
"""simple docstring"""




import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
snake_case			       =		"""src/transformers"""

# Matches is_xxx_available()
snake_case			       =		re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
snake_case			       =		re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
snake_case			       =		re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


snake_case			       =		"""
{0} = None
"""

snake_case			       =		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


snake_case			       =		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):



					if _re_test_backend.search(SCREAMING_SNAKE_CASE_      ) is None:
										return None
					SCREAMING_SNAKE_CASE           =     [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_      )]
					backends.sort()
					return "_and_".join(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		):



					with open(os.path.join(SCREAMING_SNAKE_CASE_,					'__init__.py'      ),					'r',					encoding='utf-8',					newline='\n'      ) as f:
										SCREAMING_SNAKE_CASE           =     f.readlines()

					# Get to the point we do the actual imports for type checking
					SCREAMING_SNAKE_CASE           =     0
					while not lines[line_index].startswith('if TYPE_CHECKING'      ):
										line_index += 1

					SCREAMING_SNAKE_CASE           =     {}
					# Go through the end of the file
					while line_index < len(SCREAMING_SNAKE_CASE_      ):
										# If the line is an if is_backend_available, we grab all objects associated.
										SCREAMING_SNAKE_CASE           =     find_backend(lines[line_index]      )
										if backend is not None:
															while not lines[line_index].startswith('    else:'      ):
																				line_index += 1
															line_index += 1

															SCREAMING_SNAKE_CASE           =     []
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(' ' * 8      ):
																				SCREAMING_SNAKE_CASE           =     lines[line_index]
																				SCREAMING_SNAKE_CASE           =     _re_single_line_import.search(SCREAMING_SNAKE_CASE_      )
																				if single_line_import_search is not None:
																									objects.extend(single_line_import_search.groups()[0].split(', '      )      )
																				elif line.startswith(' ' * 1_2      ):
																									objects.append(line[1_2:-2]      )
																				line_index += 1

															SCREAMING_SNAKE_CASE           =     objects
										else:
															line_index += 1

					return backend_specific_objects

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):



					if name.isupper():
										return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE_      )
					elif name.islower():
										return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
					else:
										return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_=None      ):



					if backend_specific_objects is None:
										SCREAMING_SNAKE_CASE           =     read_init()
					# For special correspondence backend to module name as used in the function requires_modulename
					SCREAMING_SNAKE_CASE           =     {}

					for backend, objects in backend_specific_objects.items():
										SCREAMING_SNAKE_CASE           =     '[' + ', '.join(f'''\"{b}\"''' for b in backend.split('_and_'      )      ) + ']'
										SCREAMING_SNAKE_CASE           =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
										dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
										dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ) for o in objects]      )
										SCREAMING_SNAKE_CASE           =     dummy_file

					return dummy_files

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_=False      ):
					SCREAMING_SNAKE_CASE           =     create_dummy_files()
					# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
					SCREAMING_SNAKE_CASE           =     {'torch': 'pt'}

					# Locate actual dummy modules and read their content.
					SCREAMING_SNAKE_CASE           =     os.path.join(SCREAMING_SNAKE_CASE_,					'utils'      )
					SCREAMING_SNAKE_CASE           =     {
					    backend: os.path.join(SCREAMING_SNAKE_CASE_,					f'''dummy_{short_names.get(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )}_objects.py'''      )
					    for backend in dummy_files.keys()
					}

					SCREAMING_SNAKE_CASE           =     {}
					for backend, file_path in dummy_file_paths.items():
										if os.path.isfile(SCREAMING_SNAKE_CASE_      ):
															with open(SCREAMING_SNAKE_CASE_,					'r',					encoding='utf-8',					newline='\n'      ) as f:
																				SCREAMING_SNAKE_CASE           =     f.read()
										else:
															SCREAMING_SNAKE_CASE           =     ''

					for backend in dummy_files.keys():
										if dummy_files[backend] != actual_dummies[backend]:
															if overwrite:
																				print(
																				    f'''Updating transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )}_objects.py as the main '''
																				    '__init__ has new objects.'      )
																				with open(dummy_file_paths[backend],					'w',					encoding='utf-8',					newline='\n'      ) as f:
																									f.write(dummy_files[backend]      )
															else:
																				raise ValueError(
																				    'The main __init__ has objects that are not present in '
																				    f'''transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )}_objects.py. Run `make fix-copies` '''
																				    'to fix this.'      )


if __name__ == "__main__":
	snake_case			       =		argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	snake_case			       =		parser.parse_args()

	check_dummies(args.fix_and_overwrite)


"""simple docstring"""




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


snake_case			       =		logging.getLogger(__name__)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'      )
					parser.add_argument(
					    '--dataset_name',					type=SCREAMING_SNAKE_CASE_,					default='wikitext',					help='Name of the training. Explore datasets at: hf.co/datasets.',					)
					parser.add_argument(
					    '--dataset_config',					type=SCREAMING_SNAKE_CASE_,					default='wikitext-103-raw-v1',					help='Configuration name of the dataset.'      )
					parser.add_argument(
					    '--batch_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_0,					help='Batch size during training.',					)
					parser.add_argument(
					    '--vocab_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_4_8,					help='Size of the desired vocabulary.',					)
					parser.add_argument(
					    '--limit',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					help='Limit the number of shards (used for debugging).',					)
					parser.add_argument(
					    '--export_to_hub',					action='store_true',					)

					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.load_dataset(args.dataset_name,					args.dataset_config,					split='train'      )

					if args.limit is not None:
										SCREAMING_SNAKE_CASE           =     min(len(SCREAMING_SNAKE_CASE_      ),					args.limit      )
										SCREAMING_SNAKE_CASE           =     dataset.select(range(SCREAMING_SNAKE_CASE_      )      )
										logger.info(f'''Limiting the dataset to {args.limit} entries.'''      )

					def batch_iterator():
										for i in range(0,					len(SCREAMING_SNAKE_CASE_      ),					args.batch_size      ):
															yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
					SCREAMING_SNAKE_CASE           =     Tokenizer(Unigram()      )
					SCREAMING_SNAKE_CASE           =     normalizers.Sequence([normalizers.Replace('``',					'"'      ), normalizers.Replace('\'\'',					'"'      )]      )
					SCREAMING_SNAKE_CASE           =     pre_tokenizers.Metaspace()

					# Prepare the trainer.
					SCREAMING_SNAKE_CASE           =     UnigramTrainer(
					    unk_token='<unk>',					special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]'],					vocab_size=args.vocab_size,					)

					logger.info('Training the tokenizer.'      )
					tokenizer.train_from_iterator(batch_iterator(),					trainer=SCREAMING_SNAKE_CASE_      )
					logger.info('Tokenizer training complete!'      )

					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[CLS]'      )
					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[SEP]'      )
					SCREAMING_SNAKE_CASE           =     processors.TemplateProcessing(
					    single='[CLS]:0 $A:0 [SEP]:0',					pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1',					special_tokens=[
					        ('[CLS]', cls_token_id),
					        ('[SEP]', sep_token_id),
					    ],					)
					SCREAMING_SNAKE_CASE           =     decoders.Metaspace()

					if args.export_to_hub:
										logger.info('Exporting the trained tokenzier to Hub.'      )
										SCREAMING_SNAKE_CASE           =     AlbertTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_      )
										new_tokenizer.push_to_hub('unigram-tokenizer-dataset'      )


if __name__ == "__main__":
	snake_case			       =		parse_args()
	main(args)
