



'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase						      =       {
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

__lowerCAmelCase						      =       {
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


__lowerCAmelCase						      =       {
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}

class      __magic_name__						(   _UpperCAmelCase       ):
							lowerCAmelCase	:   int           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   Any           = PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   Tuple           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   List[str]           = PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase	:   Any           = RoFormerTokenizer







							def __init__(							self :	Tuple     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	Any=None     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	int="[UNK]"     ,_UpperCAmelCase :	Optional[int]="[SEP]"     ,_UpperCAmelCase :	Dict="[PAD]"     ,_UpperCAmelCase :	List[str]="[CLS]"     ,_UpperCAmelCase :	Tuple="[MASK]"     ,_UpperCAmelCase :	Tuple=True     ,_UpperCAmelCase :	Optional[int]=None     ,**_UpperCAmelCase :	Union[str, Any]     ,):
									super().__init__(
									    _UpperCAmelCase     ,tokenizer_file=_UpperCAmelCase     ,do_lower_case=_UpperCAmelCase     ,unk_token=_UpperCAmelCase     ,sep_token=_UpperCAmelCase     ,pad_token=_UpperCAmelCase     ,cls_token=_UpperCAmelCase     ,mask_token=_UpperCAmelCase     ,tokenize_chinese_chars=_UpperCAmelCase     ,strip_accents=_UpperCAmelCase     ,**_UpperCAmelCase     ,)

									_a					:				Union[str, Any]				=							json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
									if (
									    pre_tok_state.get('lowercase'     ,_UpperCAmelCase		) != do_lower_case
									    or pre_tok_state.get('strip_accents'     ,_UpperCAmelCase		) != strip_accents
									):
											_a					:				List[Any]				=							getattr(_UpperCAmelCase     ,pre_tok_state.pop('type'		)		)
											_a					:				List[Any]				=							do_lower_case
											_a					:				str				=							strip_accents
											_a					:				Tuple				=							pre_tok_class(**_UpperCAmelCase		)

									_a					:				Optional[Any]				=							do_lower_case







							def __getstate__(							self :	str		):
									_a					:				Union[str, Any]				=							self.__dict__.copy()
									_a					:				Optional[int]				=							BertPreTokenizer()
									return state







							def __setstate__(							self :	int     ,_UpperCAmelCase :	int		):
									_a					:				List[str]				=							d
									_a					:				List[str]				=							self.__dict__['''_tokenizer'''].get_vocab()
									_a					:				Optional[Any]				=							PreTokenizer.custom(JiebaPreTokenizer(_UpperCAmelCase		)		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	List[str]=None		):
									_a					:				List[Any]				=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

									return output







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[str] = None		):
									_a					:				str				=							[self.sep_token_id]
									_a					:				str				=							[self.cls_token_id]
									if token_ids_a is None:
											return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Any = None		):
									_a					:				str				=							self._tokenizer.model.save(_UpperCAmelCase     ,name=_UpperCAmelCase		)
									return tuple(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Optional[Any]=None     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	Any=False     ,**_UpperCAmelCase :	Dict     ,):
									_a					:				str				=							BertPreTokenizer()
									return super().save_pretrained(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,**_UpperCAmelCase		)





'''simple docstring'''

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer

@require_tf
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]		):
									self.assertEqual(len(_UpperCAmelCase		)     ,len(_UpperCAmelCase		)		)
									for a, b in zip(_UpperCAmelCase     ,_UpperCAmelCase		):
											self.assertAlmostEqual(_UpperCAmelCase     ,_UpperCAmelCase     ,delta=_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							GradientAccumulator()
									accumulator([tf.constant([1.0, 2.0]		)]		)
									accumulator([tf.constant([-2.0, 1.0]		)]		)
									accumulator([tf.constant([-1.0, 2.0]		)]		)
									with self.assertRaises(_UpperCAmelCase		):
											accumulator([tf.constant([1.0, 1.0]		), tf.constant([2.0, 2.0]		)]		)
									self.assertEqual(accumulator.step     ,3		)
									self.assertEqual(len(accumulator.gradients		)     ,1		)
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,[-2.0, 5.0]     ,tol=1E-2		)
									accumulator.reset()
									self.assertEqual(accumulator.step     ,0		)
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,[0.0, 0.0]     ,tol=1E-2		)







							def 							__lowercase      (							self :	Any		):
									_a					:				int				=							None
									ops.enable_eager_execution_internal()
									_a					:				Optional[int]				=							tf.config.list_physical_devices('CPU'		)
									if len(_UpperCAmelCase		) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0]     ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
									_a					:				Tuple				=							tf.config.list_logical_devices(device_type='CPU'		)
									_a					:				List[str]				=							tf.distribute.MirroredStrategy(devices=devices[:2]		)

									with strategy.scope():
											_a					:				Tuple				=							GradientAccumulator()
											_a					:				List[Any]				=							tf.Variable([4.0, 3.0]		)
											_a	, _a					:				Dict				=							create_optimizer(5E-5     ,10     ,5		)
											_a					:				Tuple				=							tf.Variable([0.0, 0.0]     ,trainable=_UpperCAmelCase		)

									def accumulate_on_replica(_UpperCAmelCase :	str		):
											accumulator([gradient]		)

									def apply_on_replica():
											optimizer.apply_gradients(list(zip(accumulator.gradients     ,[variable]		)		)		)

									@tf.function
									def accumulate(_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]		):
											with strategy.scope():
													_a					:				Union[str, Any]				=							strategy.experimental_local_results(_UpperCAmelCase		)
													local_variables[0].assign(_UpperCAmelCase		)
													local_variables[1].assign(_UpperCAmelCase		)
													strategy.run(_UpperCAmelCase     ,args=(gradient_placeholder,)		)

									@tf.function
									def apply_grad():
											with strategy.scope():
													strategy.run(_UpperCAmelCase		)

									def _check_local_values(_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]		):
											_a					:				List[Any]				=							strategy.experimental_local_results(accumulator._gradients[0]		)
											self.assertListAlmostEqual(values[0].value()     ,_UpperCAmelCase     ,tol=1E-2		)
											self.assertListAlmostEqual(values[1].value()     ,_UpperCAmelCase     ,tol=1E-2		)

									accumulate([1.0, 2.0]     ,[-1.0, 1.0]		)
									accumulate([3.0, -1.0]     ,[-1.0, -1.0]		)
									accumulate([-2.0, 2.0]     ,[3.0, -2.0]		)
									self.assertEqual(accumulator.step     ,3		)
									_check_local_values([2.0, 3.0]     ,[1.0, -2.0]		)
									apply_grad()
									self.assertListAlmostEqual(variable.value()     ,[4.0, 3.0]     ,tol=1E-2		)
									accumulator.reset()
									self.assertEqual(accumulator.step     ,0		)
									_check_local_values([0.0, 0.0]     ,[0.0, 0.0]		)




