


"""simple docstring"""

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
       import timm


if is_torch_available():
       from torch import Tensor





class  lowerCamelCase__    (		_a      , _a     ):
      a			:  Union[str, Any]           =		"""pixel_values"""
      a			:  Union[str, Any]           =		False
      a			:  List[str]           =		TimmBackboneConfig
      def __init__(		self						:						List[str]   ,							A_						:						int   ,							**A_						:						Dict				):






       '''simple docstring'''


       requires_backends(self   ,							"""timm"""				)
       super().__init__(A_				)
       __lowercase						=						config

       if config.backbone is None:
        raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""				)

       if config.backbone not in timm.list_models():
        raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''				)

       if hasattr(A_   ,							"""out_features"""				) and config.out_features is not None:
        raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""				)

       __lowercase						=						getattr(A_   ,							"""use_pretrained_backbone"""   ,							A_				)
       if pretrained is None:
        raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""				)

       # We just take the final layer by default. This matches the default for the transformers models.
       __lowercase						=						config.out_indices if getattr(A_   ,							"""out_indices"""   ,							A_				) is not None else (-1,)

       __lowercase						=						timm.create_model(
           config.backbone   ,							pretrained=A_   ,							features_only=config.features_only   ,							in_chans=config.num_channels   ,							out_indices=A_   ,							**A_   ,							)
       # These are used to control the output of the model when called. If output_hidden_states is True, then
       # return_layers is modified to include all layers.
       __lowercase						=						self._backbone.return_layers
       __lowercase						=						{layer["""module"""]: str(A_				) for i, layer in enumerate(self._backbone.feature_info.info				)}
       super()._init_backbone(A_				)
      @classmethod
      def SCREAMING_SNAKE_CASE_						(		cls						:						int   ,							A_						:						Union[str, Any]   ,							*A_						:						Optional[int]   ,							**A_						:						Union[str, Any]				):






       '''simple docstring'''


       requires_backends(cls   ,							["""vision""", """timm"""]				)
       from ...models.timm_backbone import TimmBackboneConfig

       __lowercase						=						kwargs.pop("""config"""   ,							TimmBackboneConfig()				)

       __lowercase						=						kwargs.pop("""use_timm_backbone"""   ,							A_				)
       if not use_timm:
        raise ValueError("""use_timm_backbone must be True for timm backbones"""				)

       __lowercase						=						kwargs.pop("""num_channels"""   ,							config.num_channels				)
       __lowercase						=						kwargs.pop("""features_only"""   ,							config.features_only				)
       __lowercase						=						kwargs.pop("""use_pretrained_backbone"""   ,							config.use_pretrained_backbone				)
       __lowercase						=						kwargs.pop("""out_indices"""   ,							config.out_indices				)
       __lowercase						=						TimmBackboneConfig(
           backbone=A_   ,							num_channels=A_   ,							features_only=A_   ,							use_pretrained_backbone=A_   ,							out_indices=A_   ,							)
       return super()._from_config(A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						int				):






       '''simple docstring'''


       pass






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Any   ,							A_						:						Union[str, Any]=None   ,							A_						:						Optional[Any]=None   ,							A_						:						Optional[Any]=None   ,							**A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						return_dict if return_dict is not None else self.config.use_return_dict
       __lowercase						=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       __lowercase						=						output_attentions if output_attentions is not None else self.config.output_attentions

       if output_attentions:
        raise ValueError("""Cannot output attentions for timm backbones at the moment"""				)

       if output_hidden_states:
        # We modify the return layers to include all the stages of the backbone
        __lowercase						=						self._all_layers
        __lowercase						=						self._backbone(A_   ,							**A_				)
        __lowercase						=						self._return_layers
        __lowercase						=						tuple(hidden_states[i] for i in self.out_indices				)
       else:
        __lowercase						=						self._backbone(A_   ,							**A_				)
        __lowercase						=						None

       __lowercase						=						tuple(A_				)
       __lowercase						=						tuple(A_				) if hidden_states is not None else None

       if not return_dict:
        __lowercase						=						(feature_maps,)
        if output_hidden_states:
         __lowercase						=						output + (hidden_states,)
        return output

       return BackboneOutput(feature_maps=A_   ,							hidden_states=A_   ,							attentions=A_				)




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 print(f'''Vertex\tShortest Distance from vertex {src}'''			)
 for i, d in enumerate(UpperCamelCase__			):
  print(f'''{i}\t\t{d}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])
  if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
   return True
 return False


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

 for _ in range(vertex_count - 1			):
  for j in range(UpperCamelCase__			):
   __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])

   if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
    __lowercase						=						distance[u] + w

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

       UpperCAmelCase__					=[{} for _ in range(E)]

       for i in range(E):
              print("Edge ", i + 1)
              UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=(
                  int(x)
                  for x in input("Enter source, destination, weight: ").strip().split(" ")
              )
              UpperCAmelCase__					={"src": src, "dst": dest, "weight": weight}

       UpperCAmelCase__					=int(input("\nEnter shortest path source:").strip())
       UpperCAmelCase__					=bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)
