
from __future__ import annotations

from typing import Any

class __snake_case		:
    def __init__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      num_of_nodes
           lowercase							:     list[list[int]]       	=      []
           lowercase							:     dict[int, int]       	=      {}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           self.m_edges.append([u_node, v_node, weight]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if self.m_component[u_node] == u_node:
                  return u_node
           return self.find_component(self.m_component[u_node]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if self.m_component[u_node] != u_node:
                  for k in self.m_component:
                         lowercase							:     Dict       	=      self.find_component(SCREAMING_SNAKE_CASE_      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if component_size[u_node] <= component_size[v_node]:
                  lowercase							:     Tuple       	=      v_node
                  component_size[v_node] += component_size[u_node]
                  self.set_component(SCREAMING_SNAKE_CASE_      )

           elif component_size[u_node] >= component_size[v_node]:
                  lowercase							:     Union[str, Any]       	=      self.find_component(SCREAMING_SNAKE_CASE_      )
                  component_size[u_node] += component_size[v_node]
                  self.set_component(SCREAMING_SNAKE_CASE_      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      []
           lowercase							:     int       	=      0

           lowercase							:     list[Any]       	=      [-1] * self.m_num_of_nodes

           # A list of components (initialized to all of the nodes)
           for node in range(self.m_num_of_nodes      ):
                  self.m_component.update({node: node}      )
                  component_size.append(1      )

           lowercase							:     List[str]       	=      self.m_num_of_nodes

           while num_of_components > 1:
                  for edge in self.m_edges:
                         lowercase							:     List[Any]       	=      edge

                         lowercase							:     str       	=      self.m_component[u]
                         lowercase							:     Any       	=      self.m_component[v]

                         if u_component != v_component:

                                for component in (u_component, v_component):
                                       if (
                                           minimum_weight_edge[component] == -1
                                           or minimum_weight_edge[component][2] > w
                                       ):
                                              lowercase							:     Any       	=      [u, v, w]

                  for edge in minimum_weight_edge:
                         if isinstance(SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_      ):
                                lowercase							:     int       	=      edge

                                lowercase							:     List[Any]       	=      self.m_component[u]
                                lowercase							:     Tuple       	=      self.m_component[v]

                                if u_component != v_component:
                                       mst_weight += w
                                       self.union(SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_      )
                                       print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"      )
                                       num_of_components -= 1

                  lowercase							:     Optional[Any]       	=      [-1] * self.m_num_of_nodes
           print(f"The total weight of the minimal spanning tree is: {mst_weight}"      )







def 						_snake_case(  ) ->    None:
       pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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          =       logging.get_logger(__name__)


__UpperCAmelCase          =       {
    '''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 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

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






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  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

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

          UpperCamelCase    :  List[Any]							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  Optional[Any]							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






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



