





from __future__ import annotations

from collections.abc import MutableSequence





class 				A_				:







       """simple docstring"""





       def __init__(    self	:	int						,__A	:	int						,__A	:	MutableSequence[float]  )						->					None:
         if len(__A  ) != degree + 1:
           raise ValueError(
               'The number of coefficients should be equal to the degree + 1.'  )

         _lowercase							= list(__A  )
         _lowercase							= degree







       def __add__(    self	:	int						,__A	:	Polynomial  )						->					Polynomial:

         if self.degree > polynomial_a.degree:
           _lowercase							= self.coefficients[:]
           for i in range(polynomial_a.degree + 1  ):
             coefficients[i] += polynomial_a.coefficients[i]
           return Polynomial(self.degree						,__A  )
         else:
           _lowercase							= polynomial_a.coefficients[:]
           for i in range(self.degree + 1  ):
             coefficients[i] += self.coefficients[i]
           return Polynomial(polynomial_a.degree						,__A  )







       def __sub__(    self	:	List[Any]						,__A	:	Polynomial  )						->					Polynomial:
         return self + polynomial_a * Polynomial(0						,[-1]  )







       def __neg__(    self	:	Optional[Any]  )						->					Polynomial:
         return Polynomial(self.degree						,[-c for c in self.coefficients]  )







       def __mul__(    self	:	int						,__A	:	Polynomial  )						->					Polynomial:
         _lowercase							= [0] * (self.degree + polynomial_a.degree + 1)
         for i in range(self.degree + 1  ):
           for j in range(polynomial_a.degree + 1  ):
             coefficients[i + j] += (
                 self.coefficients[i] * polynomial_a.coefficients[j]
             )

         return Polynomial(self.degree + polynomial_a.degree						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	int | float  )						->					int | float:
         _lowercase							= 0
         for i in range(self.degree + 1  ):
           result += self.coefficients[i] * (substitution**i)
         return result







       def __str__(    self	:	List[str]  )						->					str:
         _lowercase							= ''
         for i in range(self.degree						,-1						,-1  ):
           if self.coefficients[i] == 0:
             continue
           elif self.coefficients[i] > 0:
             if polynomial:
               polynomial += " + "
           else:
             polynomial += " - "

           if i == 0:
             polynomial += str(abs(self.coefficients[i]  )  )
           elif i == 1:
             polynomial += str(abs(self.coefficients[i]  )  ) + "x"
           else:
             polynomial += str(abs(self.coefficients[i]  )  ) + "x^" + str(__A  )

         return polynomial







       def __repr__(    self	:	Optional[int]  )						->					str:
         return self.__str__()







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Polynomial:
         _lowercase							= [0] * self.degree
         for i in range(self.degree  ):
           _lowercase							= self.coefficients[i + 1] * (i + 1)
         return Polynomial(self.degree - 1						,__A  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	int | float = 0  )						->					Polynomial:
         _lowercase							= [0] * (self.degree + 2)
         _lowercase							= constant
         for i in range(self.degree + 1  ):
           _lowercase							= self.coefficients[i] / (i + 1)
         return Polynomial(self.degree + 1						,__A  )







       def __eq__(    self	:	Tuple						,__A	:	object  )						->					bool:
         if not isinstance(__A						,__A  ):
           return False

         if self.degree != polynomial_a.degree:
           return False

         for i in range(self.degree + 1  ):
           if self.coefficients[i] != polynomial_a.coefficients[i]:
             return False

         return True







       def __ne__(    self	:	str						,__A	:	object  )						->					bool:
         return not self.__eq__(__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