'   ) == "git":
      __lowerCamelCase       :       List[Any]           = config_dict['vision_config']

    if "model_type" in config_dict and hasattr(cls			,	'model_type'   ) and config_dict["model_type"] != cls.model_type:
      logger.warning(
          f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
          f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

    return cls.from_dict(_a			,	**_a   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""git"""




  def __init__( self      :	List[Any]			,	_a      :	Optional[Any]=None			,	_a      :	Optional[int]=3_0522			,	_a      :	Tuple=768			,	_a      :	Dict=6			,	_a      :	str=12			,	_a      :	Tuple=3072			,	_a      :	Tuple="gelu"			,	_a      :	List[str]=0.1			,	_a      :	Dict=0.1			,	_a      :	List[Any]=1024			,	_a      :	str=0.02			,	_a      :	Optional[int]=1e-12			,	_a      :	int=0			,	_a      :	Dict="absolute"			,	_a      :	List[str]=True			,	_a      :	Any=False			,	_a      :	Tuple=101			,	_a      :	List[str]=102			,	_a      :	Optional[Any]=None			,	**_a      :	Dict			,	)					->					Dict:
    super().__init__(bos_token_id=_a			,	eos_token_id=_a			,	pad_token_id=_a			,	**_a   )

    if vision_config is None:
      __lowerCamelCase       :       List[Any]           = {}
      logger.info('vision_config is None. initializing the GitVisionConfig with default values.'   )

    __lowerCamelCase       :       List[str]           = GitVisionConfig(**_a   )
    __lowerCamelCase       :       Union[str, Any]           = vocab_size
    __lowerCamelCase       :       str           = hidden_size
    __lowerCamelCase       :       int           = num_hidden_layers
    __lowerCamelCase       :       int           = num_attention_heads
    __lowerCamelCase       :       Union[str, Any]           = hidden_act
    __lowerCamelCase       :       Optional[int]           = intermediate_size
    __lowerCamelCase       :       List[Any]           = hidden_dropout_prob
    __lowerCamelCase       :       str           = attention_probs_dropout_prob
    __lowerCamelCase       :       List[Any]           = max_position_embeddings
    __lowerCamelCase       :       Any           = initializer_range
    __lowerCamelCase       :       Tuple           = layer_norm_eps
    __lowerCamelCase       :       List[str]           = position_embedding_type
    __lowerCamelCase       :       Tuple           = use_cache
    __lowerCamelCase       :       int           = tie_word_embeddings
    __lowerCamelCase       :       List[Any]           = num_image_with_embedding

    __lowerCamelCase       :       Tuple           = bos_token_id
    __lowerCamelCase       :       Optional[Any]           = eos_token_id




  def 						_lowercase     ( self      :	str   )					->					List[str]:
    __lowerCamelCase       :       Dict           = copy.deepcopy(self.__dict__   )
    __lowerCamelCase       :       Dict           = self.vision_config.to_dict()
    __lowerCamelCase       :       Dict           = self.__class__.model_type
    return output











'''simple docstring'''





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


_UpperCamelCase											=						logging.get_logger(__name__)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=False       )      ->      Tuple:
  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a PyTorch model in Flax, 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

  if not is_sharded:
    __lowerCamelCase       :       str           = os.path.abspath(_lowerCAmelCase       )
    logger.info(F'Loading PyTorch weights from {pt_path}'       )

    __lowerCamelCase       :       Optional[Any]           = torch.load(_lowerCAmelCase       ,map_location='cpu'       )
    logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters.'       )

    __lowerCamelCase       :       Tuple           = convert_pytorch_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    __lowerCamelCase       :       Union[str, Any]           = convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  return flax_state_dict

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      (Tuple[str], np.ndarray):

  def is_key_or_prefix_key_in_dict(_lowerCAmelCase       ) -> bool:
    return len(set(_lowerCAmelCase       ) & {key, (model_prefix,) + key}       ) > 0

