



"""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


lowerCamelCase  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=False ):
      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_     					=				os.path.abspath(__UpperCAmelCase )
            logger.info(f"""Loading PyTorch weights from {pt_path}""" )

            UpperCAmelCase_     					=				torch.load(__UpperCAmelCase      ,     map_location="cpu" )
            logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )

            UpperCAmelCase_     					=				convert_pytorch_state_dict_to_flax(__UpperCAmelCase      ,     __UpperCAmelCase )
      else:
            # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
            UpperCAmelCase_     					=				convert_pytorch_sharded_state_dict_to_flax(__UpperCAmelCase      ,     __UpperCAmelCase )
      return flax_state_dict
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     ):

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

      # layer norm
      UpperCAmelCase_     					=				pt_tuple_key[:-1] + ("scale",)
      if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__UpperCAmelCase ):
            return renamed_pt_tuple_key, pt_tensor

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

      # batch norm layer var
      UpperCAmelCase_     					=				pt_tuple_key[:-1] + ("var",)
      if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__UpperCAmelCase ):
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      UpperCAmelCase_     					=				pt_tuple_key[:-1] + ("embedding",)
      if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__UpperCAmelCase ):
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      UpperCAmelCase_     					=				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(__UpperCAmelCase ):
            UpperCAmelCase_     					=				pt_tensor.transpose(2      ,     3      ,     1      ,     0 )
            return renamed_pt_tuple_key, pt_tensor

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

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

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

      return pt_tuple_key, pt_tensor
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      # convert pytorch tensor to numpy
      UpperCAmelCase_     					=				{k: v.numpy() for k, v in pt_state_dict.items()}

      UpperCAmelCase_     					=				flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers
      if "params" in flax_model.params:
            UpperCAmelCase_     					=				flax_model.params["params"]
      else:
            UpperCAmelCase_     					=				flax_model.params
      UpperCAmelCase_     					=				flatten_dict(__UpperCAmelCase )

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

      UpperCAmelCase_     					=				{}

      UpperCAmelCase_     					=				(model_prefix not in flax_model_params) and (
          model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
      )
      UpperCAmelCase_     					=				(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_     					=				tuple(pt_key.split("." ) )

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

            # Correctly rename weight parameters
            UpperCAmelCase_					,       UpperCAmelCase_     					=				rename_key_and_reshape_tensor(
                __UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase )

            # add model prefix if necessary
            UpperCAmelCase_     					=				(model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCAmelCase_     					=				(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_     					=				jnp.asarray(__UpperCAmelCase )
                        continue
                  # remove num_batches_tracked key
                  if "num_batches_tracked" in flax_key[-1]:
                        flax_state_dict.pop(__UpperCAmelCase      ,     __UpperCAmelCase )
                        continue

                  # also add unexpected weight so that warning is thrown
                  UpperCAmelCase_     					=				jnp.asarray(__UpperCAmelCase )

            else:
                  # also add unexpected weight so that warning is thrown
                  UpperCAmelCase_     					=				jnp.asarray(__UpperCAmelCase )

      return unflatten_dict(__UpperCAmelCase )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      import torch

      # Load the index
      UpperCAmelCase_     					=				{}
      for shard_file in shard_filenames:
            # load using msgpack utils
            UpperCAmelCase_     					=				torch.load(__UpperCAmelCase )
            UpperCAmelCase_     					=				{k: v.numpy() for k, v in pt_state_dict.items()}

            UpperCAmelCase_     					=				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_     					=				flax_model.params["params"]

                  UpperCAmelCase_     					=				flatten_dict(__UpperCAmelCase )
                  random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"] ) )
            else:
                  UpperCAmelCase_     					=				flax_model.params
                  UpperCAmelCase_     					=				flatten_dict(__UpperCAmelCase )

            UpperCAmelCase_     					=				(model_prefix not in flax_model_params) and (
                model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
            )
            UpperCAmelCase_     					=				(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_     					=				tuple(pt_key.split("." ) )

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

                  # Correctly rename weight parameters
                  UpperCAmelCase_					,       UpperCAmelCase_     					=				rename_key_and_reshape_tensor(
                      __UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase )
                  # add model prefix if necessary
                  UpperCAmelCase_     					=				(model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        UpperCAmelCase_     					=				(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_     					=				jnp.asarray(__UpperCAmelCase )
                              continue
                        if "var" in flax_key[-1]:
                              UpperCAmelCase_     					=				jnp.asarray(__UpperCAmelCase )
                              continue
                        # remove num_batches_tracked key
                        if "num_batches_tracked" in flax_key[-1]:
                              flax_state_dict.pop(__UpperCAmelCase      ,     __UpperCAmelCase )
                              continue

                        # also add unexpected weight so that warning is thrown
                        UpperCAmelCase_     					=				jnp.asarray(__UpperCAmelCase )

                  else:
                        # also add unexpected weight so that warning is thrown
                        UpperCAmelCase_     					=				jnp.asarray(__UpperCAmelCase )
      return unflatten_dict(__UpperCAmelCase )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				os.path.abspath(__UpperCAmelCase )
      logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )

      # import correct flax class
      UpperCAmelCase_     					=				getattr(__UpperCAmelCase      ,     "Flax" + model.__class__.__name__ )

      # load flax weight dict
      with open(__UpperCAmelCase      ,     "rb" ) as state_f:
            try:
                  UpperCAmelCase_     					=				from_bytes(__UpperCAmelCase      ,     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(__UpperCAmelCase      ,     __UpperCAmelCase )


def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):

