




'''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


_snake_case   :   List[str]     							=	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     A					(	_a					):







							def __init__(						self :						Dict							,    *lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Optional[Any]=None							,    lowerCAmelCase_ :						List[Any]=None							,    lowerCAmelCase_ :						Tuple=None							,    **lowerCAmelCase_ :						Dict      )    ->							Any:


														"""simple docstring"""


														super().__init__(*lowerCAmelCase_							,    **lowerCAmelCase_      )
														_a			  =       eval_examples
														_a			  =       post_process_function
														_a			  =       quant_trainer_args
														_a			  =       1_28  # default number of calibration samples







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Any=None      )    ->							Optional[int]:


														"""simple docstring"""


														if calib_dataset is None and self.calib_dataset is None:
																					raise ValueError('''Trainer: calibration requires an calib_dataset.'''      )
														_a			  =       calib_dataset if calib_dataset is not None else self.calib_dataset

														_a			  =       self._remove_unused_columns(lowerCAmelCase_							,    description='''Calibration'''      )

														return DataLoader(
														    lowerCAmelCase_							,    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=lowerCAmelCase_							,    )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						Dict=None      )    ->							int:


														"""simple docstring"""


														_a			  =       self.train_dataset if calib_dataset is None else calib_dataset
														_a			  =       self.get_calib_dataloader(lowerCAmelCase_      )

														_a			  =       self.model
														quant_trainer.configure_model(lowerCAmelCase_							,    self.quant_trainer_args							,    calib=lowerCAmelCase_      )
														model.eval()
														quant_trainer.enable_calibration(lowerCAmelCase_      )

														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(lowerCAmelCase_      ):
																					# Prediction step
																					_a  ,							_a  ,							_a			  =       self.prediction_step(lowerCAmelCase_							,    lowerCAmelCase_							,    prediction_loss_only=lowerCAmelCase_      )
																					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																												break

														quant_trainer.finish_calibration(lowerCAmelCase_							,    self.quant_trainer_args      )
														_a			  =       model







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						Tuple=None							,    lowerCAmelCase_ :						Tuple=None							,    lowerCAmelCase_ :						Tuple=None							,    lowerCAmelCase_ :						str = "eval"      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       self.eval_dataset if eval_dataset is None else eval_dataset
														_a			  =       self.get_eval_dataloader(lowerCAmelCase_      )
														_a			  =       self.eval_examples if eval_examples is None else eval_examples

														# Temporarily disable metric computation, we will do it in the loop here.
														_a			  =       self.compute_metrics
														_a			  =       None
														_a			  =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
														try:
																					_a			  =       eval_loop(
																					    lowerCAmelCase_							,    description='''Evaluation'''							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=lowerCAmelCase_							,    )
														finally:
																					_a			  =       compute_metrics

														if self.post_process_function is not None and self.compute_metrics is not None:
																					_a			  =       self.post_process_function(lowerCAmelCase_							,    lowerCAmelCase_							,    output.predictions      )
																					_a			  =       self.compute_metrics(lowerCAmelCase_      )

																					# Prefix all keys with metric_key_prefix + '_'
																					for key in list(metrics.keys()      ):
																												if not key.startswith(F'{metric_key_prefix}_'      ):
																																			_a			  =       metrics.pop(lowerCAmelCase_      )

																					self.log(lowerCAmelCase_      )
														else:
																					_a			  =       {}

														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()      )

														_a			  =       self.callback_handler.on_evaluate(self.args							,    self.state							,    self.control							,    lowerCAmelCase_      )
														return metrics







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						int=None							,    lowerCAmelCase_ :						str = "test"      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.get_test_dataloader(lowerCAmelCase_      )

														# Temporarily disable metric computation, we will do it in the loop here.
														_a			  =       self.compute_metrics
														_a			  =       None
														_a			  =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
														try:
																					_a			  =       eval_loop(
																					    lowerCAmelCase_							,    description='''Prediction'''							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=lowerCAmelCase_							,    )
														finally:
																					_a			  =       compute_metrics

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

														_a			  =       self.post_process_function(lowerCAmelCase_							,    lowerCAmelCase_							,    output.predictions							,    '''predict'''      )
														_a			  =       self.compute_metrics(lowerCAmelCase_      )

														# Prefix all keys with metric_key_prefix + '_'
														for key in list(metrics.keys()      ):
																					if not key.startswith(F'{metric_key_prefix}_'      ):
																												_a			  =       metrics.pop(lowerCAmelCase_      )

														return PredictionOutput(predictions=predictions.predictions							,    label_ids=predictions.label_ids							,    metrics=lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						Optional[int]="./"      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       self.eval_dataset
														_a			  =       self.get_eval_dataloader(lowerCAmelCase_      )

														_a			  =       next(iter(lowerCAmelCase_      )      )

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

														# convert to tuple
														_a			  =       tuple(v.to(lowerCAmelCase_      ) for k, v in batch.items()      )

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

														_a			  =       True

														_a			  =       self.model.to(lowerCAmelCase_      )

														model.eval()
														model.float()

														_a			  =       model.module if hasattr(lowerCAmelCase_							,    '''module'''      ) else model
														quant_trainer.configure_model(lowerCAmelCase_							,    self.quant_trainer_args      )

														_a			  =       os.path.join(lowerCAmelCase_							,    '''model.onnx'''      )
														logger.info(F'exporting model to {output_model_file}'      )

														_a			  =       {0: '''batch_size''', 1: '''seq_len'''}

														torch.onnx.export(
														    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    export_params=lowerCAmelCase_							,    opset_version=13							,    do_constant_folding=lowerCAmelCase_							,    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=lowerCAmelCase_							,    )
														logger.info('''onnx export finished'''      )







'''simple docstring'''


_snake_case   :   Any     							=	{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       set()
							# keep track of all the paths to be checked
							_a			  =       [[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														_a			  =       queue.pop(0   )
														# get the last node from the path
														_a			  =       path[-1]
														if node not in explored:
																					_a			  =       graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												_a			  =       list(UpperCamelCase   )
																												new_path.append(UpperCamelCase   )
																												queue.append(UpperCamelCase   )
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(UpperCamelCase   )

    # in case there's no path between the 2 nodes
							return []
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							_a			  =       [start]
							_a			  =       set(UpperCamelCase   )
							# Keep tab on distances from `start` node.
							_a			  =       {start: 0, target: -1}
							while queue:
														_a			  =       queue.pop(0   )
														if node == target:
																					_a			  =       (
																					    dist[node] if dist[target] == -1 else min(dist[target]				,		dist[node]   )
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(UpperCamelCase   )
																												queue.append(UpperCamelCase   )
																												_a			  =       dist[node] + 1
							return dist[target]


if __name__ == "__main__":
				print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
				print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4

