

import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.albert.modeling_flax_albert import (
        FlaxAlbertForMaskedLM,
        FlaxAlbertForMultipleChoice,
        FlaxAlbertForPreTraining,
        FlaxAlbertForQuestionAnswering,
        FlaxAlbertForSequenceClassification,
        FlaxAlbertForTokenClassification,
        FlaxAlbertModel,
    )


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=True						,       a=True						,       a=True						,       a=99						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=16						,       a=2						,       a=0.02						,       a=4						,       ):
         lowercase__   :       Union[str, Any]				=	parent
         lowercase__   :       List[Any]				=	batch_size
         lowercase__   :       str				=	seq_length
         lowercase__   :       List[Any]				=	is_training
         lowercase__   :       Union[str, Any]				=	use_attention_mask
         lowercase__   :       str				=	use_token_type_ids
         lowercase__   :       str				=	use_labels
         lowercase__   :       Union[str, Any]				=	vocab_size
         lowercase__   :       List[str]				=	hidden_size
         lowercase__   :       Optional[Any]				=	num_hidden_layers
         lowercase__   :       Dict				=	num_attention_heads
         lowercase__   :       int				=	intermediate_size
         lowercase__   :       Union[str, Any]				=	hidden_act
         lowercase__   :       Tuple				=	hidden_dropout_prob
         lowercase__   :       Dict				=	attention_probs_dropout_prob
         lowercase__   :       List[Any]				=	max_position_embeddings
         lowercase__   :       Any				=	type_vocab_size
         lowercase__   :       Union[str, Any]				=	type_sequence_label_size
         lowercase__   :       Union[str, Any]				=	initializer_range
         lowercase__   :       int				=	num_choices





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       List[Any]				=	None
         if self.use_attention_mask:
              lowercase__   :       int				=	random_attention_mask([self.batch_size, self.seq_length])

         lowercase__   :       List[Any]				=	None
         if self.use_token_type_ids:
              lowercase__   :       int				=	ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size)

         lowercase__   :       Optional[int]				=	AlbertConfig(
             vocab_size=self.vocab_size						,       hidden_size=self.hidden_size						,       num_hidden_layers=self.num_hidden_layers						,       num_attention_heads=self.num_attention_heads						,       intermediate_size=self.intermediate_size						,       hidden_act=self.hidden_act						,       hidden_dropout_prob=self.hidden_dropout_prob						,       attention_probs_dropout_prob=self.attention_probs_dropout_prob						,       max_position_embeddings=self.max_position_embeddings						,       type_vocab_size=self.type_vocab_size						,       is_decoder=a						,       initializer_range=self.initializer_range						,       )

         return config, input_ids, token_type_ids, attention_mask





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.prepare_config_and_inputs()
         lowercase__   :       Dict				=	config_and_inputs
         lowercase__   :       Union[str, Any]				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict



@require_flax
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					(
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	FlaxAlbertModelTester(self)





    @slow
    def 				snake_case_    (				self):
         for model_class_name in self.all_model_classes:
              lowercase__   :       str				=	model_class_name.from_pretrained('albert-base-v2')
              lowercase__   :       Any				=	model(np.ones((1, 1)))
              self.assertIsNotNone(a)



@require_flax
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	FlaxAlbertModel.from_pretrained('albert-base-v2')
         lowercase__   :       List[Any]				=	np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
         lowercase__   :       str				=	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
         lowercase__   :       Optional[int]				=	model(a						,       attention_mask=a)[0]
         lowercase__   :       Any				=	(1, 11, 768)
         self.assertEqual(output.shape						,       a)
         lowercase__   :       Tuple				=	np.array(
             [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]])

         self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,       a						,       atol=1e-4))




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


snake_case_        =			logging.get_logger(__name__)

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

snake_case_        =			{
    '''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'''
        ),
    }
}

snake_case_        =			{
    '''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,
}


snake_case_        =			{
    '''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 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=True						,       a="[UNK]"						,       a="[SEP]"						,       a="[PAD]"						,       a="[CLS]"						,       a="[MASK]"						,       a=True						,       a=None						,       **a						,       ):
         super().__init__(
             a						,       tokenizer_file=a						,       do_lower_case=a						,       unk_token=a						,       sep_token=a						,       pad_token=a						,       cls_token=a						,       mask_token=a						,       tokenize_chinese_chars=a						,       strip_accents=a						,       **a						,       )

         lowercase__   :       Optional[Any]				=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
             pre_tok_state.get('lowercase'						,       a) != do_lower_case
             or pre_tok_state.get('strip_accents'						,       a) != strip_accents
         ):
              lowercase__   :       Optional[int]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       str				=	do_lower_case
              lowercase__   :       Union[str, Any]				=	strip_accents
              lowercase__   :       int				=	pre_tok_class(**a)

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, 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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

