





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  DebertaTokenizer
       SCREAMING_SNAKE_CASE_      : str      =  True
       SCREAMING_SNAKE_CASE_      : str      =  DebertaTokenizerFast







       def    __UpperCAmelCase (    self	:	int  )						->					Union[str, Any]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _lowercase							= [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '[UNK]',
         ]
         _lowercase							= dict(zip(__A						,range(len(__A  )  )  )  )
         _lowercase							= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         _lowercase							= {'unk_token': '[UNK]'}

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







       def    __UpperCAmelCase (    self	:	Any						,**__A	:	Dict  )						->					str:
         kwargs.update(self.special_tokens_map  )
         return self.tokenizer_class.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	int						,__A	:	List[Any]  )						->					List[Any]:
         _lowercase							= 'lower newer'
         _lowercase							= 'lower newer'
         return input_text, output_text







       def    __UpperCAmelCase (    self	:	Any  )						->					Optional[int]:
         _lowercase							= self.get_tokenizer()
         _lowercase							= 'lower newer'
         _lowercase							= ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         _lowercase							= tokenizer.tokenize(__A  )
         self.assertListEqual(__A						,__A  )

         _lowercase							= tokens + [tokenizer.unk_token]
         _lowercase							= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__A  )						,__A  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Optional[int]:
         _lowercase							= self.get_tokenizer()
         _lowercase							= tokenizer('Hello'						,'World'  )
         _lowercase							= [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['token_type_ids']						,__A  )







       @slow
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					int:
         _lowercase							= self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

         _lowercase							= tokenizer.encode('sequence builders'						,add_special_tokens=__A  )
         _lowercase							= tokenizer.encode('multi-sequence build'						,add_special_tokens=__A  )

         _lowercase							= tokenizer.encode(
             'sequence builders'						,add_special_tokens=__A						,add_prefix_space=__A  )
         _lowercase							= tokenizer.encode(
             'sequence builders'						,'multi-sequence build'						,add_special_tokens=__A						,add_prefix_space=__A  )

         _lowercase							= tokenizer.build_inputs_with_special_tokens(__A  )
         _lowercase							= tokenizer.build_inputs_with_special_tokens(__A						,__A  )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







       @slow
       def    __UpperCAmelCase (    self	:	Dict  )						->					Union[str, Any]:
         _lowercase							= [self.tokenizer_class]
         if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class  )

         for tokenizer_class in tokenizer_classes:
           _lowercase							= tokenizer_class.from_pretrained('microsoft/deberta-base'  )

           _lowercase							= [
               'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
               'ALBERT incorporates two parameter reduction techniques',
               'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
               ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
               ' vocabulary embedding.',
           ]

           _lowercase							= tokenizer(__A						,padding=__A  )
           _lowercase							= [tokenizer.decode(__A						,skip_special_tokens=__A  ) for seq in encoding['input_ids']]

           # fmt: off
           _lowercase							= {
               'input_ids': [
                   [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
               ],
               'token_type_ids': [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               'attention_mask': [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           _lowercase							= [
               'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
               'ALBERT incorporates two parameter reduction techniques',
               'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
               ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
               ' vocabulary embedding.',
           ]

           self.assertDictEqual(encoding.data						,__A  )

           for expected, decoded in zip(__A						,__A  ):
             self.assertEqual(__A						,__A  )






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  '''gptj'''
       SCREAMING_SNAKE_CASE_      : int      =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }







       def __init__(    self	:	str						,__A	:	List[str]=5_0400						,__A	:	Dict=2048						,__A	:	List[str]=4096						,__A	:	Dict=28						,__A	:	Optional[int]=16						,__A	:	Tuple=64						,__A	:	Optional[int]=None						,__A	:	Optional[int]="gelu_new"						,__A	:	Dict=0.0						,__A	:	List[str]=0.0						,__A	:	Optional[int]=0.0						,__A	:	str=1e-5						,__A	:	List[Any]=0.02						,__A	:	str=True						,__A	:	int=5_0256						,__A	:	Union[str, Any]=5_0256						,__A	:	int=False						,**__A	:	List[Any]						,)						->					str:
         _lowercase							= vocab_size
         _lowercase							= n_positions
         _lowercase							= n_embd
         _lowercase							= n_layer
         _lowercase							= n_head
         _lowercase							= n_inner
         _lowercase							= rotary_dim
         _lowercase							= activation_function
         _lowercase							= resid_pdrop
         _lowercase							= embd_pdrop
         _lowercase							= attn_pdrop
         _lowercase							= layer_norm_epsilon
         _lowercase							= initializer_range
         _lowercase							= use_cache

         _lowercase							= bos_token_id
         _lowercase							= eos_token_id

         super().__init__(
             bos_token_id=__A						,eos_token_id=__A						,tie_word_embeddings=__A						,**__A  )





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Tuple						,__A	:	PretrainedConfig						,__A	:	str = "default"						,__A	:	List[PatchingSpec] = None						,__A	:	bool = False						,)						->					Any:
         super().__init__(__A						,task=__A						,patching_specs=__A						,use_past=__A  )
         if not getattr(self._config						,'pad_token_id'						,__A  ):
           # TODO: how to do that better?
           _lowercase							= 0







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Mapping[str, Mapping[int, str]]:
         _lowercase							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
         if self.use_past:
           self.fill_with_past_key_values_(__A						,direction='inputs'  )
           _lowercase							= {0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _lowercase							= {0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self._config.n_layer







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return self._config.n_head







       def    __UpperCAmelCase (    self	:	Any						,__A	:	PreTrainedTokenizer						,__A	:	int = -1						,__A	:	int = -1						,__A	:	bool = False						,__A	:	Optional[TensorType] = None						,)						->					Mapping[str, Any]:
         _lowercase							= super(__A						,self  ).generate_dummy_inputs(
             __A						,batch_size=__A						,seq_length=__A						,is_pair=__A						,framework=__A  )

         # We need to order the input in the way they appears in the forward()
         _lowercase							= OrderedDict({'input_ids': common_inputs['input_ids']}  )

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
           else:
             import torch

             _lowercase						,		_lowercase							= common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _lowercase							= seqlen + 2
             _lowercase							= (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             _lowercase							= [
                 (torch.zeros(__A  ), torch.zeros(__A  )) for _ in range(self.num_layers  )
             ]

         _lowercase							= common_inputs['attention_mask']
         if self.use_past:
           _lowercase							= ordered_inputs['attention_mask'].dtype
           _lowercase							= torch.cat(
               [ordered_inputs['attention_mask'], torch.ones(__A						,__A						,dtype=__A  )]						,dim=1  )

         return ordered_inputs







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					int:
         return 13