


import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCAmelCase__  :str             =							'''<<<<<<< This should probably be modified because it mentions: '''

lowerCAmelCase__  :int             =							'''=======
>>>>>>>
'''

lowerCAmelCase__  :Dict             =							[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

lowerCAmelCase__  :int             =							[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'''tfds\.core''', R'''datasets'''),
    (R'''tf\.io\.gfile\.GFile''', R'''open'''),
    (R'''tf\.([\w\d]+)''', R'''datasets.Value(\'\1\')'''),
    (R'''tfds\.features\.Text\(\)''', R'''datasets.Value(\'string\')'''),
    (R'''tfds\.features\.Text\(''', R'''datasets.Value(\'string\'),'''),
    (R'''features\s*=\s*tfds.features.FeaturesDict\(''', R'''features=datasets.Features('''),
    (R'''tfds\.features\.FeaturesDict\(''', R'''dict('''),
    (R'''The TensorFlow Datasets Authors''', R'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (R'''tfds\.''', R'''datasets.'''),
    (R'''dl_manager\.manual_dir''', R'''self.config.data_dir'''),
    (R'''self\.builder_config''', R'''self.config'''),
]

def 	lowerCAmelCase__			(      a__:		Namespace	)  ->		Optional[int]:


							'''simple docstring'''





							return ConvertCommand(args.tfds_path      ,  args.datasets_directory	)







class 		__a				(    UpperCAmelCase      ):
		@staticmethod
		def      UpperCAmelCase__    (      _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      parser.add_parser(
									    'convert'      ,   help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'      ,   )
									train_parser.add_argument(
									    '--tfds_path'      ,   type=_SCREAMING_SNAKE_CASE      ,   required=_SCREAMING_SNAKE_CASE      ,   help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'      ,   )
									train_parser.add_argument(
									    '--datasets_directory'      ,   type=_SCREAMING_SNAKE_CASE      ,   required=_SCREAMING_SNAKE_CASE      ,   help='Path to the HuggingFace Datasets folder.'						)
									train_parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      get_logger('datasets-cli/converting'						)

									_UpperCAmelCase					=      tfds_path
									_UpperCAmelCase					=      datasets_directory






		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									if os.path.isdir(self._tfds_path						):
																_UpperCAmelCase					=      os.path.abspath(self._tfds_path						)
									elif os.path.isfile(self._tfds_path						):
																_UpperCAmelCase					=      os.path.dirname(self._tfds_path						)
									else:
																raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'						)

									_UpperCAmelCase					=      os.path.abspath(self._datasets_directory						)

									self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''						)

									_UpperCAmelCase					=      []
									_UpperCAmelCase					=      []
									_UpperCAmelCase					=      {}

									if os.path.isdir(self._tfds_path						):
																_UpperCAmelCase					=      os.listdir(_SCREAMING_SNAKE_CASE						)
									else:
																_UpperCAmelCase					=      [os.path.basename(self._tfds_path						)]

									for f_name in file_names:
																self._logger.info(f'''Looking at file {f_name}'''						)
																_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																if not os.path.isfile(_SCREAMING_SNAKE_CASE						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																							self._logger.info('Skipping file'						)
																							continue

																with open(_SCREAMING_SNAKE_CASE      ,   encoding='utf-8'						) as f:
																							_UpperCAmelCase					=      f.readlines()

																_UpperCAmelCase					=      []
																_UpperCAmelCase					=      False
																_UpperCAmelCase					=      False
																_UpperCAmelCase					=      []
																for line in lines:
																							_UpperCAmelCase					=      line

