



'''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


lowercase__  :	List[str]            =      datasets.utils.logging.get_logger(__name__)

@dataclass
class    lowerCamelCase_				(						datasets.BuilderConfig	):






   '''simple docstring'''

   A			:  List[Any]					=						None
   A			:  str					=						"utf-8"
   A			:  Optional[int]					=						None
   A			:  List[str]					=						10 << 20  # 10MB
   A			:  Dict					=						False
   A			:  Any					=						"line"

class    lowerCamelCase_				(						datasets.ArrowBasedBuilder	):






   '''simple docstring'''

   A			:  Union[str, Any]					=						TextConfig


   def 						_UpperCamelCase							(	self			):
       return datasets.DatasetInfo(features=self.config.features			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):



       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(A_  ,    (str, list, tuple)			):
           UpperCamelCase__	     =      data_files
           if isinstance(A_  ,    A_			):
               UpperCamelCase__	     =      [files]
           UpperCamelCase__	     =      [dl_manager.iter_files(A_			) 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(A_  ,    A_			):
               UpperCamelCase__	     =      [files]
           UpperCamelCase__	     =      [dl_manager.iter_files(A_			) for file in files]
           splits.append(datasets.SplitGenerator(name=A_  ,    gen_kwargs={"files": files}			)			)
       return splits


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):



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







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      list(self.config.features			) if self.config.features is not None else ["text"]
       for file_idx, file in enumerate(itertools.chain.from_iterable(A_			)			):
           # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
           with open(A_  ,    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(A_			).readlines()
                       if not self.config.keep_linebreaks:
                           UpperCamelCase__	     =      [line.rstrip("\n"			) for line in batch]
                       UpperCamelCase__	     =      pa.Table.from_arrays([pa.array(A_			)]  ,    names=A_			)
                       # 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(A_			)
                       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=A_			)
                       # 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(A_			)
                       batch_idx += 1
                       UpperCamelCase__	     =      batch[-1]
                   if batch:
                       UpperCamelCase__	     =      pa.Table.from_arrays([pa.array([batch]			)]  ,    names=A_			)
                       yield (file_idx, batch_idx), self._cast_table(A_			)
               elif self.config.sample_by == "document":
                   UpperCamelCase__	     =      f.read()
                   UpperCamelCase__	     =      pa.Table.from_arrays([pa.array([text]			)]  ,    names=A_			)
                   yield file_idx, self._cast_table(A_			)









"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCamelCase__    )

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
