


from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Dict    ):


     try:
          with open(_snake_case      ,					'rb'    ) as flax_state_f:
               __UpperCamelCase   							=from_bytes(_snake_case      ,					flax_state_f.read()    )
     except UnpicklingError as e:
          try:
               with open(_snake_case    ) as f:
                    if f.read().startswith('version'    ):
                         raise OSError(
                             'You seem to have cloned a repository without having git-lfs installed. Please'
                             ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
                             ' folder you cloned.'    )
                    else:
                         raise ValueError from e
          except (UnicodeDecodeError, ValueError):
               raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '    )

     return load_flax_weights_in_pytorch_model(_snake_case      ,					_snake_case    )





def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]    ):


     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              'Loading 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 SCREAMING_SNAKE_CASE__    : x.dtype == jnp.bfloataa      ,					_snake_case    )    ).values()
     if any(_snake_case    ):
          # convert all weights to fp32 if they 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 SCREAMING_SNAKE_CASE__    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params      ,					_snake_case    )

     __UpperCamelCase   							=""

     __UpperCamelCase   							=flatten_dict(_snake_case      ,					sep='.'    )
     __UpperCamelCase   							=pt_model.state_dict()

     # 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.split('.'    )

          if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
               __UpperCamelCase   							=flax_key_tuple_array[:-1] + ["weight"]
               __UpperCamelCase   							=jnp.transpose(_snake_case      ,					(3, 2, 0, 1)    )
          elif flax_key_tuple_array[-1] == "kernel":
               __UpperCamelCase   							=flax_key_tuple_array[:-1] + ["weight"]
               __UpperCamelCase   							=flax_tensor.T
          elif flax_key_tuple_array[-1] == "scale":
               __UpperCamelCase   							=flax_key_tuple_array[:-1] + ["weight"]

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(_snake_case    ):
                    __UpperCamelCase   							=(
                        flax_key_tuple_string.replace('_0'      ,					'.0'    )
                        .replace('_1'      ,					'.1'    )
                        .replace('_2'      ,					'.2'    )
                        .replace('_3'      ,					'.3'    )
                        .replace('_4'      ,					'.4'    )
                        .replace('_5'      ,					'.5'    )
                        .replace('_6'      ,					'.6'    )
                        .replace('_7'      ,					'.7'    )
                        .replace('_8'      ,					'.8'    )
                        .replace('_9'      ,					'.9'    )
                    )

          __UpperCamelCase   							=".".join(_snake_case    )

          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(_snake_case    ) if not isinstance(_snake_case      ,					np.ndarray    ) else flax_tensor
                    __UpperCamelCase   							=torch.from_numpy(_snake_case    )
                    # remove from missing keys
                    missing_keys.remove(_snake_case    )
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(_snake_case    )

     pt_model.load_state_dict(_snake_case    )

     # re-transform missing_keys to list
     __UpperCamelCase   							=list(_snake_case    )

     if len(_snake_case    ) > 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).'    )
     if len(_snake_case    ) > 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.'    )

     return pt_model









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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Features    ):
     __UpperCamelCase   							=np.inf

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

     _visit(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return None if batch_size is np.inf else batch_size






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = False ,       A_ = False ,       A_ = None ,       **A_ ,       )  ->	Dict:
         super().__init__(
             A_ ,       split=A_ ,       features=A_ ,       cache_dir=A_ ,       keep_in_memory=A_ ,       streaming=A_ ,       num_proc=A_ ,       **A_ ,       )
         __UpperCamelCase   							=path_or_paths if isinstance(A_ ,       A_   ) else {self.split: path_or_paths}
         __UpperCamelCase   							=_PACKAGED_DATASETS_MODULES['parquet'][1]
         __UpperCamelCase   							=Parquet(
             cache_dir=A_ ,       data_files=A_ ,       features=A_ ,       hash=A_ ,       **A_ ,       )






    def _a (      self   )  ->	List[Any]:
         # Build iterable dataset
         if self.streaming:
              __UpperCamelCase   							=self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None

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






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	List[Any]:
         __UpperCamelCase   							=dataset
         __UpperCamelCase   							=path_or_buf
         __UpperCamelCase   							=batch_size or get_writer_batch_size(dataset.features   )
         __UpperCamelCase   							=parquet_writer_kwargs






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=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:
                   __UpperCamelCase   							=self._write(file_obj=A_ ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         else:
              __UpperCamelCase   							=self._write(file_obj=self.path_or_buf ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         return written






    def _a (      self ,       A_ ,       A_ ,       **A_   )  ->	int:
         __UpperCamelCase   							=0
         __UpperCamelCase   							=parquet_writer_kwargs.pop('path_or_buf' ,       A_   )
         __UpperCamelCase   							=self.dataset.features.arrow_schema

         __UpperCamelCase   							=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' ,       ):
              __UpperCamelCase   							=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





