
"""simple docstring"""




import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
	from comet_ml import OfflineExperiment

if is_tensorboard_available():
	import struct

	import tensorboard.compat.proto.event_pba as event_pba

snake_case			       =		logging.getLogger(__name__)




@require_tensorboard
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_config'
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers(lowercase__		,     lowercase__      )
														accelerator.end_training()
														for child in Path(f'''{dirpath}/{project_name}'''      ).glob('*/**'      ):
																			SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : x.is_file()		,     child.iterdir()      )      )[0]
														self.assertNotEqual(str(lowercase__      )		,     ''      )


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_log'
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )
														accelerator.init_trackers(lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : x.is_file()		,     Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
														self.assertNotEqual(str(lowercase__      )		,     ''      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_log'
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )
														accelerator.init_trackers(lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'tensor': torch.tensor(1      )}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : x.is_file()		,     Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
														# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
														with open(lowercase__		,     'rb'      ) as f:
																			SCREAMING_SNAKE_CASE           =     f.read()
														SCREAMING_SNAKE_CASE           =     False
														while data:
																			SCREAMING_SNAKE_CASE           =     struct.unpack('Q'		,     data[:8]      )

																			SCREAMING_SNAKE_CASE           =     data[12 : 12 + int(header[0]      )]  # 8+4
																			SCREAMING_SNAKE_CASE           =     data[12 + int(header[0]      ) + 4 :]
																			SCREAMING_SNAKE_CASE           =     event_pba.Event()

																			event.ParseFromString(lowercase__      )
																			if event.HasField('summary'      ):
																								for value in event.summary.value:
																													if value.simple_value == 1.0 and value.tag == "tensor":
																																		SCREAMING_SNAKE_CASE           =     True
														self.assertTrue(lowercase__		,     'Converted tensor was not found in the log file!'      )


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									with self.assertRaisesRegex(lowercase__		,     'Logging with `tensorboard` requires a `logging_dir`'      ):
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'      )
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__      )




				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     ProjectConfiguration(total_limit=30      )
									with tempfile.TemporaryDirectory() as dirpath:
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with='tensorboard'		,     project_dir=lowercase__		,     project_config=lowercase__      )






@require_wandb
@mock.patch.dict(os.environ  ,				{"WANDB_MODE": "offline"}				)
class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									super().setUp()
									# wandb let's us override where logs are stored to via the WANDB_DIR env var
									self.add_mocks(mock.patch.dict(os.environ		,     {'WANDB_DIR': self.tmpdir}      )      )


				@staticmethod
				def 							A (    lowercase__		,     lowercase__		,     lowercase__ = True      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     rf'''{section} ([\S\s]*?)\n\n'''
									if record:
														SCREAMING_SNAKE_CASE           =     rf'''Record: {pattern}'''
									SCREAMING_SNAKE_CASE           =     re.findall(lowercase__		,     lowercase__      )[0]
									# A config
									if section == "config" or section == "history":
														SCREAMING_SNAKE_CASE           =     re.findall(r'"([a-zA-Z0-9_.,]+)'		,     lowercase__      )
														return {key: val for key, val in zip(cleaned_record[0::2]		,     cleaned_record[1::2]      )}
									# Everything else
									else:
														return dict(re.findall(r'(\w+): "([^\s]+)"'		,     lowercase__      )      )




				@skip
				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     'test_project_with_config'
									SCREAMING_SNAKE_CASE           =     Accelerator(log_with='wandb'      )
									SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
									SCREAMING_SNAKE_CASE           =     {'wandb': {'tags': ['my_tag']}}
									accelerator.init_trackers(lowercase__		,     lowercase__		,     lowercase__      )
									SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
									accelerator.log(lowercase__		,     step=0      )
									accelerator.end_training()
									# The latest offline log is stored at wandb/latest-run/*.wandb
									for child in Path(f'''{self.tmpdir}/wandb/latest-run'''      ).glob('*'      ):
														if child.is_file() and child.suffix == ".wandb":
																			SCREAMING_SNAKE_CASE           =     subprocess.check_output(
																			    ['wandb', 'sync', '--view', '--verbose', str(lowercase__      )]		,     env=os.environ.copy()      ).decode('utf8'		,     'ignore'      )
																			break

        # Check HPS through careful parsing and cleaning
									SCREAMING_SNAKE_CASE           =     self.parse_log(lowercase__		,     'config'      )
									self.assertEqual(logged_items['num_iterations']		,     '12'      )
									self.assertEqual(logged_items['learning_rate']		,     '0.01'      )
									self.assertEqual(logged_items['some_boolean']		,     'false'      )
									self.assertEqual(logged_items['some_string']		,     'some_value'      )
									self.assertEqual(logged_items['some_string']		,     'some_value'      )

									# Run tags
									SCREAMING_SNAKE_CASE           =     self.parse_log(lowercase__		,     'run'		,     lowercase__      )
									self.assertEqual(logged_items['tags']		,     'my_tag'      )

									# Actual logging
									SCREAMING_SNAKE_CASE           =     self.parse_log(lowercase__		,     'history'      )
									self.assertEqual(logged_items['total_loss']		,     '0.1'      )
									self.assertEqual(logged_items['iteration']		,     '1'      )
									self.assertEqual(logged_items['my_text']		,     'some_value'      )
									self.assertEqual(logged_items['_step']		,     '0'      )






def   UpperCamelCase_  (		self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     run_name
					SCREAMING_SNAKE_CASE           =     OfflineExperiment(project_name=SCREAMING_SNAKE_CASE_,					offline_directory=SCREAMING_SNAKE_CASE_      )
					logger.info(f'''Initialized offline CometML project {self.run_name}'''      )
					logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'      )




