






"""simple docstring"""




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


UpperCamelCase							       =    logging.get_logger(__name__)






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							Optional[int]:







		'''simple docstring'''







		_a							=	r"\w+[.]\d+"
		_a							=	re.findall(__lowerCAmelCase     , __lowerCAmelCase					)
		for pat in pats:
				_a							=	key.replace(__lowerCAmelCase     , "_".join(pat.split("."					)					)					)
		return key






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Tuple					)       ->							Optional[Any]:







		'''simple docstring'''







		_a							=	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)
		):
				_a							=	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:
				_a							=	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:
				_a							=	pt_tuple_key[:-1] + ("embedding",)
				return renamed_pt_tuple_key, pt_tensor

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

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

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

		# old PyTorch layer norm bias
		_a							=	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 (      UpperCamelCase_:							int     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Tuple=42					)       ->							Any:







		'''simple docstring'''







		_a							=	{k: v.numpy() for k, v in pt_state_dict.items()}

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

		_a							=	flatten_dict(__lowerCAmelCase					)
		_a							=	{}

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

				# Correctly rename weight parameters
				_a			,	_a							=	rename_key_and_reshape_tensor(__lowerCAmelCase     , __lowerCAmelCase     , __lowerCAmelCase					)

				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
				_a							=	jnp.asarray(__lowerCAmelCase					)

		return unflatten_dict(__lowerCAmelCase					)










"""simple docstring"""




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def        lowerCAmelCase (      UpperCamelCase_:							Features					)       ->							Optional[int]:







  '''simple docstring'''







  _a							=	np.inf

  def set_batch_size(UpperCamelCase_:							FeatureType					) -> None:
    nonlocal batch_size
    if isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
    elif isinstance(UpperCamelCase_     , UpperCamelCase_					):
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
    elif isinstance(UpperCamelCase_     , UpperCamelCase_					) and feature.dtype == "binary":
      _a							=	min(UpperCamelCase_     , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

  _visit(UpperCamelCase_     , UpperCamelCase_					)

  return None if batch_size is np.inf else batch_size




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     a_     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = False     ,     a_ = False     ,     a_ = None     ,     **a_     ,     )	->Optional[int]:







        '''simple docstring'''


        super().__init__(
            a_     ,     split=a_     ,     features=a_     ,     cache_dir=a_     ,     keep_in_memory=a_     ,     streaming=a_     ,     num_proc=a_     ,     **a_     ,     )
        _a							=	path_or_paths if isinstance(a_     ,     a_	) else {self.split: path_or_paths}
        _a							=	_PACKAGED_DATASETS_MODULES["parquet"][1]
        _a							=	Parquet(
            cache_dir=a_     ,     data_files=a_     ,     features=a_     ,     hash=a_     ,     **a_     ,     )






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        if self.streaming:
          _a							=	self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
          _a							=	None
          _a							=	None
          _a							=	None
          _a							=	None

          self.builder.download_and_prepare(
              download_config=a_     ,     download_mode=a_     ,     verification_mode=a_     ,     base_path=a_     ,     num_proc=self.num_proc     ,     )
          _a							=	self.builder.as_dataset(
              split=self.split     ,     verification_mode=a_     ,     in_memory=self.keep_in_memory	)
        return dataset







class    lowercase_    :






      def __init__(    self     ,     a_     ,     a_     ,     a_ = None     ,     **a_     ,     )	->int:







        '''simple docstring'''


        _a							=	dataset
        _a							=	path_or_buf
        _a							=	batch_size or get_writer_batch_size(dataset.features	)
        _a							=	parquet_writer_kwargs






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf     ,     (str, bytes, os.PathLike)	):
          with open(self.path_or_buf     ,     "wb+"	) as buffer:
            _a							=	self._write(file_obj=a_     ,     batch_size=a_     ,     **self.parquet_writer_kwargs	)
        else:
          _a							=	self._write(file_obj=self.path_or_buf     ,     batch_size=a_     ,     **self.parquet_writer_kwargs	)
        return written






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     **a_	)	->int:







        '''simple docstring'''


        _a							=	0
        _a							=	parquet_writer_kwargs.pop("path_or_buf"     ,     a_	)
        _a							=	self.dataset.features.arrow_schema

        _a							=	pq.ParquetWriter(a_     ,     schema=a_     ,     **a_	)

        for offset in logging.tqdm(
            range(0     ,     len(self.dataset	)     ,     a_	)     ,     unit="ba"     ,     disable=not logging.is_progress_bar_enabled()     ,     desc="Creating parquet from Arrow format"     ,     ):
          _a							=	query_table(
              table=self.dataset._data     ,     key=slice(a_     ,     offset + batch_size	)     ,     indices=self.dataset._indices if self.dataset._indices is not None else None     ,     )
          writer.write_table(a_	)
          written += batch.nbytes
        writer.close()
        return written


