


'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowercase__				     =		datasets.utils.logging.get_logger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					(      datasets.BuilderConfig ):
     UpperCAmelCase 					=				10_000
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None







class 						__SCREAMING_SNAKE_CASE					(      datasets.ArrowBasedBuilder ):
     UpperCAmelCase 					=				ParquetConfig






     def  a_					(			self		)       ->    Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          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}"		)
          _a						=		dl_manager.download_and_extract(self.config.data_files		)
          if isinstance(__UpperCamelCase     ,							(str, list, tuple)		):
               _a						=		data_files
               if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               _a						=		[dl_manager.iter_files(__UpperCamelCase		) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,							gen_kwargs={"files": files}		)]
          _a						=		[]
          for split_name, files in data_files.items():
               if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               _a						=		[dl_manager.iter_files(__UpperCamelCase		) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                    for file in itertools.chain.from_iterable(__UpperCamelCase		):
                         with open(__UpperCamelCase     ,							"rb"		) as f:
                              _a						=		datasets.Features.from_arrow_schema(pq.read_schema(__UpperCamelCase		)		)
                         break
               splits.append(datasets.SplitGenerator(name=__UpperCamelCase     ,							gen_kwargs={"files": files}		)		)
          return splits






     def  a_					(			self     ,							__UpperCamelCase		)       ->    pa.Table:
          if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               _a						=		table_cast(__UpperCamelCase     ,							self.info.features.arrow_schema		)
          return pa_table






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		self.info.features.arrow_schema if self.info.features is not None else None
          if self.info.features is not None and self.config.columns is not None:
               if sorted(field.name for field in schema		) != sorted(self.config.columns		):
                    raise ValueError(
                        f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"		)
          for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCamelCase		)		):
               with open(__UpperCamelCase     ,							"rb"		) as f:
                    _a						=		pq.ParquetFile(__UpperCamelCase		)
                    try:
                         for batch_idx, record_batch in enumerate(
                             parquet_file.iter_batches(batch_size=self.config.batch_size     ,							columns=self.config.columns		)		):
                              _a						=		pa.Table.from_batches([record_batch]		)
                              # 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 f"{file_idx}_{batch_idx}", self._cast_table(__UpperCamelCase		)
                    except ValueError as e:
                         logger.error(f"Failed to read file '{file}' with error {type(__UpperCamelCase		)}: {e}"		)
                         raise





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("layer.", "layer_"),
         ("word_embeddings.weight", "word_embeddings"),
         ("position_embeddings.weight", "position_embeddings"),
         ("token_type_embeddings.weight", "token_type_embeddings"),
         (".", "/"),
         ("LayerNorm/weight", "LayerNorm/gamma"),
         ("LayerNorm/bias", "LayerNorm/beta"),
         ("weight", "kernel"),
     )

     if not os.path.isdir(__lowerCamelCase				):
          os.makedirs(__lowerCamelCase				)

     _a						=		model.state_dict()

     def to_tf_var_name(__lowerCamelCase		: str				):
          for patt, repl in iter(__lowerCamelCase				):
               _a						=		name.replace(__lowerCamelCase      ,  __lowerCamelCase				)
          return F"bert/{name}"

     def create_tf_var(__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: tf.Session				):
          _a						=		tf.dtypes.as_dtype(tensor.dtype				)
          _a						=		tf.get_variable(dtype=__lowerCamelCase      ,  shape=tensor.shape      ,  name=__lowerCamelCase      ,  initializer=tf.zeros_initializer()				)
          session.run(tf.variables_initializer([tf_var]				)				)
          session.run(__lowerCamelCase				)
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               _a						=		to_tf_var_name(__lowerCamelCase				)
               _a						=		state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose				):
                    _a						=		torch_tensor.T
               _a						=		create_tf_var(tensor=__lowerCamelCase      ,  name=__lowerCamelCase      ,  session=__lowerCamelCase				)
               tf.keras.backend.set_value(__lowerCamelCase      ,  __lowerCamelCase				)
               _a						=		session.run(__lowerCamelCase				)
               print(F"Successfully created {tf_name}: {np.allclose(__lowerCamelCase      ,  __lowerCamelCase				)}"				)

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--model_name"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="model name e.g. bert-base-uncased"				)
     parser.add_argument(
         "--cache_dir"      ,  type=__lowerCamelCase      ,  default=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory containing pytorch model"				)
     parser.add_argument("--pytorch_model_path"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="/path/to/<pytorch-model-name>.bin"				)
     parser.add_argument("--tf_cache_dir"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory in which to save tensorflow model"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name      ,  state_dict=torch.load(args.pytorch_model_path				)      ,  cache_dir=args.cache_dir      ,  )

     convert_pytorch_checkpoint_to_tf(model=__lowerCamelCase      ,  ckpt_dir=args.tf_cache_dir      ,  model_name=args.model_name				)


if __name__ == "__main__":
   main()

