

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
      from fairscale.optim import OSS


__a    :Tuple      =			logging.get_logger(__name__)

__a    :int      =			{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}

class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				str						,			UpperCAmelCase							:				str=None						,			UpperCAmelCase							:				Union[str, Any]=None						,			*UpperCAmelCase							:				int						,			**UpperCAmelCase							:				List[str]	):
            super().__init__(*UpperCAmelCase						,			**UpperCAmelCase	)

            if config is None:
                  assert isinstance(self.model						,			UpperCAmelCase	), (
                      "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                      f''' {self.model.__class__}'''
                  )
                  A_						  =				self.model.config
            else:
                  A_						  =				config

            A_						  =				data_args
            A_						  =				self.config.tgt_vocab_size if isinstance(self.config						,			UpperCAmelCase	) else self.config.vocab_size

            if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                  assert self.config.pad_token_id is not None, (
                      "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                      " calculation or doing label smoothing."
                  )

            if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                  logger.warning(
                      f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
                      " padding.."	)

            if self.args.label_smoothing == 0:
                  A_						  =				torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id	)
            else:
                  # dynamically import label_smoothed_nll_loss
                  from utils import label_smoothed_nll_loss

                  A_						  =				label_smoothed_nll_loss





      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				int	):
            if self.optimizer is None:
                  A_						  =				["bias", "LayerNorm.weight"]
                  A_						  =				[
                      {
                          "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay	)],
                          "weight_decay": self.args.weight_decay,
                      },
                      {
                          "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay	)],
                          "weight_decay": 0.0,
                      },
                  ]
                  A_						  =				Adafactor if self.args.adafactor else AdamW
                  if self.args.adafactor:
                        A_						  =				Adafactor
                        A_						  =				{"scale_parameter": False, "relative_step": False}
                  else:
                        A_						  =				AdamW
                        A_						  =				{
                            "betas": (self.args.adam_betaa, self.args.adam_betaa),
                            "eps": self.args.adam_epsilon,
                        }
                  A_						  =				self.args.learning_rate
                  if self.sharded_ddp:
                        A_						  =				OSS(
                            params=UpperCAmelCase						,			optim=UpperCAmelCase						,			**UpperCAmelCase						,			)
                  else:
                        A_						  =				optimizer_cls(UpperCAmelCase						,			**UpperCAmelCase	)

            if self.lr_scheduler is None:
                  A_						  =				self._get_lr_scheduler(UpperCAmelCase	)
            else:  # ignoring --lr_scheduler
                  logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."	)





      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				List[Any]	):
            A_						  =				arg_to_scheduler[self.args.lr_scheduler]
            if self.args.lr_scheduler == "constant":
                  A_						  =				schedule_func(self.optimizer	)
            elif self.args.lr_scheduler == "constant_w_warmup":
                  A_						  =				schedule_func(self.optimizer						,			num_warmup_steps=self.args.warmup_steps	)
            else:
                  A_						  =				schedule_func(
                      self.optimizer						,			num_warmup_steps=self.args.warmup_steps						,			num_training_steps=UpperCAmelCase	)
            return scheduler





      def 		__A	(   self							:				Optional[int]	):
            if isinstance(self.train_dataset						,			torch.utils.data.IterableDataset	):
                  return None
            elif is_torch_tpu_available():
                  return get_tpu_sampler(self.train_dataset	)
            else:
                  if self.args.sortish_sampler:
                        self.train_dataset.make_sortish_sampler(
                            self.args.per_device_train_batch_size						,			distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)						,			)

                  return (
                      RandomSampler(self.train_dataset	)
                      if self.args.local_rank == -1
                      else DistributedSampler(self.train_dataset	)
                  )





      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				str	):
            if self.args.label_smoothing == 0:
                  if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                        # force training to ignore pad token
                        A_						  =				model(**UpperCAmelCase						,			use_cache=UpperCAmelCase	)[0]
                        A_						  =				self.loss_fn(logits.view(-1						,			logits.shape[-1]	)						,			labels.view(-1	)	)
                  else:
                        # compute usual loss via models
                        A_  ,     A_						  =				model(**UpperCAmelCase						,			labels=UpperCAmelCase						,			use_cache=UpperCAmelCase	)[:2]
            else:
                  # compute label smoothed loss
                  A_						  =				model(**UpperCAmelCase						,			use_cache=UpperCAmelCase	)[0]
                  A_						  =				torch.nn.functional.log_softmax(UpperCAmelCase						,			dim=-1	)
                  A_  ,     A_						  =				self.loss_fn(UpperCAmelCase						,			UpperCAmelCase						,			self.args.label_smoothing						,			ignore_index=self.config.pad_token_id	)
            return loss, logits





      def 		__A	(   self							:				int						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				int	):
            A_						  =				inputs.pop("labels"	)
            A_  ,     A_						  =				self._compute_loss(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)
            return loss





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				nn.Module						,			UpperCAmelCase							:				Dict[str, Union[torch.Tensor, Any]]						,			UpperCAmelCase							:				bool						,			UpperCAmelCase							:				Optional[List[str]] = None						,			):
            A_						  =				self._prepare_inputs(UpperCAmelCase	)

