





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.bert.modeling_flax_bert import (
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForNextSentencePrediction,
           FlaxBertForPreTraining,
           FlaxBertForQuestionAnswering,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertModel,
       )

class  __A(						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,       ):





            """simple docstring"""

            _UpperCamelCase              =						parent
            _UpperCamelCase              =						batch_size
            _UpperCamelCase              =						seq_length
            _UpperCamelCase              =						is_training
            _UpperCamelCase              =						use_attention_mask
            _UpperCamelCase              =						use_token_type_ids
            _UpperCamelCase              =						use_labels
            _UpperCamelCase              =						vocab_size
            _UpperCamelCase              =						hidden_size
            _UpperCamelCase              =						num_hidden_layers
            _UpperCamelCase              =						num_attention_heads
            _UpperCamelCase              =						intermediate_size
            _UpperCamelCase              =						hidden_act
            _UpperCamelCase              =						hidden_dropout_prob
            _UpperCamelCase              =						attention_probs_dropout_prob
            _UpperCamelCase              =						max_position_embeddings
            _UpperCamelCase              =						type_vocab_size
            _UpperCamelCase              =						type_sequence_label_size
            _UpperCamelCase              =						initializer_range
            _UpperCamelCase              =						num_choices

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						ids_tensor([self.batch_size, self.seq_length],       self.vocab_size      )

            _UpperCamelCase              =						None
            if self.use_attention_mask:
                   _UpperCamelCase              =						random_attention_mask([self.batch_size, self.seq_length]      )

            _UpperCamelCase              =						None
            if self.use_token_type_ids:
                   _UpperCamelCase              =						ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size      )

            _UpperCamelCase              =						BertConfig(
                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=__SCREAMING_SNAKE_CASE,       initializer_range=self.initializer_range,       )

            return config, input_ids, token_type_ids, attention_mask

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.prepare_config_and_inputs()
            _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						config_and_inputs
            _UpperCamelCase              =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.prepare_config_and_inputs()
            _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						config_and_inputs

            _UpperCamelCase              =						True
            _UpperCamelCase              =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
            _UpperCamelCase              =						ids_tensor([self.batch_size, self.seq_length],       vocab_size=2      )

            return (
                config,
                input_ids,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
            )






@require_flax
class  __A(						__UpperCAmelCase						,			unittest.TestCase ):
     __A	  =  True

     __A	  =  (
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						FlaxBertModelTester(self      )



     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						FlaxBertModel.from_pretrained('''bert-base-cased'''      )
            _UpperCamelCase              =						model(np.ones((1, 1)      )      )
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )













import json
import os
from typing import Optional, Tuple

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


lowercase    : str    	=  logging.get_logger(__name__)

lowercase    : Tuple    	=  {"""vocab_file""": """vocab.json"""}

lowercase    : int    	=  {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

lowercase    : int    	=  {"""mgp-str""": 27}

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(      self,       A,       A="[GO]",       A="[GO]",       A="[s]",       A="[GO]",       **A      ):





            """simple docstring"""

            super().__init__(
                unk_token=A,       bos_token=A,       eos_token=A,       pad_token=A,       **A,       )

            with open(A,       encoding='''utf-8'''      ) as vocab_handle:
                   _UpperCamelCase              =						json.load(A      )
            _UpperCamelCase              =						{v: k for k, v in self.vocab.items()}

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return len(self.vocab      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return dict(self.vocab,       **self.added_tokens_encoder      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						[]
            for s in text:
                   char_tokens.extend(A      )
            return char_tokens

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.vocab.get(A,       self.vocab.get(self.unk_token      )      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.decoder.get(A      )



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not os.path.isdir(A      ):
                   logger.error('''Vocabulary path ({}) should be a directory'''.format(A      )      )
                   return
            _UpperCamelCase              =						os.path.join(
                A,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

            with open(A,       '''w''',       encoding='''utf-8'''      ) as f:
                   f.write(json.dumps(self.vocab,       indent=2,       sort_keys=A,       ensure_ascii=A      ) + '''\n'''      )

            return (vocab_file,)






