"""simple docstring"""




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a      :		Any           =   logging.get_logger(__name__)

_a      :		Dict           =   {'vocab_file': 'vocab.txt'}

_a      :		str           =   {
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

_a      :		List[str]           =   {
    'openbmb/cpm-ant-10b': 1_024,
}





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Tuple:
  _lowerCAmelCase						:       List[str]  			=     collections.OrderedDict()
  with open(_lowerCamelCase			,"""r"""			,encoding="""utf-8"""	) as reader:
    _lowerCAmelCase						:       Tuple  			=     reader.readlines()
  for index, token in enumerate(_lowerCamelCase	):
    _lowerCAmelCase						:       str  			=     token.rstrip("""\n"""	)
    _lowerCAmelCase						:       str  			=     index
  return vocab
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__="<unk>"				,     a__=200  ):
       _lowerCAmelCase						:       Optional[Any]  			=     vocab
       _lowerCAmelCase						:       Any  			=     unk_token
       _lowerCAmelCase						:       int  			=     max_input_chars_per_word






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Any  			=     list(a__  )
       if len(a__  ) > self.max_input_chars_per_word:
         return [self.unk_token]

       _lowerCAmelCase						:       Union[str, Any]  			=     0
       _lowerCAmelCase						:       Dict  			=     []
       while start < len(a__  ):
         _lowerCAmelCase						:       Tuple  			=     len(a__  )
         _lowerCAmelCase						:       int  			=     None
         while start < end:
           _lowerCAmelCase						:       str  			=     """""".join(chars[start:end]  )
           if substr in self.vocab:
             _lowerCAmelCase						:       int  			=     substr
             break
           end -= 1
         if cur_substr is None:
           sub_tokens.append(self.unk_token  )
           start += 1
         else:
           sub_tokens.append(a__  )
           _lowerCAmelCase						:       Union[str, Any]  			=     end

       return sub_tokens







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Optional[int]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[str]							=		["input_ids", "attention_mask"]
     _UpperCamelCase							:			str							=		False






     def __init__(		self				,     a__				,     a__="<d>"				,     a__="</d>"				,     a__="<s>"				,     a__="</s>"				,     a__="<pad>"				,     a__="<unk>"				,     a__="</n>"				,     a__="</_>"				,     a__="left"				,     **a__				,     ):
       requires_backends(self				,     ["""jieba"""]  )
       super().__init__(
           bod_token=a__				,     eod_token=a__				,     bos_token=a__				,     eos_token=a__				,     pad_token=a__				,     unk_token=a__				,     line_token=a__				,     space_token=a__				,     padding_side=a__				,     **a__				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     bod_token
       _lowerCAmelCase						:       List[Any]  			=     eod_token
       _lowerCAmelCase						:       List[str]  			=     load_vocab(a__  )
       _lowerCAmelCase						:       Tuple  			=     self.encoder[space_token]
       _lowerCAmelCase						:       Optional[Any]  			=     self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       _lowerCAmelCase						:       Any  			=     collections.OrderedDict(sorted(self.encoder.items()				,     key=lambda a__  : x[1]  )  )
       _lowerCAmelCase						:       int  			=     {v: k for k, v in self.encoder.items()}

       _lowerCAmelCase						:       Optional[int]  			=     WordpieceTokenizer(vocab=self.encoder				,     unk_token=self.unk_token  )






     @property
     def     __A  (		self  ):
       return self.encoder[self.bod_token]






     @property
     def     __A  (		self  ):
       return self.encoder[self.eod_token]






     @property
     def     __A  (		self  ):
       return self.encoder["\n"]






     @property
     def     __A  (		self  ):
       return len(self.encoder  )






     def     __A  (		self  ):
       return dict(self.encoder				,     **self.added_tokens_encoder  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     []
       for x in jieba.cut(a__				,     cut_all=a__  ):
         output_tokens.extend(self.wordpiece_tokenizer.tokenize(a__  )  )
       return output_tokens






     def     __A  (		self				,     a__				,     **a__  ):
       _lowerCAmelCase						:       Any  			=     [i for i in token_ids if i >= 0]
       _lowerCAmelCase						:       str  			=     [
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(a__				,     **a__  )






     def     __A  (		self				,     a__  ):
       return token in self.encoder






     def     __A  (		self				,     a__  ):
       return "".join(a__  )






     def     __A  (		self				,     a__  ):
       return self.encoder.get(a__				,     self.encoder.get(self.unk_token  )  )






     def     __A  (		self				,     a__  ):
       return self.decoder.get(a__				,     self.unk_token  )






