





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  MgpstrTokenizer
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  False
       SCREAMING_SNAKE_CASE_       :				Any     						=  {}
       SCREAMING_SNAKE_CASE_       :				Dict     						=  False




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             super().setUp()

             # fmt: off
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
             # fmt: on
             __SCREAMING_SNAKE_CASE       :Any		    =  dict(zip(SCREAMING_SNAKE_CASE__		,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)

             __SCREAMING_SNAKE_CASE       :str		    =  os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES['''vocab_file''']							)
             with open(self.vocab_file		,'''w'''		,encoding='''utf-8'''							) as fp:
                   fp.write(json.dumps(SCREAMING_SNAKE_CASE__							) + '''\n'''							)




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             return MgpstrTokenizer.from_pretrained(self.tmpdirname		,**SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  '''tester'''
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''tester'''
             return input_text, output_text




       @unittest.skip('''MGP-STR always lower cases letters.'''							)
       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__							)
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  '''[SPECIAL_TOKEN]'''

                         tokenizer.add_special_tokens({'''cls_token''': special_token}							)
                         __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tokenizer.encode([special_token]		,add_special_tokens=SCREAMING_SNAKE_CASE__							)
                         self.assertEqual(len(SCREAMING_SNAKE_CASE__							)		,1							)

                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  tokenizer.decode(SCREAMING_SNAKE_CASE__		,skip_special_tokens=SCREAMING_SNAKE_CASE__							)
                         self.assertTrue(special_token not in decoded							)




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_input_output_texts(SCREAMING_SNAKE_CASE__							)

                         __SCREAMING_SNAKE_CASE       :int		    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer.encode(SCREAMING_SNAKE_CASE__		,add_special_tokens=SCREAMING_SNAKE_CASE__							)
                         self.assertListEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                         __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							)
                         self.assertNotEqual(len(SCREAMING_SNAKE_CASE__							)		,0							)
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.decode(SCREAMING_SNAKE_CASE__							)
                         self.assertIsInstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

                         self.assertEqual(text_a.replace(''' '''		,''''''							)		,SCREAMING_SNAKE_CASE__							)




       @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''							)
       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             pass




       @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''							)
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             pass






"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''xlm-prophetnet'''
       SCREAMING_SNAKE_CASE_       :				Any     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  {
           '''num_attention_heads''': '''num_encoder_attention_heads''',
       }




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = "gelu"		,SCREAMING_SNAKE_CASE__ = 3_05_22		,SCREAMING_SNAKE_CASE__ = 10_24		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 5_12		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1_28		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 2		,**SCREAMING_SNAKE_CASE__		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  num_encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[str]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Dict		    =  init_std  # Normal(0, this parameter)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  activation_function

             # parameters for xlmprophetnet
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ngram
             __SCREAMING_SNAKE_CASE       :int		    =  num_buckets
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  relative_max_distance
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  disable_ngram_loss
             __SCREAMING_SNAKE_CASE       :Dict		    =  eps

             # 3 Types of Dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dropout

             __SCREAMING_SNAKE_CASE       :int		    =  use_cache

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,add_cross_attention=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.num_encoder_layers + self.num_decoder_layers




       @num_hidden_layers.setter
       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             raise NotImplementedError(
                 '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                 ''' `num_decoder_layers`.'''							)