


import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


UpperCAmelCase_      : Optional[int]  					=       logging.getLogger(__name__)

UpperCAmelCase_      : Dict  					=       tf.data.AUTOTUNE







def        SCREAMING_SNAKE_CASE_						(			)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  argparse.ArgumentParser(description='Train a masked language model on TPU.'				)
       parser.add_argument(
           '--pretrained_model_config'  ,    type=__A  ,    default='roberta-base'  ,    help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'  ,    )
       parser.add_argument(
           '--tokenizer'  ,    type=__A  ,    default='unigram-tokenizer-wikitext'  ,    help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'  ,    )

       parser.add_argument(
           '--per_replica_batch_size'  ,    type=__A  ,    default=8  ,    help='Batch size per TPU core.'  ,    )

       parser.add_argument(
           '--no_tpu'  ,    action='store_true'  ,    help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'  ,    )

       parser.add_argument(
           '--tpu_name'  ,    type=__A  ,    help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'  ,    default='local'  ,    )

       parser.add_argument(
           '--tpu_zone'  ,    type=__A  ,    help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'  ,    )

       parser.add_argument(
           '--gcp_project'  ,    type=__A  ,    help='Google cloud project name. Only used for non-Colab TPU nodes.'				)

       parser.add_argument(
           '--bfloat16'  ,    action='store_true'  ,    help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'  ,    )

       parser.add_argument(
           '--train_dataset'  ,    type=__A  ,    help='Path to training dataset to load. If the path begins with `gs://`'
           ' then the dataset will be loaded from a Google Cloud Storage bucket.'  ,    )

       parser.add_argument(
           '--shuffle_buffer_size'  ,    type=__A  ,    default=2**18  ,    help='Size of the shuffle buffer (in samples)'  ,    )

       parser.add_argument(
           '--eval_dataset'  ,    type=__A  ,    help='Path to evaluation dataset to load. If the path begins with `gs://`'
           ' then the dataset will be loaded from a Google Cloud Storage bucket.'  ,    )

       parser.add_argument(
           '--num_epochs'  ,    type=__A  ,    default=1  ,    help='Number of epochs to train for.'  ,    )

       parser.add_argument(
           '--learning_rate'  ,    type=__A  ,    default=1e-4  ,    help='Learning rate to use for training.'  ,    )

       parser.add_argument(
           '--weight_decay_rate'  ,    type=__A  ,    default=1e-3  ,    help='Weight decay rate to use for training.'  ,    )

       parser.add_argument(
           '--max_length'  ,    type=__A  ,    default=5_12  ,    help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'  ,    )

       parser.add_argument(
           '--mlm_probability'  ,    type=__A  ,    default=0.15  ,    help='Fraction of tokens to mask during training.'  ,    )

       parser.add_argument('--output_dir'  ,    type=__A  ,    required=__A  ,    help='Path to save model checkpoints to.'				)
       parser.add_argument('--hub_model_id'  ,    type=__A  ,    help='Model ID to upload to on the Hugging Face Hub.'				)

       a_					:		Optional[int]							=  parser.parse_args()
       return args







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]				)	->		List[Any]:

       """simple docstring"""

       try:
              if args.tpu_name:
                     a_					:		List[str]							=  tf.distribute.cluster_resolver.TPUClusterResolver(
                         args.tpu_name  ,    zone=args.tpu_zone  ,    project=args.gcp_project				)
              else:
                     a_					:		List[Any]							=  tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
              raise RuntimeError(
                  'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
                  '--gcp_project. When running on a TPU VM, use --tpu_name local.'				)

       tf.config.experimental_connect_to_cluster(__A				)
       tf.tpu.experimental.initialize_tpu_system(__A				)

       return tpu







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		List[Any]:

       """simple docstring"""

       a_					:		Dict							=  0
       for file in file_list:
              a_					:		Union[str, Any]							=  file.split('/'				)[-1]
              a_					:		Tuple							=  re.search(R'-\d+-(\d+)\.tfrecord'  ,    __A				).group(1				)
              a_					:		List[str]							=  int(__A				)
              num_samples += sample_count

       return num_samples







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Dict  ,    __A :			str  ,    __A :			str  ,    __A :			Optional[int]  ,    __A :			str=None				)	->		List[Any]:

