
import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


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		(				lowerCAmelCase					):
    def __init__(  self		,*snake_case		,snake_case=None		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case				)
           lowercase							:					Optional[Any]	     =				eval_examples
           lowercase							:					Optional[Any]	     =				post_process_function
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case = None		,snake_case=None		,snake_case = None		,snake_case = "eval"		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				gen_kwargs.copy()
           lowercase							:					Union[str, Any]	     =				(
               gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""				) is not None else self.args.generation_max_length
           )
           lowercase							:					Optional[Any]	     =				(
               gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""				) is not None else self.args.generation_num_beams
           )
           lowercase							:					Dict	     =				gen_kwargs

           lowercase							:					Tuple	     =				self.eval_dataset if eval_dataset is None else eval_dataset
           lowercase							:					Union[str, Any]	     =				self.get_eval_dataloader(snake_case				)
           lowercase							:					Any	     =				self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           lowercase							:					List[Any]	     =				self.compute_metrics
           lowercase							:					Optional[int]	     =				None
           lowercase							:					Tuple	     =				time.time()
           lowercase							:					Any	     =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                  lowercase							:					Optional[int]	     =				eval_loop(
                      snake_case		,description="""Evaluation"""		,prediction_loss_only=True if compute_metrics is None else None		,ignore_keys=snake_case		,metric_key_prefix=snake_case		,)
           finally:
                  lowercase							:					Tuple	     =				compute_metrics
           lowercase							:					int	     =				self.args.eval_batch_size * self.args.world_size
           if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
                  start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
           output.metrics.update(
               speed_metrics(
                   snake_case		,snake_case		,num_samples=output.num_samples		,num_steps=math.ceil(output.num_samples / total_batch_size				)		,)				)

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                  # Only the main node write the results by default
                  lowercase							:					Dict	     =				self.post_process_function(snake_case		,snake_case		,snake_case				)
                  lowercase							:					Optional[Any]	     =				self.compute_metrics(snake_case				)

                  # Prefix all keys with metric_key_prefix + '_'
                  for key in list(metrics.keys()				):
                         if not key.startswith(f"{metric_key_prefix}_"				):
                                lowercase							:					int	     =				metrics.pop(snake_case				)

                  metrics.update(output.metrics				)
           else:
                  lowercase							:					str	     =				output.metrics

           if self.args.should_log:
                  # Only the main node log the results by default
                  self.log(snake_case				)

           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							:					Tuple	     =				self.callback_handler.on_evaluate(self.args		,self.state		,self.control		,snake_case				)
           return metrics
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case=None		,snake_case = "test"		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				gen_kwargs.copy()

           lowercase							:					Tuple	     =				self.get_test_dataloader(snake_case				)

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

           lowercase							:					List[str]	     =				self.args.eval_batch_size * self.args.world_size
           if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
                  start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
           output.metrics.update(
               speed_metrics(
                   snake_case		,snake_case		,num_samples=output.num_samples		,num_steps=math.ceil(output.num_samples / total_batch_size				)		,)				)
           if self.post_process_function is None or self.compute_metrics is None:
                  return output

           lowercase							:					Optional[int]	     =				self.post_process_function(snake_case		,snake_case		,snake_case		,"""predict"""				)
           lowercase							:					Optional[Any]	     =				self.compute_metrics(snake_case				)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()				):
                  if not key.startswith(f"{metric_key_prefix}_"				):
                         lowercase							:					Optional[Any]	     =				metrics.pop(snake_case				)
           metrics.update(output.metrics				)
           return PredictionOutput(predictions=predictions.predictions		,label_ids=predictions.label_ids		,metrics=snake_case				)


class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=None		,snake_case=None				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				data
           lowercase							:					List[Any]	     =				previous
           lowercase							:					List[str]	     =				next_node
    def __str__(  self				):



           '''simple docstring'''







           return f"{self.data}"
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.data
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.next
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.previous






class __snake_case		:
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				head
    def __iter__(  self				):



           '''simple docstring'''







           return self
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if not self.current:
                  raise StopIteration
           else:
                  lowercase							:					Union[str, Any]	     =				self.current.get_data()
                  lowercase							:					Optional[Any]	     =				self.current.get_next()
                  return value






class __snake_case		:
    def __init__(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				None  # First node in list
           lowercase							:					str	     =				None  # Last node in list
    def __str__(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.head
           lowercase							:					str	     =				[]
           while current is not None:
                  nodes.append(current.get_data()				)
                  lowercase							:					Dict	     =				current.get_next()
           return " ".join(str(snake_case				) for node in nodes				)
    def __contains__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.head
           while current:
                  if current.get_data() == value:
                         return True
                  lowercase							:					Any	     =				current.get_next()
           return False
    def __iter__(  self				):



           '''simple docstring'''







           return LinkedListIterator(self.head				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.head:
                  return self.head.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.tail:
                  return self.tail.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  lowercase							:					Any	     =				node
                  lowercase							:					Dict	     =				node
           else:
                  self.insert_before_node(self.head		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				Node(snake_case				)
           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.set_tail(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				node
           lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous() is None:
                  lowercase							:					Optional[int]	     =				node_to_insert
           else:
                  lowercase							:					Optional[int]	     =				node_to_insert

           lowercase							:					List[Any]	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				node
           lowercase							:					List[str]	     =				node.next

           if node.get_next() is None:
                  lowercase							:					Union[str, Any]	     =				node_to_insert
           else:
                  lowercase							:					List[str]	     =				node_to_insert

           lowercase							:					Dict	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				1
           lowercase							:					List[str]	     =				Node(snake_case				)
           lowercase							:					Any	     =				self.head
           while node:
                  if current_position == position:
                         self.insert_before_node(snake_case		,snake_case				)
                         return
                  current_position += 1
                  lowercase							:					List[Any]	     =				node.next
           self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.head
           while node:
                  if node.get_data() == item:
                         return node
                  lowercase							:					Any	     =				node.get_next()
           raise Exception("""Node not found"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if (node := self.get_node(snake_case				)) is not None:
                  if node == self.head:
                         lowercase							:					Optional[Any]	     =				self.head.get_next()

                  if node == self.tail:
                         lowercase							:					List[str]	     =				self.tail.get_previous()

                  self.remove_node_pointers(snake_case				)
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           if node.get_next():
                  lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous():
                  lowercase							:					Union[str, Any]	     =				node.next

           lowercase							:					Union[str, Any]	     =				None
           lowercase							:					Optional[Any]	     =				None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.head is None







def 						_snake_case(  )			->   None:
       pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
