





'''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_		:  Dict		 =						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  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   def __init__(							self	,		*__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		**__SCREAMING_SNAKE_CASE       ):
    super().__init__(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[str]    =  eval_examples
    snake_case__     :					List[str]    =  post_process_function
    snake_case__     :					Optional[Any]    =  quant_trainer_args
    snake_case__     :					List[str]    =  1_2_8  # default number of calibration samples

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=None       ):
    if calib_dataset is None and self.calib_dataset is None:
     raise ValueError("""Trainer: calibration requires an calib_dataset."""       )
    snake_case__     :					List[str]    =  calib_dataset if calib_dataset is not None else self.calib_dataset

    snake_case__     :					Dict    =  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 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=None       ):
    snake_case__     :					List[Any]    =  self.train_dataset if calib_dataset is None else calib_dataset
    snake_case__     :					Dict    =  self.get_calib_dataloader(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  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
     snake_case__  ,							snake_case__  ,							snake_case__     :					Any    =  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       )
    snake_case__     :					Optional[Any]    =  model

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE = "eval"       ):
    snake_case__     :					Dict    =  self.eval_dataset if eval_dataset is None else eval_dataset
    snake_case__     :					Dict    =  self.get_eval_dataloader(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  self.eval_examples if eval_examples is None else eval_examples

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

    if self.post_process_function is not None and self.compute_metrics is not None:
     snake_case__     :					Any    =  self.post_process_function(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		output.predictions       )
     snake_case__     :					str    =  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}_"       ):
       snake_case__     :					str    =  metrics.pop(__SCREAMING_SNAKE_CASE       )

     self.log(__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					Union[str, 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()       )

    snake_case__     :					int    =  self.callback_handler.on_evaluate(self.args	,		self.state	,		self.control	,		__SCREAMING_SNAKE_CASE       )
    return metrics

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE = "test"       ):
    snake_case__     :					int    =  self.get_test_dataloader(__SCREAMING_SNAKE_CASE       )

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

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

    snake_case__     :					List[str]    =  self.post_process_function(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		output.predictions	,		"""predict"""       )
    snake_case__     :					Any    =  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}_"       ):
      snake_case__     :					Optional[int]    =  metrics.pop(__SCREAMING_SNAKE_CASE       )

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


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE="./"       ):
    snake_case__     :					List[Any]    =  self.eval_dataset
    snake_case__     :					Union[str, Any]    =  self.get_eval_dataloader(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  next(iter(__SCREAMING_SNAKE_CASE       )       )

    # saving device - to make it consistent
    snake_case__     :					Union[str, Any]    =  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""       )

    # convert to tuple
    snake_case__     :					Tuple    =  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

    snake_case__     :					Optional[Any]    =  True

    snake_case__     :					Optional[Any]    =  self.model.to(__SCREAMING_SNAKE_CASE       )

    model.eval()
    model.float()

    snake_case__     :					List[Any]    =  model.module if hasattr(__SCREAMING_SNAKE_CASE	,		"""module"""       ) else model
    quant_trainer.configure_model(__SCREAMING_SNAKE_CASE	,		self.quant_trainer_args       )

    snake_case__     :					Tuple    =  os.path.join(__SCREAMING_SNAKE_CASE	,		"""model.onnx"""       )
    logger.info(f"exporting model to {output_model_file}"       )

    snake_case__     :					List[str]    =  {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=1_3	,		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"""       )






"""simple docstring"""




from __future__ import annotations

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







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			graph
         # mapping node to its parent in resulting breadth first tree
         a__		=			{}
         a__		=			source_vertex



     def 			lowercase__  (							self   )		->     None:







         """simple docstring"""



         a__		=			{self.source_vertex}
         a__		=			None
         a__		=			[self.source_vertex]  # first in first out queue

         while queue:
             a__		=			queue.pop(0   )
             for adjacent_vertex in self.graph[vertex]:
                 if adjacent_vertex not in visited:
                     visited.add(__SCREAMING_SNAKE_CASE   )
                     a__		=			vertex
                     queue.append(__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         if target_vertex == self.source_vertex:
             return self.source_vertex

         a__		=			self.parent.get(__SCREAMING_SNAKE_CASE   )
         if target_vertex_parent is None:
             a__		=			(
                 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__":
    a		:       Optional[Any]     						=       Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))


