




"""simple docstring"""




import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


a_        =       logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met

class   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						*UpperCamelCase_				,						UpperCamelCase_=None				,						UpperCamelCase_=None				,						UpperCamelCase_=None				,						**UpperCamelCase_       )						->       Tuple:
					super().__init__(*UpperCamelCase_				,						**UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       eval_examples
					__lowercase			:  Union[str, Any]								=       post_process_function
					__lowercase			:  Any								=       quant_trainer_args
					__lowercase			:  Optional[Any]								=       1_28  # default number of calibration samples
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_=None       )						->       Any:
					if calib_dataset is None and self.calib_dataset is None:
								raise ValueError('''Trainer: calibration requires an calib_dataset.'''       )
					__lowercase			:  Tuple								=       calib_dataset if calib_dataset is not None else self.calib_dataset

					__lowercase			:  str								=       self._remove_unused_columns(UpperCamelCase_				,						description='''Calibration'''       )

					return DataLoader(
					    UpperCamelCase_				,						batch_size=self.args.eval_batch_size				,						collate_fn=self.data_collator				,						drop_last=self.args.dataloader_drop_last				,						num_workers=self.args.dataloader_num_workers				,						pin_memory=self.args.dataloader_pin_memory				,						shuffle=UpperCamelCase_				,						)
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_=None       )						->       Any:
					__lowercase			:  Optional[int]								=       self.train_dataset if calib_dataset is None else calib_dataset
					__lowercase			:  List[Any]								=       self.get_calib_dataloader(UpperCamelCase_       )

					__lowercase			:  Dict								=       self.model
					quant_trainer.configure_model(UpperCamelCase_				,						self.quant_trainer_args				,						calib=UpperCamelCase_       )
					model.eval()
					quant_trainer.enable_calibration(UpperCamelCase_       )

					logger.info('''***** Running calibration *****'''       )
					logger.info(F"""  Num examples = {self.calib_num}"""       )
					logger.info(F"""  Batch size = {calib_dataloader.batch_size}"""       )

					for step, inputs in enumerate(UpperCamelCase_       ):
								# Prediction step
								__lowercase			,__lowercase			,__lowercase			:  Optional[Any]								=       self.prediction_step(UpperCamelCase_				,						UpperCamelCase_				,						prediction_loss_only=UpperCamelCase_       )
								if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
											break

					quant_trainer.finish_calibration(UpperCamelCase_				,						self.quant_trainer_args       )
					__lowercase			:  Tuple								=       model
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_=None				,						UpperCamelCase_=None				,						UpperCamelCase_=None				,						UpperCamelCase_ = "eval"       )						->       str:
					__lowercase			:  Tuple								=       self.eval_dataset if eval_dataset is None else eval_dataset
					__lowercase			:  Union[str, Any]								=       self.get_eval_dataloader(UpperCamelCase_       )
					__lowercase			:  str								=       self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					__lowercase			:  Optional[int]								=       self.compute_metrics
					__lowercase			:  Dict								=       None
					__lowercase			:  List[str]								=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
								__lowercase			:  Tuple								=       eval_loop(
								    UpperCamelCase_				,						description='''Evaluation'''				,						prediction_loss_only=True if compute_metrics is None else None				,						ignore_keys=UpperCamelCase_				,						)
					finally:
								__lowercase			:  List[str]								=       compute_metrics

					if self.post_process_function is not None and self.compute_metrics is not None:
								__lowercase			:  int								=       self.post_process_function(UpperCamelCase_				,						UpperCamelCase_				,						output.predictions       )
								__lowercase			:  Optional[int]								=       self.compute_metrics(UpperCamelCase_       )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()       ):
											if not key.startswith(F"""{metric_key_prefix}_"""       ):
														__lowercase			:  List[str]								=       metrics.pop(UpperCamelCase_       )

								self.log(UpperCamelCase_       )
					else:
								__lowercase			:  Dict								=       {}

					if self.args.tpu_metrics_debug or self.args.debug:
								# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
								xm.master_print(met.metrics_report()       )

					__lowercase			:  int								=       self.callback_handler.on_evaluate(self.args				,						self.state				,						self.control				,						UpperCamelCase_       )
					return metrics
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_=None				,						UpperCamelCase_ = "test"       )						->       List[Any]:
					__lowercase			:  Optional[int]								=       self.get_test_dataloader(UpperCamelCase_       )

