

"""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


_A							=  logging.get_logger(__name__)




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=False    )	-> Optional[Any]:
      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:
            UpperCAmelCase__			:							Tuple							=    os.path.abspath(lowerCAmelCase    )
            logger.info(F"""Loading PyTorch weights from {pt_path}"""    )

            UpperCAmelCase__			:							Any							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )
            logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters."""    )

            UpperCAmelCase__			:							str							=    convert_pytorch_state_dict_to_flax(lowerCAmelCase			,	lowerCAmelCase    )
      else:
            # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
            UpperCAmelCase__			:							Optional[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
      UpperCAmelCase__			:							Any							=    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
      UpperCAmelCase__			:							Tuple							=    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
      UpperCAmelCase__			:							Any							=    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
      UpperCAmelCase__			:							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
      UpperCAmelCase__			:							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    ):
            UpperCAmelCase__			:							Union[str, Any]							=    pt_tensor.transpose(2			,	3			,	1			,	0    )
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      UpperCAmelCase__			:							int							=    pt_tuple_key[:-1] + ("""kernel""",)
      if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCAmelCase    ):
            UpperCAmelCase__			:							str							=    pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

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

      # old PyTorch layer norm bias
      UpperCAmelCase__			:							List[str]							=    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
      UpperCAmelCase__			:							Optional[int]							=    None
      if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
            UpperCAmelCase__			:							int							=    pt_tuple_key[-2] + """_g"""
      elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
            UpperCAmelCase__			:							Optional[int]							=    pt_tuple_key[-2] + """_v"""
      if name is not None:
            UpperCAmelCase__			:							List[str]							=    pt_tuple_key[:-3] + (name,)
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Dict:
      # convert pytorch tensor to numpy
      UpperCAmelCase__			:							Optional[int]							=    {k: v.numpy() for k, v in pt_state_dict.items()}

      UpperCAmelCase__			:							Optional[Any]							=    flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers
      if "params" in flax_model.params:
            UpperCAmelCase__			:							Any							=    flax_model.params["""params"""]
      else:
            UpperCAmelCase__			:							Optional[Any]							=    flax_model.params
      UpperCAmelCase__			:							Tuple							=    flatten_dict(lowerCAmelCase    )

      # add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
            UpperCAmelCase__			:							Dict							=    flatten_dict(flax_model.params["""batch_stats"""]    )
            random_flax_state_dict.update(lowerCAmelCase    )

      UpperCAmelCase__			:							List[str]							=    {}

      UpperCAmelCase__			:							str							=    (model_prefix not in flax_model_params) and (
          model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
      )
      UpperCAmelCase__			:							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():
            UpperCAmelCase__			:							Optional[Any]							=    tuple(pt_key.split("""."""    )    )

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

            # Correctly rename weight parameters
            UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    rename_key_and_reshape_tensor(
                lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )

            # add model prefix if necessary
            UpperCAmelCase__			:							str							=    (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCAmelCase__			:							str							=    (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]:
                        UpperCAmelCase__			:							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
                  UpperCAmelCase__			:							Union[str, Any]							=    jnp.asarray(lowerCAmelCase    )

            else:
                  # also add unexpected weight so that warning is thrown
                  UpperCAmelCase__			:							Any							=    jnp.asarray(lowerCAmelCase    )

      return unflatten_dict(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      import torch

      # Load the index
      UpperCAmelCase__			:							List[str]							=    {}
      for shard_file in shard_filenames:
            # load using msgpack utils
            UpperCAmelCase__			:							Tuple							=    torch.load(lowerCAmelCase    )
            UpperCAmelCase__			:							Optional[int]							=    {k: v.numpy() for k, v in pt_state_dict.items()}

            UpperCAmelCase__			:							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:
                  UpperCAmelCase__			:							List[Any]							=    flax_model.params["""params"""]

                  UpperCAmelCase__			:							int							=    flatten_dict(lowerCAmelCase    )
                  random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]    )    )
            else:
                  UpperCAmelCase__			:							Any							=    flax_model.params
                  UpperCAmelCase__			:							List[Any]							=    flatten_dict(lowerCAmelCase    )

            UpperCAmelCase__			:							int							=    (model_prefix not in flax_model_params) and (
                model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
            )
            UpperCAmelCase__			:							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():
                  UpperCAmelCase__			:							Optional[int]							=    tuple(pt_key.split("""."""    )    )

