

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


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



@dataclass
class     lowercase						(       datasets.BuilderConfig					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				None
 __SCREAMING_SNAKE_CASE												=				"utf-8"
 __SCREAMING_SNAKE_CASE												=				None
 __SCREAMING_SNAKE_CASE												=				10 << 20  # 10MB
 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				"line"







class     lowercase						(       datasets.ArrowBasedBuilder					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				TextConfig




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  return datasets.DatasetInfo(features=self.config.features )




 def   UpperCamelCase__							(self       ,       __a ) ->					Dict:






  """simple docstring"""





  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       ,       __a ) ->					pa.Table:






  """simple docstring"""





  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       ,       __a ) ->					Any:






  """simple docstring"""





  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 )









import argparse

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


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

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

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

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

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


if __name__ == "__main__":
     UpperCamelCase								=     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.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
