
'''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


UpperCAmelCase_       :   Tuple   =       logging.get_logger(__name__)


UpperCAmelCase_       :   Dict   =       {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'codegen'
    lowerCAmelCase_				    =		{
        'max_position_embeddings': 'n_positions',
        'hidden_size': 'n_embd',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }



    def __init__(	self      :       Any,__A      :       Optional[int]=5_0_4_0_0,__A      :       Any=2_0_4_8,__A      :       str=2_0_4_8,__A      :       int=4_0_9_6,__A      :       Any=2_8,__A      :       str=1_6,__A      :       Optional[Any]=6_4,__A      :       str=None,__A      :       Optional[int]="gelu_new",__A      :       Optional[int]=0.0,__A      :       List[Any]=0.0,__A      :       str=0.0,__A      :       Union[str, Any]=1e-5,__A      :       Tuple=0.02,__A      :       str=True,__A      :       Any=5_0_2_5_6,__A      :       Optional[Any]=5_0_2_5_6,__A      :       int=False,**__A      :       Optional[int],):
          _lowerCamelCase				:   Any								=   vocab_size
          _lowerCamelCase				:   Tuple								=   n_ctx
          _lowerCamelCase				:   int								=   n_positions
          _lowerCamelCase				:   int								=   n_embd
          _lowerCamelCase				:   List[str]								=   n_layer
          _lowerCamelCase				:   Union[str, Any]								=   n_head
          _lowerCamelCase				:   List[Any]								=   n_inner
          _lowerCamelCase				:   str								=   rotary_dim
          _lowerCamelCase				:   Optional[int]								=   activation_function
          _lowerCamelCase				:   Any								=   resid_pdrop
          _lowerCamelCase				:   Dict								=   embd_pdrop
          _lowerCamelCase				:   Any								=   attn_pdrop
          _lowerCamelCase				:   Optional[int]								=   layer_norm_epsilon
          _lowerCamelCase				:   List[str]								=   initializer_range
          _lowerCamelCase				:   Optional[int]								=   use_cache

          _lowerCamelCase				:   List[str]								=   bos_token_id
          _lowerCamelCase				:   str								=   eos_token_id

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


class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       Any,__A      :       PretrainedConfig,__A      :       str = "default",__A      :       List[PatchingSpec] = None,__A      :       bool = False,):
          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?
                _lowerCamelCase				:   Any								=   0



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Union[str, Any]								=   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
          if self.use_past:
                self.fill_with_past_key_values_(__A,direction="inputs"     )
                _lowerCamelCase				:   Union[str, Any]								=   {0: "batch", 1: "past_sequence + sequence"}
          else:
                _lowerCamelCase				:   int								=   {0: "batch", 1: "sequence"}

          return common_inputs



    @property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          return self._config.n_layer



    @property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          return self._config.n_head



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       PreTrainedTokenizer,__A      :       int = -1,__A      :       int = -1,__A      :       bool = False,__A      :       Optional[TensorType] = None,):
          _lowerCamelCase				:   str								=   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()
          _lowerCamelCase				:   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

                      _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   common_inputs["input_ids"].shape
                      # Not using the same length for past_key_values
                      _lowerCamelCase				:   List[Any]								=   seqlen + 2
                      _lowerCamelCase				:   str								=   (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      _lowerCamelCase				:   Dict								=   [
                          (torch.zeros(__A     ), torch.zeros(__A     )) for _ in range(self.num_layers     )
                      ]

          _lowerCamelCase				:   Tuple								=   common_inputs["attention_mask"]
          if self.use_past:
                _lowerCamelCase				:   Optional[int]								=   ordered_inputs["attention_mask"].dtype
                _lowerCamelCase				:   Union[str, Any]								=   torch.cat(
                    [ordered_inputs["attention_mask"], torch.ones(__A,__A,dtype=__A     )],dim=1     )

          return ordered_inputs



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return 1_3

'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

UpperCAmelCase_       :   str   =       (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_       :   int   =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       Iterable[int]     ):
          _lowerCamelCase				:   Node | None								=   None
          for i in sorted(__A,reverse=__A     ):
                _lowerCamelCase				:   Dict								=   Node(__A,self.head     )



    def __iter__(	self      :       str     ):
          _lowerCamelCase				:   Dict								=   self.head
          while node:
                yield node.data
                _lowerCamelCase				:   Optional[Any]								=   node.next_node



    def __len__(	self      :       str     ):
          return sum(1 for _ in self     )



    def __str__(	self      :       str     ):
          return " -> ".join([str(__A     ) for node in self]     )







def 		A_       (	_lowerCAmelCase   :    SortedLinkedList				,	_lowerCAmelCase   :    SortedLinkedList							):



      """simple docstring"""




      return SortedLinkedList(list(_lowerCAmelCase							) + list(_lowerCAmelCase							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 UpperCAmelCase_       :   List[str]   =       SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))