
"""simple docstring"""

import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


SCREAMING_SNAKE_CASE_         =  datasets.utils.logging.get_logger(__name__)
@dataclass
class        _UpperCAmelCase							(						datasets.BuilderConfig							):
      __SCREAMING_SNAKE_CASE				:							Optional[datasets.Features]							 =		None
      __SCREAMING_SNAKE_CASE				:							str							 =		"utf-8"
      __SCREAMING_SNAKE_CASE				:							Optional[str]							 =		None
      __SCREAMING_SNAKE_CASE				:							int							 =		1_0 << 2_0  # 10MB
      __SCREAMING_SNAKE_CASE				:							bool							 =		False
      __SCREAMING_SNAKE_CASE				:							str							 =		"line"

class        _UpperCAmelCase							(						datasets.ArrowBasedBuilder							):
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		TextConfig




      def       a_ (							self							)  ->   Tuple:
          return datasets.DatasetInfo(features=self.config.features							)




      def       a_ (							self				,    lowercase_							)  ->   Tuple:
          if not self.config.data_files:
              raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"							)
          UpperCAmelCase       =   dl_manager.download_and_extract(self.config.data_files							)
          if isinstance(lowercase_				,    (str, list, tuple)							):
              UpperCAmelCase       =   data_files
              if isinstance(lowercase_				,    lowercase_							):
                  UpperCAmelCase       =   [files]
              UpperCAmelCase       =   [dl_manager.iter_files(lowercase_							) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={'files': files}							)]
          UpperCAmelCase       =   []
          for split_name, files in data_files.items():
              if isinstance(lowercase_				,    lowercase_							):
                  UpperCAmelCase       =   [files]
              UpperCAmelCase       =   [dl_manager.iter_files(lowercase_							) for file in files]
              splits.append(datasets.SplitGenerator(name=lowercase_				,    gen_kwargs={'files': files}							)							)
          return splits




      def       a_ (							self				,    lowercase_							)  ->   pa.Table:
          if self.config.features is not None:
              UpperCAmelCase       =   self.config.features.arrow_schema
              if all(not require_storage_cast(lowercase_							) for feature in self.config.features.values()							):
                  # cheaper cast
                  UpperCAmelCase       =   pa_table.cast(lowercase_							)
              else:
                  # more expensive cast; allows str <-> int/float or str to Audio for example
                  UpperCAmelCase       =   table_cast(lowercase_				,    lowercase_							)
              return pa_table
          else:
              return pa_table.cast(pa.schema({'text': pa.string()}							)							)




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   list(self.config.features							) if self.config.features is not None else ['text']
          for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_							)							):
              # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
              with open(lowercase_				,    encoding=self.config.encoding				,    errors=self.config.errors							) as f:
                  if self.config.sample_by == "line":
                      UpperCAmelCase       =   0
                      while True:
                          UpperCAmelCase       =   f.read(self.config.chunksize							)
                          if not batch:
                              break
                          batch += f.readline()  # finish current line
                          # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                          UpperCAmelCase       =   StringIO(lowercase_							).readlines()
                          if not self.config.keep_linebreaks:
                              UpperCAmelCase       =   [line.rstrip('\n'							) for line in batch]
                          UpperCAmelCase       =   pa.Table.from_arrays([pa.array(lowercase_							)]				,    names=lowercase_							)
                          # Uncomment for debugging (will print the Arrow table size and elements)
                          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                          yield (file_idx, batch_idx), self._cast_table(lowercase_							)
                          batch_idx += 1
                  elif self.config.sample_by == "paragraph":
                      UpperCAmelCase       =   0
                      UpperCAmelCase       =   ''
                      while True:
                          UpperCAmelCase       =   f.read(self.config.chunksize							)
                          if not new_batch:
                              break
                          batch += new_batch
                          batch += f.readline()  # finish current line
                          UpperCAmelCase       =   batch.split('\n\n'							)
                          UpperCAmelCase       =   pa.Table.from_arrays(
                              [pa.array([example for example in batch[:-1] if example]							)]				,    names=lowercase_							)
                          # Uncomment for debugging (will print the Arrow table size and elements)
                          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                          yield (file_idx, batch_idx), self._cast_table(lowercase_							)
                          batch_idx += 1
                          UpperCAmelCase       =   batch[-1]
                      if batch:
                          UpperCAmelCase       =   pa.Table.from_arrays([pa.array([batch]							)]				,    names=lowercase_							)
                          yield (file_idx, batch_idx), self._cast_table(lowercase_							)
                  elif self.config.sample_by == "document":
                      UpperCAmelCase       =   f.read()
                      UpperCAmelCase       =   pa.Table.from_arrays([pa.array([text]							)]				,    names=lowercase_							)
                      yield file_idx, self._cast_table(lowercase_							)





"""simple docstring"""

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

    # Embeddings
    # Position IDs
    UpperCAmelCase       =   fnet_pretraining_model.state_dict()

    UpperCAmelCase       =   state_dict['fnet.embeddings.position_ids']
    UpperCAmelCase       =   {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']	)

    # Pooler Layers
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

    # Masked LM Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']	)
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_bias']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(lowerCAmelCase	)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--fnet_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained FNet model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



