



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


__UpperCAmelCase          =       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 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			*SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			**SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          super().__init__(*SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	eval_examples
          UpperCamelCase    :  Optional[Any]							=	post_process_function
          UpperCamelCase    :  int							=	quant_trainer_args
          UpperCamelCase    :  str							=	128  # default number of calibration samples
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None       )     ->		List[Any]:
          if calib_dataset is None and self.calib_dataset is None:
             raise ValueError('Trainer: calibration requires an calib_dataset.'       )
          UpperCamelCase    :  Union[str, Any]							=	calib_dataset if calib_dataset is not None else self.calib_dataset

          UpperCamelCase    :  Optional[Any]							=	self._remove_unused_columns(SCREAMING_SNAKE_CASE_,			description='Calibration'       )

          return DataLoader(
              SCREAMING_SNAKE_CASE_,			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=SCREAMING_SNAKE_CASE_,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None       )     ->		Any:
          UpperCamelCase    :  Union[str, Any]							=	self.train_dataset if calib_dataset is None else calib_dataset
          UpperCamelCase    :  Dict							=	self.get_calib_dataloader(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	self.model
          quant_trainer.configure_model(SCREAMING_SNAKE_CASE_,			self.quant_trainer_args,			calib=SCREAMING_SNAKE_CASE_       )
          model.eval()
          quant_trainer.enable_calibration(SCREAMING_SNAKE_CASE_       )

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

          quant_trainer.finish_calibration(SCREAMING_SNAKE_CASE_,			self.quant_trainer_args       )
          UpperCamelCase    :  Optional[int]							=	model
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_ = "eval"       )     ->		List[Any]:
          UpperCamelCase    :  Optional[Any]							=	self.eval_dataset if eval_dataset is None else eval_dataset
          UpperCamelCase    :  List[Any]							=	self.get_eval_dataloader(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	self.eval_examples if eval_examples is None else eval_examples

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

          if self.post_process_function is not None and self.compute_metrics is not None:
             UpperCamelCase    :  Optional[Any]							=	self.post_process_function(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			output.predictions       )
             UpperCamelCase    :  Dict							=	self.compute_metrics(SCREAMING_SNAKE_CASE_       )

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

             self.log(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  Any							=	{}

          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()       )

          UpperCamelCase    :  Optional[int]							=	self.callback_handler.on_evaluate(self.args,			self.state,			self.control,			SCREAMING_SNAKE_CASE_       )
          return metrics
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_ = "test"       )     ->		Dict:
          UpperCamelCase    :  Dict							=	self.get_test_dataloader(SCREAMING_SNAKE_CASE_       )

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

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

          UpperCamelCase    :  Tuple							=	self.post_process_function(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			output.predictions,			'predict'       )
          UpperCamelCase    :  Dict							=	self.compute_metrics(SCREAMING_SNAKE_CASE_       )

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

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






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_="./"       )     ->		int:
          UpperCamelCase    :  Any							=	self.eval_dataset
          UpperCamelCase    :  Dict							=	self.get_eval_dataloader(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	next(iter(SCREAMING_SNAKE_CASE_       )       )

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

          # convert to tuple
          UpperCamelCase    :  List[str]							=	tuple(v.to(SCREAMING_SNAKE_CASE_       ) for k, v in batch.items()       )

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

          UpperCamelCase    :  Union[str, Any]							=	True

          UpperCamelCase    :  List[str]							=	self.model.to(SCREAMING_SNAKE_CASE_       )

          model.eval()
          model.float()

          UpperCamelCase    :  Tuple							=	model.module if hasattr(SCREAMING_SNAKE_CASE_,			'module'       ) else model
          quant_trainer.configure_model(SCREAMING_SNAKE_CASE_,			self.quant_trainer_args       )

          UpperCamelCase    :  Dict							=	os.path.join(SCREAMING_SNAKE_CASE_,			'model.onnx'       )
          logger.info(F"""exporting model to {output_model_file}"""       )

          UpperCamelCase    :  int							=	{0: 'batch_size', 1: 'seq_len'}

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








from __future__ import annotations

__UpperCAmelCase          =       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Union[str, Any]							=	graph
          # mapping node to its parent in resulting breadth first tree
          UpperCamelCase    :  dict[str, str | None]							=	{}
          UpperCamelCase    :  Union[str, Any]							=	source_vertex
       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  str							=	{self.source_vertex}
          UpperCamelCase    :  str							=	None
          UpperCamelCase    :  int							=	[self.source_vertex]  # first in first out queue

          while queue:
             UpperCamelCase    :  Dict							=	queue.pop(0       )
             for adjacent_vertex in self.graph[vertex]:
                if adjacent_vertex not in visited:
                   visited.add(SCREAMING_SNAKE_CASE_       )
                   UpperCamelCase    :  Tuple							=	vertex
                   queue.append(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          if target_vertex == self.source_vertex:
             return self.source_vertex

          UpperCamelCase    :  Optional[Any]							=	self.parent.get(SCREAMING_SNAKE_CASE_       )
          if target_vertex_parent is None:
             UpperCamelCase    :  Union[str, Any]							=	(
                 F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
             )
             raise ValueError(SCREAMING_SNAKE_CASE_       )

          return self.shortest_path(SCREAMING_SNAKE_CASE_       ) + F"""->{target_vertex}"""


if __name__ == "__main__":
    __UpperCAmelCase          =       Graph(graph, '''G''')
    g.breath_first_search()
    print(g.shortest_path('''D'''))
    print(g.shortest_path('''G'''))
    print(g.shortest_path('''Foo'''))