      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_     					=				flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase__ : x.dtype == jnp.bfloataa      ,     __UpperCAmelCase ) ).values()
      if any(__UpperCAmelCase ):
            # 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_     					=				jax.tree_util.tree_map(
                lambda lowerCAmelCase__ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params      ,     __UpperCAmelCase )

      UpperCAmelCase_     					=				flatten_dict(__UpperCAmelCase )
      UpperCAmelCase_     					=				pt_model.state_dict()

      UpperCAmelCase_     					=				(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_     					=				(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_     					=				[]
      UpperCAmelCase_     					=				set(pt_model_dict.keys() )

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            UpperCAmelCase_     					=				flax_key_tuple[0] == pt_model.base_model_prefix
            UpperCAmelCase_     					=				".".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_     					=				flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCAmelCase_     					=				(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(__UpperCAmelCase ) not in pt_model_dict:
                  # conv layer
                  UpperCAmelCase_     					=				flax_key_tuple[:-1] + ("weight",)
                  UpperCAmelCase_     					=				jnp.transpose(__UpperCAmelCase      ,     (3, 2, 0, 1) )
            elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCAmelCase ) not in pt_model_dict:
                  # linear layer
                  UpperCAmelCase_     					=				flax_key_tuple[:-1] + ("weight",)
                  UpperCAmelCase_     					=				flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  UpperCAmelCase_     					=				flax_key_tuple[:-1] + ("weight",)

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

            if "batch_stats" in flax_state:
                  UpperCAmelCase_     					=				".".join(flax_key_tuple[1:] )  # Remove the params/batch_stats header
            else:
                  UpperCAmelCase_     					=				".".join(__UpperCAmelCase )

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

            if flax_key in special_pt_names:
                  UpperCAmelCase_     					=				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_     					=				np.asarray(__UpperCAmelCase ) if not isinstance(__UpperCAmelCase      ,     np.ndarray ) else flax_tensor
                        UpperCAmelCase_     					=				torch.from_numpy(__UpperCAmelCase )
                        # remove from missing keys
                        missing_keys.remove(__UpperCAmelCase )
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(__UpperCAmelCase )

      pt_model.load_state_dict(__UpperCAmelCase )

      # re-transform missing_keys to list
      UpperCAmelCase_     					=				list(__UpperCAmelCase )

      if len(__UpperCAmelCase ) > 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(__UpperCAmelCase ) > 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









import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

 a__												= (
     ('''layer.''', '''layer_'''),
     ('''word_embeddings.weight''', '''word_embeddings'''),
     ('''position_embeddings.weight''', '''position_embeddings'''),
     ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
     ('''.''', '''/'''),
     ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
     ('''LayerNorm/bias''', '''LayerNorm/beta'''),
     ('''weight''', '''kernel'''),
 )

 if not os.path.isdir(__UpperCAmelCase       ):
  os.makedirs(__UpperCAmelCase       )

 a__												= model.state_dict()

 def to_tf_var_name(__UpperCAmelCase       ):
  for patt, repl in iter(__UpperCAmelCase       ):
   a__												= name.replace(__UpperCAmelCase							,  __UpperCAmelCase       )
  return f"bert/{name}"

 def create_tf_var(__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
  a__												= tf.dtypes.as_dtype(tensor.dtype       )
  a__												= tf.get_variable(dtype=__UpperCAmelCase							,  shape=tensor.shape							,  name=__UpperCAmelCase							,  initializer=tf.zeros_initializer()       )
  session.run(tf.variables_initializer([tf_var]       )       )
  session.run(__UpperCAmelCase       )
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   a__												= to_tf_var_name(__UpperCAmelCase       )
   a__												= state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose       ):
    a__												= torch_tensor.T
   a__												= create_tf_var(tensor=__UpperCAmelCase							,  name=__UpperCAmelCase							,  session=__UpperCAmelCase       )
   tf.keras.backend.set_value(__UpperCAmelCase							,  __UpperCAmelCase       )
   a__												= session.run(__UpperCAmelCase       )
   print(f"Successfully created {tf_name}: {np.allclose(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )

  a__												= tf.train.Saver(tf.trainable_variables()       )
  saver.save(__UpperCAmelCase							,  os.path.join(__UpperCAmelCase							,  model_name.replace('''-'''							,  '''_'''       ) + '''.ckpt'''       )       )






def     __a					(		__UpperCAmelCase=None       ):
 a__												= argparse.ArgumentParser()
 parser.add_argument('''--model_name'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''model name e.g. bert-base-uncased'''       )
 parser.add_argument(
     '''--cache_dir'''							,  type=__UpperCAmelCase							,  default=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory containing pytorch model'''       )
 parser.add_argument('''--pytorch_model_path'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''/path/to/<pytorch-model-name>.bin'''       )
 parser.add_argument('''--tf_cache_dir'''							,  type=__UpperCAmelCase							,  required=__UpperCAmelCase							,  help='''Directory in which to save tensorflow model'''       )
 a__												= parser.parse_args(__UpperCAmelCase       )

 a__												= BertModel.from_pretrained(
     pretrained_model_name_or_path=args.model_name							,  state_dict=torch.load(args.pytorch_model_path       )							,  cache_dir=args.cache_dir							,  )

 convert_pytorch_checkpoint_to_tf(model=__UpperCAmelCase							,  ckpt_dir=args.tf_cache_dir							,  model_name=args.model_name       )


if __name__ == "__main__":
     main()





