




'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
         TFDebertaModel,
     )





class 			lowerCAmelCase      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=13			,     __SCREAMING_SNAKE_CASE=7			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=99			,     __SCREAMING_SNAKE_CASE=32			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=4			,     __SCREAMING_SNAKE_CASE=37			,     __SCREAMING_SNAKE_CASE="gelu"			,     __SCREAMING_SNAKE_CASE=0.1			,     __SCREAMING_SNAKE_CASE=0.1			,     __SCREAMING_SNAKE_CASE=512			,     __SCREAMING_SNAKE_CASE=16			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=0.02			,     __SCREAMING_SNAKE_CASE=3			,     __SCREAMING_SNAKE_CASE=4			,     __SCREAMING_SNAKE_CASE=None			,     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     parent
            __snake_case			    =     13
            __snake_case			    =     7
            __snake_case			    =     True
            __snake_case			    =     True
            __snake_case			    =     True
            __snake_case			    =     True
            __snake_case			    =     99
            __snake_case			    =     32
            __snake_case			    =     2
            __snake_case			    =     4
            __snake_case			    =     37
            __snake_case			    =     '''gelu'''
            __snake_case			    =     0.1
            __snake_case			    =     0.1
            __snake_case			    =     512
            __snake_case			    =     16
            __snake_case			    =     False
            __snake_case			    =     -1
            __snake_case			    =     True
            __snake_case			    =     2
            __snake_case			    =     0.02
            __snake_case			    =     3
            __snake_case			    =     4
            __snake_case			    =     None


       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




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

            __snake_case			    =     None
            if self.use_input_mask:
                 __snake_case			    =     random_attention_mask([self.batch_size, self.seq_length]     )

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

            __snake_case			    =     None
            __snake_case			    =     None
            __snake_case			    =     None
            if self.use_labels:
                 __snake_case			    =     ids_tensor([self.batch_size]			,     self.type_sequence_label_size     )
                 __snake_case			    =     ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels     )

            __snake_case			    =     DebertaConfig(
                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			,     relative_attention=self.relative_attention			,     max_relative_positions=self.max_relative_positions			,     position_biased_input=self.position_biased_input			,     initializer_range=self.initializer_range			,     return_dict=_a			,     )

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Optional[int]:



            '''simple docstring'''




            __snake_case			    =     TFDebertaModel(config=_a     )
            __snake_case			    =     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

            __snake_case			    =     [input_ids, input_mask]
            __snake_case			    =     model(_a     )

            __snake_case			    =     model(_a     )

            self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     int:



            '''simple docstring'''




            __snake_case			    =     TFDebertaForMaskedLM(config=_a     )
            __snake_case			    =     {
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            __snake_case			    =     model(_a     )
            self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     self.num_labels
            __snake_case			    =     TFDebertaForSequenceClassification(config=_a     )
            __snake_case			    =     {
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            __snake_case			    =     model(_a     )
            self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Optional[Any]:



            '''simple docstring'''




            __snake_case			    =     self.num_labels
            __snake_case			    =     TFDebertaForTokenClassification(config=_a     )
            __snake_case			    =     {
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            __snake_case			    =     model(_a     )
            self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     TFDebertaForQuestionAnswering(config=_a     )
            __snake_case			    =     {
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            __snake_case			    =     model(_a     )
            self.parent.assertEqual(result.start_logits.shape			,     (self.batch_size, self.seq_length)     )
            self.parent.assertEqual(result.end_logits.shape			,     (self.batch_size, self.seq_length)     )




       def 					lowerCAmelCase					(      self     )					->     List[Any]:



            '''simple docstring'''




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





@require_tf
class 			lowerCAmelCase      (       UpperCamelCase__ ,      UpperCamelCase__ ,      unittest.TestCase):
       __lowercase     :					Optional[Any]       				=	(
           (
               TFDebertaModel,
               TFDebertaForMaskedLM,
               TFDebertaForQuestionAnswering,
               TFDebertaForSequenceClassification,
               TFDebertaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       __lowercase     :					int       				=	(
           {
               '''feature-extraction''': TFDebertaModel,
               '''fill-mask''': TFDebertaForMaskedLM,
               '''question-answering''': TFDebertaForQuestionAnswering,
               '''text-classification''': TFDebertaForSequenceClassification,
               '''token-classification''': TFDebertaForTokenClassification,
               '''zero-shot''': TFDebertaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       __lowercase     :					str       				=	False
       __lowercase     :					Union[str, Any]       				=	False


