




'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class 		SCREAMING_SNAKE_CASE__  :





			def __init__(      self						:    List[Any]      ,		a_						:    Optional[Any]=2      ,		a_						:    Tuple=3      ,		a_						:    Any=64      ,		a_						:    str=None							):



						"""simple docstring"""




						__snake_case			=		np.random.default_rng(a_							)
						__snake_case			=		length
						__snake_case			=		rng.normal(size=(length,)							).astype(np.floataa							)
						__snake_case			=		a * self.x + b + rng.normal(scale=0.1      ,		size=(length,)							).astype(np.floataa							)





			def __len__(      self						:    Optional[Any]							):



						"""simple docstring"""




						return self.length






			def __getitem__(      self						:    Optional[Any]      ,		a_						:    List[str]							):



						"""simple docstring"""




						return {"x": self.x[i], "y": self.y[i]}







class 		SCREAMING_SNAKE_CASE__  (			torch.nn.Module	):





			def __init__(      self						:    Any      ,		a_						:    List[str]=0      ,		a_						:    Union[str, Any]=0      ,		a_						:    Tuple=False							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
						__snake_case			=		torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
						__snake_case			=		True






			def 							A (      self						:    List[str]      ,		a_						:    int=None							):



						"""simple docstring"""




						if self.first_batch:
									print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
									__snake_case			=		False
						return x * self.a[0] + self.b[0]







class 		SCREAMING_SNAKE_CASE__  (			torch.nn.Module	):





			def __init__(      self						:    Dict      ,		a_						:    Dict=0      ,		a_						:    Any=0      ,		a_						:    Optional[Any]=False							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		torch.nn.Parameter(torch.tensor(a_							).float()							)
						__snake_case			=		torch.nn.Parameter(torch.tensor(a_							).float()							)
						__snake_case			=		True






			def 							A (      self						:    Dict      ,		a_						:    Any=None							):



						"""simple docstring"""




						if self.first_batch:
									print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
									__snake_case			=		False
						return x * self.a + self.b







def __UpperCAmelCase	(   _UpperCAmelCase		:   List[Any]		,				_UpperCAmelCase		:   int = 16						)      ->      str:
			from datasets import load_dataset
			from transformers import AutoTokenizer

			__snake_case			=		AutoTokenizer.from_pretrained("bert-base-cased"						)
			__snake_case			=		{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			__snake_case			=		load_dataset("csv"		,				data_files=_UpperCAmelCase						)
			__snake_case			=		datasets["train"].unique("label"						)

			__snake_case			=		{v: i for i, v in enumerate(_UpperCAmelCase						)}

			def tokenize_function(_UpperCAmelCase		:   List[str]						):
						# max_length=None => use the model max length (it's actually the default)
						__snake_case			=		tokenizer(
						    examples["sentence1"]		,				examples["sentence2"]		,				truncation=_UpperCAmelCase		,				max_length=_UpperCAmelCase		,				padding="max_length"						)
						if "label" in examples:
									__snake_case			=		[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__snake_case			=		datasets.map(
			    _UpperCAmelCase		,				batched=_UpperCAmelCase		,				remove_columns=["sentence1", "sentence2", "label"]		,				)

			def collate_fn(_UpperCAmelCase		:   Optional[Any]						):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCAmelCase		,				padding="max_length"		,				max_length=1_28		,				return_tensors="pt"						)
						return tokenizer.pad(_UpperCAmelCase		,				padding="longest"		,				return_tensors="pt"						)

			# Instantiate dataloaders.
			__snake_case			=		DataLoader(tokenized_datasets["train"]		,				shuffle=_UpperCAmelCase		,				collate_fn=_UpperCAmelCase		,				batch_size=2						)
			__snake_case			=		DataLoader(tokenized_datasets["validation"]		,				shuffle=_UpperCAmelCase		,				collate_fn=_UpperCAmelCase		,				batch_size=1						)

			return train_dataloader, eval_dataloader









"""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)
