

import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a_     =			logging.get_logger(__name__)

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Any    =				r"\w+[.]\d+"
	SCREAMING_SNAKE_CASE      :					List[str]    =				re.findall(_a							,	_a)
	for pat in pats:
		SCREAMING_SNAKE_CASE      :					List[str]    =				key.replace(_a							,	"_".join(pat.split(".")))
	return key

def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				pt_tuple_key[:-1] + ("scale",)
	if (
	    any("norm" in str_ for str_ in pt_tuple_key)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		SCREAMING_SNAKE_CASE      :					List[Any]    =				pt_tuple_key[:-1] + ("scale",)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		SCREAMING_SNAKE_CASE      :					int    =				pt_tuple_key[:-1] + ("scale",)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				pt_tuple_key[:-1] + ("embedding",)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	SCREAMING_SNAKE_CASE      :					int    =				pt_tuple_key[:-1] + ("kernel",)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pt_tensor.transpose(2							,	3							,	1							,	0)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	SCREAMING_SNAKE_CASE      :					str    =				pt_tuple_key[:-1] + ("kernel",)
	if pt_tuple_key[-1] == "weight":
		SCREAMING_SNAKE_CASE      :					Tuple    =				pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

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

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

	return pt_tuple_key, pt_tensor


def        lowerCamelCase__       (    _a							,	_a							,	_a=42):
	# Step 1: Convert pytorch tensor to numpy
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				{k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	SCREAMING_SNAKE_CASE      :					str    =				flax_model.init_weights(PRNGKey(_a))

	SCREAMING_SNAKE_CASE      :					str    =				flatten_dict(_a)
	SCREAMING_SNAKE_CASE      :					Any    =				{}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		SCREAMING_SNAKE_CASE      :					Tuple    =				rename_key(_a)
		SCREAMING_SNAKE_CASE      :					Any    =				tuple(renamed_pt_key.split("."))

		# Correctly rename weight parameters
		SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[Any]    =				rename_key_and_reshape_tensor(_a							,	_a							,	_a)

		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}.")

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

	return unflatten_dict(_a)







"""simple docstring"""






import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_   : Dict   			= logging.getLogger(__name__)

if __name__ == "__main__":
  UpperCAmelCase_   : List[str]   			= argparse.ArgumentParser(
      description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
  )
  parser.add_argument(
      """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
  )
  parser.add_argument(
      """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
  )
  parser.add_argument("""--vocab_size""", default=30522, type=int)
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()

  logger.info(f'''Loading data from {args.data_file}''')
  with open(args.data_file, """rb""") as fp:
    UpperCAmelCase_   : Union[str, Any]   			= pickle.load(fp)

  logger.info("""Counting occurrences for MLM.""")
  UpperCAmelCase_   : Any   			= Counter()
  for tk_ids in data:
    counter.update(tk_ids)
  UpperCAmelCase_   : List[Any]   			= [0] * args.vocab_size
  for k, v in counter.items():
    UpperCAmelCase_   : Dict   			= v

  logger.info(f'''Dump to {args.token_counts_dump}''')
  with open(args.token_counts_dump, """wb""") as handle:
    pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