  # layer norm
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  __lowerCamelCase       :       int           = pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[Any]           = pt_tensor.transpose(2       ,3       ,1       ,0       )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  __lowerCamelCase       :       Optional[Any]           = pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    __lowerCamelCase       :       Dict           = pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  __lowerCamelCase       :       int           = pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  __lowerCamelCase       :       Optional[int]           = None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    __lowerCamelCase       :       Union[str, Any]           = pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    __lowerCamelCase       :       Tuple           = pt_tuple_key[-2] + '_v'
  if name is not None:
    __lowerCamelCase       :       Any           = pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  # convert pytorch tensor to numpy
  __lowerCamelCase       :       Optional[int]           = {k: v.numpy() for k, v in pt_state_dict.items()}

  __lowerCamelCase       :       str           = flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    __lowerCamelCase       :       List[Any]           = flax_model.params['params']
  else:
    __lowerCamelCase       :       List[str]           = flax_model.params
  __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    __lowerCamelCase       :       Optional[int]           = flatten_dict(flax_model.params['batch_stats']       )
    random_flax_state_dict.update(_lowerCAmelCase       )

  __lowerCamelCase       :       str           = {}

  __lowerCamelCase       :       Union[str, Any]           = (model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )
  __lowerCamelCase       :       List[str]           = (model_prefix in flax_model_params) and (
      model_prefix not in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    __lowerCamelCase       :       Any           = tuple(pt_key.split('.'       )       )

    # remove base model prefix if necessary
    __lowerCamelCase       :       Any           = pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      __lowerCamelCase       :       Optional[Any]           = pt_tuple_key[1:]

    # Correctly rename weight parameters
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = rename_key_and_reshape_tensor(
        _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )

    # add model prefix if necessary
    __lowerCamelCase       :       Dict           = (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Dict           = (model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
            F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        __lowerCamelCase       :       List[Any]           = jnp.asarray(_lowerCAmelCase       )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
        continue

      # also add unexpected weight so that warning is thrown
      __lowerCamelCase       :       List[Any]           = jnp.asarray(_lowerCAmelCase       )

    else:
      # also add unexpected weight so that warning is thrown
      __lowerCamelCase       :       str           = jnp.asarray(_lowerCAmelCase       )

  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Any:
  import torch

  # Load the index
  __lowerCamelCase       :       Optional[int]           = {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    __lowerCamelCase       :       Dict           = torch.load(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {k: v.numpy() for k, v in pt_state_dict.items()}

    __lowerCamelCase       :       Optional[Any]           = flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      __lowerCamelCase       :       str           = flax_model.params['params']

      __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']       )       )
    else:
      __lowerCamelCase       :       Dict           = flax_model.params
      __lowerCamelCase       :       Optional[int]           = flatten_dict(_lowerCAmelCase       )

    __lowerCamelCase       :       List[Any]           = (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    __lowerCamelCase       :       List[str]           = (model_prefix in flax_model_params) and (
        model_prefix not in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      __lowerCamelCase       :       Optional[Any]           = tuple(pt_key.split('.'       )       )

      # remove base model prefix if necessary
      __lowerCamelCase       :       List[Any]           = pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        __lowerCamelCase       :       Optional[int]           = pt_tuple_key[1:]

      # Correctly rename weight parameters
      __lowerCamelCase       ,__lowerCamelCase       :       Any           = rename_key_and_reshape_tensor(
          _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
      # add model prefix if necessary
      __lowerCamelCase       :       Tuple           = (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        __lowerCamelCase       :       Optional[Any]           = (model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
              F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'       )

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          __lowerCamelCase       :       int           = jnp.asarray(_lowerCAmelCase       )
          continue
        if "var" in flax_key[-1]:
          __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
          continue

        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Optional[int]           = jnp.asarray(_lowerCAmelCase       )

      else:
        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  __lowerCamelCase       :       Union[str, Any]           = os.path.abspath(_lowerCAmelCase       )
  logger.info(F'Loading Flax weights from {flax_checkpoint_path}'       )

  # import correct flax class
  __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,'Flax' + model.__class__.__name__       )