																							# Convert imports
																							if "import tensorflow.compat.v2 as tf" in out_line:
																														continue
																							elif "@tfds.core" in out_line:
																														continue
																							elif "builder=self" in out_line:
																														continue
																							elif "import tensorflow_datasets.public_api as tfds" in out_line:
																														_UpperCAmelCase					=      'import datasets\n'
																							elif "import tensorflow" in out_line:
																														# order is important here
																														_UpperCAmelCase					=      ''
																														continue
																							elif "from absl import logging" in out_line:
																														_UpperCAmelCase					=      'from datasets import logging\n'
																							elif "getLogger" in out_line:
																														_UpperCAmelCase					=      out_line.replace('getLogger'      ,   'get_logger'						)
																							elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																														_UpperCAmelCase					=      True
																														_UpperCAmelCase					=      list(filter(lambda _SCREAMING_SNAKE_CASE						: e in out_line      ,   _SCREAMING_SNAKE_CASE						)						)
																														out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_SCREAMING_SNAKE_CASE						) + '\n'						)
																														out_lines.append(_SCREAMING_SNAKE_CASE						)
																														out_lines.append(_SCREAMING_SNAKE_CASE						)
																														continue
																							else:
																														for pattern, replacement in TO_CONVERT:
																																					_UpperCAmelCase					=      re.sub(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

                # Take care of saving utilities (to later move them together with main script)
																							if "tensorflow_datasets" in out_line:
																														_UpperCAmelCase					=      re.match(R'from\stensorflow_datasets.*import\s([^\.\r\n]+)'      ,   _SCREAMING_SNAKE_CASE						)
																														tfds_imports.extend(imp.strip() for imp in match.group(1						).split(','						)						)
																														_UpperCAmelCase					=      'from . import ' + match.group(1						)

																							# Check we have not forget anything
																							if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																														raise ValueError(f'''Error converting {out_line.strip()}'''						)

																							if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																														_UpperCAmelCase					=      True
																							out_lines.append(_SCREAMING_SNAKE_CASE						)

																if is_builder or "wmt" in f_name:
																							# We create a new directory for each dataset
																							_UpperCAmelCase					=      f_name.replace('.py'      ,   ''						)
																							_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							os.makedirs(_SCREAMING_SNAKE_CASE      ,   exist_ok=_SCREAMING_SNAKE_CASE						)
																							self._logger.info(f'''Adding directory {output_dir}'''						)
																							imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}						)
																else:
																							# Utilities will be moved at the end
																							utils_files.append(_SCREAMING_SNAKE_CASE						)

																if needs_manual_update:
																							with_manual_update.append(_SCREAMING_SNAKE_CASE						)

																with open(_SCREAMING_SNAKE_CASE      ,   'w'      ,   encoding='utf-8'						) as f:
																							f.writelines(_SCREAMING_SNAKE_CASE						)
																self._logger.info(f'''Converted in {output_file}'''						)

									for utils_file in utils_files:
																try:
																							_UpperCAmelCase					=      os.path.basename(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      imports_to_builder_map[f_name.replace('.py'      ,   ''						)]
																							self._logger.info(f'''Moving {dest_folder} to {utils_file}'''						)
																							shutil.copy(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																except KeyError:
																							self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.'''						)

									if with_manual_update:
																for file_path in with_manual_update:
																							self._logger.warning(
																							    f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''						)




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							argparse.ArgumentParser(
		    description=(
		        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
		        ''' Distillation'''
		    )
		)
		parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
		parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
		parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
		parser.add_argument('''--vocab_transform''', action='''store_true''')
		lowerCAmelCase__  :Optional[int]             =							parser.parse_args()

		if args.model_type == "bert":
				lowerCAmelCase__  :Tuple             =							BertForMaskedLM.from_pretrained(args.model_name)
				lowerCAmelCase__  :Optional[int]             =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowerCAmelCase__  :List[Any]             =							state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				lowerCAmelCase__  :Union[str, Any]             =							state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
						]
						lowerCAmelCase__  :int             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
						]
				std_idx += 1

		lowerCAmelCase__  :Optional[int]             =							state_dict['''cls.predictions.decoder.weight''']
		lowerCAmelCase__  :List[str]             =							state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[f'''cls.predictions.transform.dense.{w}''']
						lowerCAmelCase__  :List[str]             =							state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

		print(f'''N layers selected for distillation: {std_idx}''')
		print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

		print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
		torch.save(compressed_sd, args.dump_checkpoint)