					# Temporarily disable metric computation, we will do it in the loop here.
					__lowercase			:  str								=       self.compute_metrics
					__lowercase			:  Dict								=       None
					__lowercase			:  List[str]								=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
								__lowercase			:  Union[str, Any]								=       eval_loop(
								    UpperCamelCase_				,						description='''Prediction'''				,						prediction_loss_only=True if compute_metrics is None else None				,						ignore_keys=UpperCamelCase_				,						)
					finally:
								__lowercase			:  Any								=       compute_metrics

					if self.post_process_function is None or self.compute_metrics is None:
								return output

					__lowercase			:  Dict								=       self.post_process_function(UpperCamelCase_				,						UpperCamelCase_				,						output.predictions				,						'''predict'''       )
					__lowercase			:  Optional[int]								=       self.compute_metrics(UpperCamelCase_       )

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()       ):
								if not key.startswith(F"""{metric_key_prefix}_"""       ):
											__lowercase			:  List[str]								=       metrics.pop(UpperCamelCase_       )

					return PredictionOutput(predictions=predictions.predictions				,						label_ids=predictions.label_ids				,						metrics=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_="./"       )						->       int:
					__lowercase			:  Optional[int]								=       self.eval_dataset
					__lowercase			:  Optional[int]								=       self.get_eval_dataloader(UpperCamelCase_       )

					__lowercase			:  Any								=       next(iter(UpperCamelCase_       )       )

					# saving device - to make it consistent
					__lowercase			:  Any								=       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )

					# convert to tuple
					__lowercase			:  Tuple								=       tuple(v.to(UpperCamelCase_       ) for k, v in batch.items()       )

					logger.info('''Converting model to be onnx compatible'''       )
					from pytorch_quantization.nn import TensorQuantizer

					__lowercase			:  List[Any]								=       True

					__lowercase			:  int								=       self.model.to(UpperCamelCase_       )

					model.eval()
					model.float()

					__lowercase			:  Optional[int]								=       model.module if hasattr(UpperCamelCase_				,						'''module'''       ) else model
					quant_trainer.configure_model(UpperCamelCase_				,						self.quant_trainer_args       )

					__lowercase			:  Tuple								=       os.path.join(UpperCamelCase_				,						'''model.onnx'''       )
					logger.info(F"""exporting model to {output_model_file}"""       )

					__lowercase			:  Tuple								=       {0: '''batch_size''', 1: '''seq_len'''}

					torch.onnx.export(
					    UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						export_params=UpperCamelCase_				,						opset_version=13				,						do_constant_folding=UpperCamelCase_				,						input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']				,						output_names=['''output_start_logits''', '''output_end_logits''']				,						dynamic_axes={
					        '''input_ids''': axes,
					        '''attention_mask''': axes,
					        '''token_type_ids''': axes,
					        '''output_start_logits''': axes,
					        '''output_end_logits''': axes,
					    }				,						verbose=UpperCamelCase_				,						)
					logger.info('''onnx export finished'''       )










from __future__ import annotations


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[float]   ,    _lowercase     : Tuple			)   ->							int:




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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : list[float]   ,    _lowercase     : int			)   ->							Any:




		'''simple docstring'''
		for j in range(_lowercase			):
				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		(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       SCREAMING_SNAKE_CASE__  (       _lowercase     : list[dict[str, int]]   ,    _lowercase     : int   ,    _lowercase     : int   ,    _lowercase     : int			)   ->							list[float]:




		'''simple docstring'''
		lowercase__					:      Dict      =		[float('inf'			)] * vertex_count
		lowercase__					:      Dict      =		0.0

		for _ in range(vertex_count - 1			):
				for j in range(_lowercase			):
						lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		(graph[j][k] for k in ['src', 'dst', 'weight'])

						if distance[u] != float('inf'			) and distance[u] + w < distance[v]:
								lowercase__					:      str      =		distance[u] + w

		lowercase__					:      str      =		check_negative_cycle(_lowercase   ,    _lowercase   ,    _lowercase			)
		if negative_cycle_exists:
				raise Exception('Negative cycle found'			)

		return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__UpperCamelCase:	Optional[int] = int(input("""Enter number of vertices: """).strip())
			__UpperCamelCase:	Union[str, Any] = int(input("""Enter number of edges: """).strip())

			__UpperCamelCase:	list[dict[str, int]] = [{} for _ in range(E)]

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

			__UpperCamelCase:	Optional[int] = int(input("""\nEnter shortest path source:""").strip())
			__UpperCamelCase:	Dict = bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)


