
"""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
)
lowerCAmelCase__				    =       logging.getLogger(__name__)

if __name__ == "__main__":
     lowerCAmelCase__				    =       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=30_522, type=int)
     lowerCAmelCase__				    =       parser.parse_args()

     logger.info(F"Loading data from {args.data_file}")
     with open(args.data_file, '''rb''') as fp:
          lowerCAmelCase__				    =       pickle.load(fp)

     logger.info('''Counting occurrences for MLM.''')
     lowerCAmelCase__				    =       Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     lowerCAmelCase__				    =       [0] * args.vocab_size
     for k, v in counter.items():
          lowerCAmelCase__				    =       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)









"""simple docstring"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import jax
     import jaxlib

lowerCAmelCase__				    =       get_logger()

lowerCAmelCase__				    =       None


class 				SCREAMING_SNAKE_CASE__    (		TensorFormatter[Mapping, "jax.Array", Mapping]    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__=None		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         super().__init__(features=snake_case__						)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(snake_case__		,					snake_case__						):
           raise ValueError(
               f"""Expected {device} to be a `str` not {type(snake_case__						)}, as `jaxlib.xla_extension.Device` """
               "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
               "the device with `str()` to get its string identifier that will be internally mapped "
               "to the actual `jaxlib.xla_extension.Device`."						)
         lowerCAmelCase					:					List[str]								=							device if isinstance(snake_case__		,					snake_case__						) else str(jax.devices()[0]						)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           lowerCAmelCase					:					Optional[Any]								=							self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()						):
           logger.warning(
               f"""Device with string identifier {self.device} not listed among the available """
               f"""devices: {list(DEVICE_MAPPING.keys()						)}, so falling back to the default """
               f"""device: {str(jax.devices()[0]						)}."""						)
           lowerCAmelCase					:					Dict								=							str(jax.devices()[0]						)
         lowerCAmelCase					:					Union[str, Any]								=							jnp_array_kwargs

       @staticmethod
       def 	lowercase__							(						):






         """simple docstring"""





         import jax

         return {str(snake_case__						): device for device in jax.devices()}

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

         if isinstance(snake_case__		,					snake_case__						) and column:
           if all(
               isinstance(snake_case__		,					jax.Array						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
             return jnp.stack(snake_case__		,					axis=0						)
         return column

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

         if isinstance(snake_case__		,					(str, bytes, type(snake_case__						))						):
           return value
         elif isinstance(snake_case__		,					(np.character, np.ndarray)						) and np.issubdtype(value.dtype		,					np.character						):
           return value.tolist()

         lowerCAmelCase					:					Tuple								=							{}

         if isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.integer						):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
             lowerCAmelCase					:					List[Any]								=							{"dtype": jnp.intaa}
           else:
             lowerCAmelCase					:					Optional[Any]								=							{"dtype": jnp.intaa}
         elif isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.floating						):
           lowerCAmelCase					:					int								=							{"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(snake_case__		,					PIL.Image.Image						):
             lowerCAmelCase					:					Dict								=							np.asarray(snake_case__						)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           lowerCAmelCase					:					str								=							self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]						):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(snake_case__		,					**{**default_dtype, **self.jnp_array_kwargs}						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(snake_case__		,					torch.Tensor						):
             return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
         if hasattr(snake_case__		,					"__array__"						) and not isinstance(snake_case__		,					jax.Array						):
           lowerCAmelCase					:					Dict								=							data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(snake_case__		,					np.ndarray						):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         elif isinstance(snake_case__		,					(list, tuple)						):
           return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         return self._tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return map_nested(self._recursive_tensorize		,					snake_case__		,					map_list=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.numpy_arrow_extractor().extract_row(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.python_features_decoder.decode_row(snake_case__						)
         return self.recursive_tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.numpy_arrow_extractor().extract_column(snake_case__						)
         lowerCAmelCase					:					Dict								=							self.python_features_decoder.decode_column(snake_case__		,					pa_table.column_names[0]						)
         lowerCAmelCase					:					Optional[Any]								=							self.recursive_tensorize(snake_case__						)
         lowerCAmelCase					:					Dict								=							self._consolidate(snake_case__						)
         return column







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.numpy_arrow_extractor().extract_batch(snake_case__						)
         lowerCAmelCase					:					str								=							self.python_features_decoder.decode_batch(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.recursive_tensorize(snake_case__						)
         for column_name in batch:
           lowerCAmelCase					:					Optional[int]								=							self._consolidate(batch[column_name]						)
         return batch