            A_						  =				{
                "max_length": self.data_args.val_max_target_length
                if self.data_args is not None
                else self.config.max_length,
                "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
            }

            if self.args.predict_with_generate and not self.args.prediction_loss_only:
                  A_						  =				self.model.generate(
                      inputs["input_ids"]						,			attention_mask=inputs["attention_mask"]						,			**UpperCAmelCase						,			)
                  # in case the batch is shorter than max length, the output should be padded
                  if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                        A_						  =				self._pad_tensors_to_max_len(UpperCAmelCase						,			gen_kwargs["max_length"]	)

            A_						  =				inputs.pop("labels"	)
            with torch.no_grad():
                  # compute loss on predict data
                  A_  ,     A_						  =				self._compute_loss(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				loss.mean().detach()
            if self.args.prediction_loss_only:
                  return (loss, None, None)

            A_						  =				generated_tokens if self.args.predict_with_generate else logits

            if labels.shape[-1] < gen_kwargs["max_length"]:
                  A_						  =				self._pad_tensors_to_max_len(UpperCAmelCase						,			gen_kwargs["max_length"]	)

            return (loss, logits, labels)







      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Tuple	):
            # If PAD token is not defined at least EOS token has to be defined
            A_						  =				self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

            if pad_token_id is None:
                  raise ValueError(
                      "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
                      f''' padded to `max_length`={max_length}'''	)

            A_						  =				pad_token_id * torch.ones(
                (tensor.shape[0], max_length)						,			dtype=tensor.dtype						,			device=tensor.device	)
            A_						  =				tensor
            return padded_tensor




from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_		 =					TypeVar("""T""")

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (position - 1) // 2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 2


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					Optional[int]      )   ->	None:
    _A      =       []
    _A      =       {}
    _A      =       0

 def __len__(    self					:					str      )   ->	int:
    return self.elements

 def __repr__(    self					:					Optional[int]      )   ->	str:
    return str(self.heap      )

 def     snake_case_      (    self					:					str      )   ->	bool:
    # Check if the priority queue is empty
    return self.elements == 0

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an element with given priority to the queue
    self.heap.append((elem, weight)      )
    _A      =       self.elements
    self.elements += 1
    self._bubble_up(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	T:
    # Remove and return the element with lowest weight (highest priority)
    if self.elements > 1:
       self._swap_nodes(0			,						self.elements - 1      )
    _A  ,    _A      =       self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
       _A  ,    _A      =       self.heap[0]
       self._bubble_down(__lowerCAmelCase      )
    return elem

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Update the weight of the given key
    _A      =       self.position_map[elem]
    _A      =       (elem, weight)
    if position > 0:
       _A      =       get_parent_position(__lowerCAmelCase      )
       _A  ,    _A      =       self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(__lowerCAmelCase      )
       else:
          self._bubble_down(__lowerCAmelCase      )
    else:
       self._bubble_down(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (upward movement) [to be used internally
    # only]
    _A      =       self.position_map[elem]
    if curr_pos == 0:
       return None
    _A      =       get_parent_position(__lowerCAmelCase      )
    _A  ,    _A      =       self.heap[curr_pos]
    _A  ,    _A      =       self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
       return self._bubble_up(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (downward movement) [to be used
    # internally only]
    _A      =       self.position_map[elem]
    _A  ,    _A      =       self.heap[curr_pos]
    _A      =       get_child_left_position(__lowerCAmelCase      )
    _A      =       get_child_right_position(__lowerCAmelCase      )
    if child_left_position < self.elements and child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    if child_left_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       if child_left_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    else:
       return None
    if child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	None:
    # Swap the nodes at the given positions
    _A      =       self.heap[nodea_pos][0]
    _A      =       self.heap[nodea_pos][0]
    _A  ,    _A      =       (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    _A      =       nodea_pos
    _A      =       nodea_pos


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					str      )   ->	None:
    _A      =       {}
    _A      =       0

 def __repr__(    self					:					str      )   ->	str:
    return str(self.connections      )

 def __len__(    self					:					Dict      )   ->	int:
    return self.nodes

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					T      )   ->	None:
    # Add a node in the graph if it is not in the graph
    if node not in self.connections:
       _A      =       {}
       self.nodes += 1

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an edge between 2 nodes in the graph
    self.add_node(__lowerCAmelCase      )
    self.add_node(__lowerCAmelCase      )
    _A      =       weight
    _A      =       weight

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:GraphUndirectedWeighted[T]	,   )  ->     tuple[dict[T, int], dict[T, T | None]]:
   _A      =       {node: maxsize for node in graph.connections}
   _A      =       {node: None for node in graph.connections}

   _A      =       MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(_snake_case	,   _snake_case				)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   _A      =       priority_queue.extract_min()
   _A      =       0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         _A      =       dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(_snake_case	,   dist[neighbour]				)
         _A      =       node

    # running prim's algorithm
   while not priority_queue.is_empty():
      _A      =       priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            _A      =       dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(_snake_case	,   dist[neighbour]				)
            _A      =       node
   return dist, parent