@require_comet_ml
@mock.patch.object(__magic_name__  ,				"__init__"  ,				__magic_name__				)
class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				@staticmethod
				def 							A (    lowercase__		,     lowercase__		,     lowercase__ = False      )   ->   Optional[int]:
									"""simple docstring"""







									for log in log_list:
														SCREAMING_SNAKE_CASE           =     json.loads(lowercase__      )['payload']
														if is_param and "param" in j.keys():
																			if j["param"]["paramName"] == key:
																								return j["param"]["paramValue"]
														if "log_other" in j.keys():
																			if j["log_other"]["key"] == key:
																								return j["log_other"]["val"]
														if "metric" in j.keys():
																			if j["metric"]["metricName"] == key:
																								return j["metric"]["metricValue"]


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     CometMLTracker('test_project_with_config'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers(lowercase__		,     lowercase__      )
														accelerator.end_training()
														SCREAMING_SNAKE_CASE           =     os.listdir(lowercase__      )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     zipfile.ZipFile(lowercase__		,     'r'      )
														SCREAMING_SNAKE_CASE           =     archive.open('messages.json'      ).read().decode('utf-8'      )
									SCREAMING_SNAKE_CASE           =     log.split('\n'      )[:-1]
									self.assertEqual(self.get_value_from_key(lowercase__		,     'num_iterations'		,     lowercase__      )		,     12      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'learning_rate'		,     lowercase__      )		,     0.01      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'some_boolean'		,     lowercase__      )		,     lowercase__      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'some_string'		,     lowercase__      )		,     'some_value'      )




				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     CometMLTracker('test_project_with_config'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														accelerator.init_trackers(lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														SCREAMING_SNAKE_CASE           =     os.listdir(lowercase__      )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     zipfile.ZipFile(lowercase__		,     'r'      )
														SCREAMING_SNAKE_CASE           =     archive.open('messages.json'      ).read().decode('utf-8'      )
									SCREAMING_SNAKE_CASE           =     log.split('\n'      )[:-1]
									self.assertEqual(self.get_value_from_key(lowercase__		,     'curr_step'		,     lowercase__      )		,     0      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'total_loss'      )		,     0.1      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'iteration'      )		,     1      )
									self.assertEqual(self.get_value_from_key(lowercase__		,     'my_text'      )		,     'some_value'      )






class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				str						  =    [
				    "total_loss",
				    "iteration",
				    "my_text",
				    "learning_rate",
				    "num_iterations",
				    "some_boolean",
				    "some_string",
				]

				UpperCAmelCase_    :				Optional[int]						  =    "my_custom_tracker"
				UpperCAmelCase_    :				Union[str, Any]						  =    False


				def __init__(    self		,     lowercase__      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     open(f'''{dir}/log.csv'''		,     'w+'      )
									SCREAMING_SNAKE_CASE           =     csv.DictWriter(self.f		,     fieldnames=self._col_names      )
									self.writer.writeheader()


				@property
				def 							A (    self      )   ->   int:
									"""simple docstring"""







									return self.writer


				def 							A (    self		,     lowercase__      )   ->   List[str]:
									"""simple docstring"""







									logger.info('Call init'      )
									self.writer.writerow(lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__      )   ->   int:
									"""simple docstring"""







									logger.info('Call log'      )
									self.writer.writerow(lowercase__      )




				def 							A (    self      )   ->   int:
									"""simple docstring"""







									self.f.close()






class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   int:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     MyCustomTracker(lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														SCREAMING_SNAKE_CASE           =     {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
														accelerator.init_trackers('Some name'		,     lowercase__      )
														accelerator.end_training()
														with open(f'''{d}/log.csv'''		,     'r'      ) as f:
																			SCREAMING_SNAKE_CASE           =     csv.DictReader(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     next(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     {
																			    'total_loss': '',
																			    'iteration': '',
																			    'my_text': '',
																			    'learning_rate': '0.01',
																			    'num_iterations': '12',
																			    'some_boolean': 'False',
																			    'some_string': 'some_value',
																			}
																			self.assertDictEqual(lowercase__		,     lowercase__      )




				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									with tempfile.TemporaryDirectory() as d:
														SCREAMING_SNAKE_CASE           =     MyCustomTracker(lowercase__      )
														SCREAMING_SNAKE_CASE           =     Accelerator(log_with=lowercase__      )
														accelerator.init_trackers('Some name'      )
														SCREAMING_SNAKE_CASE           =     {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
														accelerator.log(lowercase__		,     step=0      )
														accelerator.end_training()
														with open(f'''{d}/log.csv'''		,     'r'      ) as f:
																			SCREAMING_SNAKE_CASE           =     csv.DictReader(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     next(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     {
																			    'total_loss': '0.1',
																			    'iteration': '1',
																			    'my_text': 'some_value',
																			    'learning_rate': '',
																			    'num_iterations': '',
																			    'some_boolean': '',
																			    'some_string': '',
																			}
																			self.assertDictEqual(lowercase__		,     lowercase__      )


"""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)