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

                  # Correctly rename weight parameters
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Union[str, Any]							=    rename_key_and_reshape_tensor(
                      lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )
                  # add model prefix if necessary
                  UpperCAmelCase__			:							Tuple							=    (model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        UpperCAmelCase__			:							Optional[int]							=    (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]:
                              UpperCAmelCase__			:							Any							=    jnp.asarray(lowerCAmelCase    )
                              continue
                        if "var" in flax_key[-1]:
                              UpperCAmelCase__			:							List[str]							=    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
                        UpperCAmelCase__			:							Optional[int]							=    jnp.asarray(lowerCAmelCase    )

                  else:
                        # also add unexpected weight so that warning is thrown
                        UpperCAmelCase__			:							List[str]							=    jnp.asarray(lowerCAmelCase    )
      return unflatten_dict(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							Optional[Any]							=    os.path.abspath(lowerCAmelCase    )
      logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""    )

      # import correct flax class
      UpperCAmelCase__			:							Dict							=    getattr(lowerCAmelCase			,	"""Flax""" + model.__class__.__name__    )

      # load flax weight dict
      with open(lowerCAmelCase			,	"""rb"""    ) as state_f:
            try:
                  UpperCAmelCase__			:							Any							=    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
      UpperCAmelCase__			:							List[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."""    )
            UpperCAmelCase__			:							Union[str, Any]							=    jax.tree_util.tree_map(
                lambda lowerCAmelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params			,	lowerCAmelCase    )

      UpperCAmelCase__			:							int							=    flatten_dict(lowerCAmelCase    )
      UpperCAmelCase__			:							str							=    pt_model.state_dict()

      UpperCAmelCase__			:							Optional[int]							=    (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()}
      )
      UpperCAmelCase__			:							str							=    (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
      UpperCAmelCase__			:							List[str]							=    []
      UpperCAmelCase__			:							Optional[int]							=    set(pt_model_dict.keys()    )

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            UpperCAmelCase__			:							Dict							=    flax_key_tuple[0] == pt_model.base_model_prefix
            UpperCAmelCase__			:							List[str]							=    """.""".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:
                  UpperCAmelCase__			:							int							=    flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCAmelCase__			:							List[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
                  UpperCAmelCase__			:							Union[str, Any]							=    flax_key_tuple[:-1] + ("""weight""",)
                  UpperCAmelCase__			:							str							=    jnp.transpose(lowerCAmelCase			,	(3, 2, 0, 1)    )
            elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase    ) not in pt_model_dict:
                  # linear layer
                  UpperCAmelCase__			:							Optional[Any]							=    flax_key_tuple[:-1] + ("""weight""",)
                  UpperCAmelCase__			:							Union[str, Any]							=    flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  UpperCAmelCase__			:							Any							=    flax_key_tuple[:-1] + ("""weight""",)

            # adding batch stats from flax batch norm to pt
            elif "mean" in flax_key_tuple[-1]:
                  UpperCAmelCase__			:							Any							=    flax_key_tuple[:-1] + ("""running_mean""",)
            elif "var" in flax_key_tuple[-1]:
                  UpperCAmelCase__			:							Dict							=    flax_key_tuple[:-1] + ("""running_var""",)

            if "batch_stats" in flax_state:
                  UpperCAmelCase__			:							int							=    """.""".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
            else:
                  UpperCAmelCase__			:							Optional[Any]							=    """.""".join(lowerCAmelCase    )

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

            if flax_key in special_pt_names:
                  UpperCAmelCase__			:							Optional[Any]							=    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
                        UpperCAmelCase__			:							Dict							=    np.asarray(lowerCAmelCase    ) if not isinstance(lowerCAmelCase			,	np.ndarray    ) else flax_tensor
                        UpperCAmelCase__			:							Optional[int]							=    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
      UpperCAmelCase__			:							Optional[Any]							=    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




"""simple docstring"""
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""    )

      UpperCAmelCase__			:							Dict							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"
      UpperCAmelCase__			:							Any							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"

      UpperCAmelCase__			:							Optional[Any]							=    max(len(lowerCAmelCase    )			,	len(lowerCAmelCase    )    )

      return "0b" + "".join(
          str(int(char_a != char_b    )    )
          for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase    )			,	b_binary.zfill(lowerCAmelCase    )    )    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