  # load flax weight dict
  with open(_lowerCAmelCase       ,'rb'       ) as state_f:
    try:
      __lowerCamelCase       :       Tuple           = from_bytes(_lowerCAmelCase       ,state_f.read()       )
    except UnpicklingError:
      raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '       )

  return load_flax_weights_in_pytorch_model(_lowerCAmelCase       ,_lowerCAmelCase       )


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a 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       :       Any           = flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase       : x.dtype == jnp.bfloataa       ,_lowerCAmelCase       )       ).values()
  if any(_lowerCAmelCase       ):
    # convert all weights to fp32 if the 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       :       Dict           = jax.tree_util.tree_map(
        lambda _lowerCAmelCase       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = flatten_dict(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = pt_model.state_dict()

  __lowerCamelCase       :       Union[str, Any]           = (pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split('.'       )[0] for k in pt_model_dict.keys()}
  )
  __lowerCamelCase       :       Tuple           = (pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split('.'       )[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  __lowerCamelCase       :       Any           = []
  __lowerCamelCase       :       Union[str, Any]           = set(pt_model_dict.keys()       )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __lowerCamelCase       :       List[str]           = flax_key_tuple[0] == pt_model.base_model_prefix
    __lowerCamelCase       :       Dict           = '.'.join((pt_model.base_model_prefix,) + flax_key_tuple       ) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      __lowerCamelCase       :       List[Any]           = flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Optional[Any]           = (pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCAmelCase       ) not in pt_model_dict:
      # conv layer
      __lowerCamelCase       :       Tuple           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       Tuple           = jnp.transpose(_lowerCAmelCase       ,(3, 2, 0, 1)       )
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase       ) not in pt_model_dict:
      # linear layer
      __lowerCamelCase       :       Dict           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       str           = flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      __lowerCamelCase       :       Optional[Any]           = flax_key_tuple[:-1] + ('weight',)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      __lowerCamelCase       :       str           = flax_key_tuple[:-1] + ('running_mean',)
    elif "var" in flax_key_tuple[-1]:
      __lowerCamelCase       :       int           = flax_key_tuple[:-1] + ('running_var',)

    if "batch_stats" in flax_state:
      __lowerCamelCase       :       Tuple           = '.'.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
    else:
      __lowerCamelCase       :       Optional[int]           = '.'.join(_lowerCAmelCase       )

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    __lowerCamelCase       :       Tuple           = {}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      __lowerCamelCase       :       str           = key.split('.'       )
      __lowerCamelCase       :       Tuple           = None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        __lowerCamelCase       :       Any           = key_components[-2] + '_g'
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        __lowerCamelCase       :       Tuple           = key_components[-2] + '_v'
      if name is not None:
        __lowerCamelCase       :       Optional[int]           = key_components[:-3] + [name]
        __lowerCamelCase       :       Union[str, Any]           = '.'.join(_lowerCAmelCase       )
        __lowerCamelCase       :       Optional[int]           = key

    if flax_key in special_pt_names:
      __lowerCamelCase       :       int           = special_pt_names[flax_key]

    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       :       Tuple           = np.asarray(_lowerCAmelCase       ) if not isinstance(_lowerCAmelCase       ,np.ndarray       ) else flax_tensor
        __lowerCamelCase       :       List[str]           = torch.from_numpy(_lowerCAmelCase       )
        # remove from missing keys
        missing_keys.remove(_lowerCAmelCase       )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_lowerCAmelCase       )

  pt_model.load_state_dict(_lowerCAmelCase       )

  # re-transform missing_keys to list
  __lowerCamelCase       :       int           = list(_lowerCAmelCase       )

  if len(_lowerCAmelCase       ) > 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).'       )
  else:
    logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'       )
  if len(_lowerCAmelCase       ) > 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.'       )
  else:
    logger.warning(
        F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
        'If your task is similar to the task the model of the checkpoint was trained on, '
        F'you can already use {pt_model.__class__.__name__} for predictions without further training.'       )

  return pt_model