       """simple docstring"""

       a_					:		Union[str, Any]							=  count_samples(__A				)
       a_					:		int							=  tf.data.Dataset.from_tensor_slices(__A				)
       if shuffle:
              a_					:		Optional[int]							=  dataset.shuffle(len(__A				)				)
       a_					:		Tuple							=  tf.data.TFRecordDataset(__A  ,    num_parallel_reads=__A				)
       # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
       a_					:		Optional[Any]							=  dataset.apply(tf.data.experimental.assert_cardinality(__A				)				)
       a_					:		Dict							=  dataset.map(__A  ,    num_parallel_calls=__A				)
       if shuffle:
              assert shuffle_buffer_size is not None
              a_					:		Dict							=  dataset.shuffle(args.shuffle_buffer_size				)
       a_					:		Dict							=  dataset.batch(__A  ,    drop_remainder=__A				)
       a_					:		Tuple							=  dataset.map(__A  ,    num_parallel_calls=__A				)
       a_					:		Tuple							=  dataset.prefetch(__A				)
       return dataset







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Tuple:

       """simple docstring"""

       if not args.no_tpu:
              a_					:		Dict							=  initialize_tpu(__A				)
              a_					:		int							=  tf.distribute.TPUStrategy(__A				)
       else:
              a_					:		int							=  tf.distribute.OneDeviceStrategy(device='/gpu:0'				)

       if args.bfloataa:
              tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'				)

       a_					:		Tuple							=  AutoTokenizer.from_pretrained(args.tokenizer				)
       a_					:		List[str]							=  AutoConfig.from_pretrained(args.pretrained_model_config				)
       a_					:		Any							=  tokenizer.vocab_size

       a_					:		List[Any]							=  tf.io.gfile.glob(os.path.join(args.train_dataset  ,    '*.tfrecord'				)				)
       if not training_records:
              raise ValueError(F"""No .tfrecord files found in {args.train_dataset}."""				)
       a_					:		Any							=  tf.io.gfile.glob(os.path.join(args.eval_dataset  ,    '*.tfrecord'				)				)
       if not eval_records:
              raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}."""				)

       a_					:		List[Any]							=  count_samples(__A				)

       a_					:		str							=  num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
       a_					:		Union[str, Any]							=  steps_per_epoch * args.num_epochs

       with strategy.scope():
              a_					:		Union[str, Any]							=  TFAutoModelForMaskedLM.from_config(__A				)
              model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
              a_							,		a_					:		List[str]							=  create_optimizer(
                  num_train_steps=__A  ,    num_warmup_steps=total_train_steps // 20  ,    init_lr=args.learning_rate  ,    weight_decay_rate=args.weight_decay_rate  ,    )

              # Transformers models compute the right loss for their task by default when labels are passed, and will
              # use this for training unless you specify your own loss function in compile().
              model.compile(optimizer=__A  ,    metrics=['accuracy']				)

       def decode_fn(__A :			Dict				):
              a_					:		Union[str, Any]							=  {
                  'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa  ,    shape=(args.max_length,)				),
                  'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa  ,    shape=(args.max_length,)				),
              }
              return tf.io.parse_single_example(__A  ,    __A				)

       # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
       # use their methods in our data pipeline.
       a_					:		Dict							=  DataCollatorForLanguageModeling(
           tokenizer=__A  ,    mlm_probability=args.mlm_probability  ,    mlm=__A  ,    return_tensors='tf'				)

       def mask_with_collator(__A :			Optional[Any]				):
              # TF really needs an isin() function
              a_					:		Optional[Any]							=  (
                  ~tf.cast(batch['attention_mask']  ,    tf.bool				)
                  | (batch['input_ids'] == tokenizer.cls_token_id)
                  | (batch['input_ids'] == tokenizer.sep_token_id)
              )
              a_							,		a_					:		Optional[int]							=  data_collator.tf_mask_tokens(
                  batch['input_ids']  ,    vocab_size=len(__A				)  ,    mask_token_id=tokenizer.mask_token_id  ,    special_tokens_mask=__A  ,    )
              return batch

       a_					:		Optional[Any]							=  args.per_replica_batch_size * strategy.num_replicas_in_sync

       a_					:		Dict							=  prepare_dataset(
           __A  ,    decode_fn=__A  ,    mask_fn=__A  ,    batch_size=__A  ,    shuffle=__A  ,    shuffle_buffer_size=args.shuffle_buffer_size  ,    )

       a_					:		Any							=  prepare_dataset(
           __A  ,    decode_fn=__A  ,    mask_fn=__A  ,    batch_size=__A  ,    shuffle=__A  ,    )

       a_					:		int							=  []
       if args.hub_model_id:
              callbacks.append(
                  PushToHubCallback(output_dir=args.output_dir  ,    hub_model_id=args.hub_model_id  ,    tokenizer=__A				)				)

       model.fit(
           __A  ,    validation_data=__A  ,    epochs=args.num_epochs  ,    callbacks=__A  ,    )

       model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
   UpperCAmelCase_      : List[Any]  					=       parse_args()
   main(args)







'''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






