
'''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     __snake_case		(   a__):
					def __init__(     self,    *A,    A=None,    A=None,    A=None,    **A      ):

									"""simple docstring"""


									super().__init__(*A,    **A      )
									lowerCamelCase					:			List[Any]									= eval_examples
									lowerCamelCase					:			Dict									= post_process_function
									lowerCamelCase					:			int									= quant_trainer_args
									lowerCamelCase					:			Optional[int]									= 128  # default number of calibration samples
					def     UpperCAmelCase_							(     self,    A=None      ):

									"""simple docstring"""


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

									lowerCamelCase					:			str									= self._remove_unused_columns(A,    description='Calibration'      )

									return DataLoader(
									    A,    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=A,    )
					def     UpperCAmelCase_							(     self,    A=None      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.train_dataset if calib_dataset is None else calib_dataset
									lowerCamelCase					:			Any									= self.get_calib_dataloader(A      )

									lowerCamelCase					:			Tuple									= self.model
									quant_trainer.configure_model(A,    self.quant_trainer_args,    calib=A      )
									model.eval()
									quant_trainer.enable_calibration(A      )

									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(A      ):
													# Prediction step
													lowerCamelCase					:			List[str]									= self.prediction_step(A,    A,    prediction_loss_only=A      )
													if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																	break

									quant_trainer.finish_calibration(A,    self.quant_trainer_args      )
									lowerCamelCase					:			List[str]									= model
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    A=None,    A = "eval"      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.eval_dataset if eval_dataset is None else eval_dataset
									lowerCamelCase					:			List[str]									= self.get_eval_dataloader(A      )
									lowerCamelCase					:			Union[str, Any]									= self.eval_examples if eval_examples is None else eval_examples

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

									if self.post_process_function is not None and self.compute_metrics is not None:
													lowerCamelCase					:			str									= self.post_process_function(A,    A,    output.predictions      )
													lowerCamelCase					:			List[Any]									= self.compute_metrics(A      )

													# Prefix all keys with metric_key_prefix + '_'
													for key in list(metrics.keys()      ):
																	if not key.startswith(F'''{metric_key_prefix}_'''      ):
																					lowerCamelCase					:			Union[str, Any]									= metrics.pop(A      )

													self.log(A      )
									else:
													lowerCamelCase					:			List[str]									= {}

									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()      )

									lowerCamelCase					:			List[str]									= self.callback_handler.on_evaluate(self.args,    self.state,    self.control,    A      )
									return metrics
					def     UpperCAmelCase_							(     self,    A,    A,    A=None,    A = "test"      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.get_test_dataloader(A      )

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

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

									lowerCamelCase					:			Union[str, Any]									= self.post_process_function(A,    A,    output.predictions,    'predict'      )
									lowerCamelCase					:			Optional[int]									= self.compute_metrics(A      )

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

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




					def     UpperCAmelCase_							(     self,    A="./"      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.eval_dataset
									lowerCamelCase					:			Tuple									= self.get_eval_dataloader(A      )

									lowerCamelCase					:			Dict									= next(iter(A      )      )

									# saving device - to make it consistent
									lowerCamelCase					:			List[str]									= torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

									# convert to tuple
									lowerCamelCase					:			Union[str, Any]									= tuple(v.to(A      ) for k, v in batch.items()      )

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

									lowerCamelCase					:			Any									= True

									lowerCamelCase					:			Optional[int]									= self.model.to(A      )

									model.eval()
									model.float()

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

									lowerCamelCase					:			str									= os.path.join(A,    'model.onnx'      )
									logger.info(F'''exporting model to {output_model_file}'''      )

									lowerCamelCase					:			str									= {0: 'batch_size', 1: 'seq_len'}

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


'''simple docstring'''






A			      =      {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A			      =      ['a', 'b', 'c', 'd', 'e']





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			List[Any]									= start
				# add current to visited
				visited.append(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												lowerCamelCase					:			Any									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # if all neighbors visited add current to sort
				sort.append(UpperCAmelCase__)
				# if all vertices haven't been visited select a new one to visit
				if len(UpperCAmelCase__) != len(UpperCAmelCase__):
								for vertice in vertices:
												if vertice not in visited:
																lowerCamelCase					:			List[str]									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # return sort
				return sort


if __name__ == "__main__":
					A			      =      topological_sort('a', [], [])
					print(sort)