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



            '''simple docstring'''




            __snake_case			    =     TFDebertaModelTester(self     )
            __snake_case			    =     ConfigTester(self			,     config_class=_a			,     hidden_size=37     )


       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




            self.config_tester.run_common_tests()


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



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_a     )


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*_a     )


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*_a     )


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



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*_a     )


       def 					lowerCAmelCase					(      self     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*_a     )




       @slow
       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''     )
            self.assertIsNotNone(_a     )





@require_tf
class 			lowerCAmelCase      (       unittest.TestCase):


       @unittest.skip(reason='''Model not available yet'''     )
       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            pass




       @slow
       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




            __snake_case			    =     TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''     )
            __snake_case			    =     tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     )
            __snake_case			    =     tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
            __snake_case			    =     model(_a			,     attention_mask=_a     )[0]

            __snake_case			    =     tf.constant(
                [
                    [
                        [-0.59_855_896, -0.80_552_566, -0.8_462_135],
                        [1.4_484_025, -0.93_483_794, -0.80_593_085],
                        [0.3_122_741, 0.00_316_059, -1.4_131_377],
                    ]
                ]     )
            tf.debugging.assert_near(output[:, 1:4, 1:4]			,     _a			,     atol=1E-4     )










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_  :	Tuple							  =							logging.get_logger(__name__)

snake_case_  :	int							  =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Dict							  =							{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					Tuple=None ,			_a    :					Union[str, Any]=None ,			_a    :					Dict=False ,			_a    :					int=True ,			_a    :					str=False ,			_a    :					int="<s>" ,			_a    :					int="</s>" ,			_a    :					Optional[int]="<unk>" ,			_a    :					Dict="<sep>" ,			_a    :					Optional[int]="<pad>" ,			_a    :					str="<cls>" ,			_a    :					List[Any]="<mask>" ,			_a    :					Optional[Any]=["<eop>", "<eod>"] ,			**_a    :					Any ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    vocab_file=_a ,			tokenizer_file=_a ,			do_lower_case=_a ,			remove_space=_a ,			keep_accents=_a ,			bos_token=_a ,			eos_token=_a ,			unk_token=_a ,			sep_token=_a ,			pad_token=_a ,			cls_token=_a ,			mask_token=_a ,			additional_special_tokens=_a ,			**_a ,			)

										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =do_lower_case
										_SCREAMING_SNAKE_CASE	     =remove_space
										_SCREAMING_SNAKE_CASE	     =keep_accents
										_SCREAMING_SNAKE_CASE	     =vocab_file
										_SCREAMING_SNAKE_CASE	     =False if not self.vocab_file else True

										try:
														import jieba
										except ModuleNotFoundError as error:
														raise error.__class__(
														    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
														    '''See https://pypi.org/project/jieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =jieba
										_SCREAMING_SNAKE_CASE	     =str.maketrans(''' \n''' ,			'''\u2582\u2583'''     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[2]

										if token_ids_a is None:
														return len(token_ids_a + sep     ) * [0] + cls_segment_id
										return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id




						def 							__UpperCamelCase (				self    :					str ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''     )

										if not os.path.isdir(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
														return
										_SCREAMING_SNAKE_CASE	     =os.path.join(
										    _a ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(_a     ):
														copyfile(self.vocab_file ,			_a     )

										return (out_vocab_file,)




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			*_a    :					Optional[int] ,			**_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[
										    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_a ,			cut_all=_a     )]     )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_a ,			*_a ,			**_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			*_a    :					Union[str, Any] ,			**_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =super()._decode(*_a ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =text.replace(''' ''' ,			''''''     ).replace('''\u2582''' ,			''' '''     ).replace('''\u2583''' ,			'''\n'''     )
										return text