



'''simple docstring'''

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


__lowerCAmelCase		    =     logging.get_logger(__name__)

__lowerCAmelCase		    =     {
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}

class 			__magic_name__		(					_UpperCamelCase     ):
       lowerCAmelCase :					str            =      'gpt2'
       lowerCAmelCase :					Optional[int]            =      ['past_key_values']
       lowerCAmelCase :					str            =      {
           'hidden_size': 'n_embd',
           'max_position_embeddings': 'n_positions',
           'num_attention_heads': 'n_head',
           'num_hidden_layers': 'n_layer',
       }







       def __init__(							self      : Optional[int]			,_UpperCAmelCase      : Union[str, Any]=50257			,_UpperCAmelCase      : List[str]=1024			,_UpperCAmelCase      : List[str]=768			,_UpperCAmelCase      : int=12			,_UpperCAmelCase      : Optional[Any]=12			,_UpperCAmelCase      : Tuple=None			,_UpperCAmelCase      : List[str]="gelu_new"			,_UpperCAmelCase      : Tuple=0.1			,_UpperCAmelCase      : Any=0.1			,_UpperCAmelCase      : List[str]=0.1			,_UpperCAmelCase      : Dict=1E-5			,_UpperCAmelCase      : Union[str, Any]=0.02			,_UpperCAmelCase      : Tuple="cls_index"			,_UpperCAmelCase      : Any=True			,_UpperCAmelCase      : Optional[int]=None			,_UpperCAmelCase      : List[Any]=True			,_UpperCAmelCase      : Union[str, Any]=0.1			,_UpperCAmelCase      : Optional[Any]=True			,_UpperCAmelCase      : Any=True			,_UpperCAmelCase      : str=50256			,_UpperCAmelCase      : Optional[int]=50256			,_UpperCAmelCase      : Any=False			,_UpperCAmelCase      : List[Any]=False			,**_UpperCAmelCase      : List[Any]			,):
         _a  :   List[str]				 =							vocab_size
         _a  :   int				 =							n_positions
         _a  :   Optional[Any]				 =							n_embd
         _a  :   str				 =							n_layer
         _a  :   Dict				 =							n_head
         _a  :   int				 =							n_inner
         _a  :   Optional[int]				 =							activation_function
         _a  :   Optional[int]				 =							resid_pdrop
         _a  :   int				 =							embd_pdrop
         _a  :   Dict				 =							attn_pdrop
         _a  :   int				 =							layer_norm_epsilon
         _a  :   Optional[int]				 =							initializer_range
         _a  :   Union[str, Any]				 =							summary_type
         _a  :   List[str]				 =							summary_use_proj
         _a  :   Optional[int]				 =							summary_activation
         _a  :   Optional[Any]				 =							summary_first_dropout
         _a  :   Dict				 =							summary_proj_to_labels
         _a  :   List[Any]				 =							scale_attn_weights
         _a  :   int				 =							use_cache
         _a  :   str				 =							scale_attn_by_inverse_layer_idx
         _a  :   List[Any]				 =							reorder_and_upcast_attn

         _a  :   Any				 =							bos_token_id
         _a  :   Any				 =							eos_token_id

         super().__init__(bos_token_id=_UpperCAmelCase			,eos_token_id=_UpperCAmelCase			,**_UpperCAmelCase	)

class 			__magic_name__		(					_UpperCamelCase     ):







       def __init__(							self      : Optional[int]			,_UpperCAmelCase      : PretrainedConfig			,_UpperCAmelCase      : str = "default"			,_UpperCAmelCase      : List[PatchingSpec] = None			,_UpperCAmelCase      : bool = False			,):
         super().__init__(_UpperCAmelCase			,task=_UpperCAmelCase			,patching_specs=_UpperCAmelCase			,use_past=_UpperCAmelCase	)
         if not getattr(self._config			,'pad_token_id'			,_UpperCAmelCase	):
           # TODO: how to do that better?
           _a  :   Dict				 =							0







       @property
       def 							__lowercase    (							self      : str	):
         _a  :   Optional[int]				 =							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
         if self.use_past:
           self.fill_with_past_key_values_(_UpperCAmelCase			,direction='inputs'	)
           _a  :   List[Any]				 =							{0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _a  :   int				 =							{0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def 							__lowercase    (							self      : int	):
         return self._config.n_layer







       @property
       def 							__lowercase    (							self      : Optional[int]	):
         return self._config.n_head







       def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : PreTrainedTokenizer			,_UpperCAmelCase      : int = -1			,_UpperCAmelCase      : int = -1			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : Optional[TensorType] = None			,):
         _a  :   Tuple				 =							super(_UpperCAmelCase			,self	).generate_dummy_inputs(
             _UpperCAmelCase			,batch_size=_UpperCAmelCase			,seq_length=_UpperCAmelCase			,is_pair=_UpperCAmelCase			,framework=_UpperCAmelCase	)

         # We need to order the input in the way they appears in the forward()
         _a  :   Optional[int]				 =							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

             _a  :   Tuple				 =							common_inputs['input_ids'].shape
             # Not using the same length for past_key_values
             _a  :   Optional[Any]				 =							seqlen + 2
             _a  :   Optional[int]				 =							(
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             _a  :   Union[str, Any]				 =							[
                 (torch.zeros(_UpperCAmelCase	), torch.zeros(_UpperCAmelCase	)) for _ in range(self.num_layers	)
             ]

         _a  :   Tuple				 =							common_inputs['attention_mask']
         if self.use_past:
           _a  :   Tuple				 =							ordered_inputs['attention_mask'].dtype
           _a  :   List[Any]				 =							torch.cat(
               [ordered_inputs['attention_mask'], torch.ones(_UpperCAmelCase			,_UpperCAmelCase			,dtype=_UpperCAmelCase	)]			,dim=1	)

         return ordered_inputs







       @property
       def 							__lowercase    (							self      : Dict	):
         return 13









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