     def     __A  (		self				,     a__				,     a__ = None  ):
       if os.path.isdir(a__  ):
         _lowerCAmelCase						:       int  			=     os.path.join(
             a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       else:
         _lowerCAmelCase						:       List[Any]  			=     (filename_prefix + """-""" if filename_prefix else """""") + save_directory
       _lowerCAmelCase						:       Any  			=     0
       if " " in self.encoder:
         _lowerCAmelCase						:       int  			=     self.encoder[""" """]
         del self.encoder[" "]
       if "\n" in self.encoder:
         _lowerCAmelCase						:       int  			=     self.encoder["""\n"""]
         del self.encoder["\n"]
       _lowerCAmelCase						:       List[Any]  			=     collections.OrderedDict(sorted(self.encoder.items()				,     key=lambda a__  : x[1]  )  )
       with open(a__				,     """w"""				,     encoding="""utf-8"""  ) as writer:
         for token, token_index in self.encoder.items():
           if index != token_index:
             logger.warning(
                 F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                 """ Please check that the vocabulary is not corrupted!"""  )
             _lowerCAmelCase						:       List[str]  			=     token_index
           writer.write(token + """\n"""  )
           index += 1
       return (vocab_file,)






     def     __A  (		self				,     a__				,     a__ = None  ):
       if token_ids_a is None:
         return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = False  ):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=a__				,     token_ids_a=a__				,     already_has_special_tokens=a__  )

       if token_ids_a is not None:
         return [1] + ([0] * len(a__  )) + [1] + ([0] * len(a__  ))
       return [1] + ([0] * len(a__  ))








"""simple docstring"""




import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__UpperCamelCase						=      [
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None				)     ->							int:
								SCREAMING_SNAKE_CASE													=					None
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'by_feature'				)				)
								SCREAMING_SNAKE_CASE													=					os.path.abspath('examples'				)
								for item in os.listdir(lowerCAmelCase__				):
														if item not in EXCLUDE_EXAMPLES:
																				SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)
																				if os.path.isfile(lowerCAmelCase__				) and ".py" in item_path:
																										with self.subTest(
																										    tested_script=lowerCAmelCase__						,       feature_script=lowerCAmelCase__						,       tested_section='main()' if parser_only else 'training_function()'						,       ):
																																SCREAMING_SNAKE_CASE													=					compare_against_test(
																																    os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
																																SCREAMING_SNAKE_CASE													=					'\n'.join(lowerCAmelCase__				)
																																if special_strings is not None:
																																						for string in special_strings:
																																												SCREAMING_SNAKE_CASE													=					diff.replace(lowerCAmelCase__						,       ''				)
																																self.assertEqual(lowerCAmelCase__						,       ''				)







		def 							__A						(	self				)     ->							Optional[int]:
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'cv_example.py'				)				)
								SCREAMING_SNAKE_CASE													=					[
								    ' ' * 16 + '{\n\n',
								    ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
								    ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
								    ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
								    ' ' * 20 + '"epoch": epoch,\n\n',
								    ' ' * 16 + '},\n\n',
								    ' ' * 16 + 'step=epoch,\n',
								    ' ' * 12,
								    ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
								]
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)





@mock.patch.dict(os.environ	,    {"""TESTING_MOCKED_DATALOADERS""": """1"""}    )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[str]								=     False







		@classmethod
		def 							__A						(	cls				)     ->							List[str]:
								super().setUpClass()
								SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE													=					os.path.join(cls._tmpdir						,       'default_config.yml'				)

								write_basic_config(save_location=cls.configPath				)
								SCREAMING_SNAKE_CASE													=					['accelerate', 'launch', '--config_file', cls.configPath]







		@classmethod
		def 							__A						(	cls				)     ->							Dict:
								super().tearDownClass()
								shutil.rmtree(cls._tmpdir				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
								run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'epoch_0'				)				)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'step_2'				)				)				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "epoch_0"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
								self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "step_2"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE													=					torch.cuda.device_count()
								else:
														SCREAMING_SNAKE_CASE													=					1
								if num_processes > 1:
														self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)
								else:
														self.assertIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
								with mock.patch.dict(os.environ						,       {'TESTING_MOCKED_DATALOADERS': '0'}				):
														SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					re.findall('({.+})'						,       lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					[r for r in results if 'accuracy' in r][-1]
														SCREAMING_SNAKE_CASE													=					ast.literal_eval(lowerCAmelCase__				)
														self.assertGreaterEqual(results['accuracy']						,       0.75				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/multi_process_metrics.py']
								run_command(self._launch_args + testargs				)







		@require_trackers
		@mock.patch.dict(os.environ						,       {'WANDB_MODE': 'offline'}				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								with tempfile.TemporaryDirectory() as tmpdir:
														SCREAMING_SNAKE_CASE													=					F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
														run_command(self._launch_args + testargs				)
														self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__						,       'tracking'				)				)				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/gradient_accumulation.py']
								run_command(self._launch_args + testargs				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/local_sgd.py']
								run_command(self._launch_args + testargs				)


