
class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: Optional[int] = ""							,A					: str = False ):
					__A   			= {}

					# A node will be a leaf if the tree contains its word
					__A   			= is_leaf

					__A   			= prefix






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: int ):
					__A   			= 0
					for q, w in zip(self.prefix							,snake_case__ ):
									if q != w:
													break

									x += 1

					return self.prefix[:x], self.prefix[x:], word[x:]






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Tuple ):





					for word in words:
									self.insert(snake_case__ )






	def 				UpperCamelCase_  (       self					: int							,A					: Optional[Any] ):





					if self.prefix == word:
									__A   			= True

					# Case 2: The node has no edges that have a prefix to the word
					# Solution: We create an edge from the current node to a new one
					# containing the word
					elif word[0] not in self.nodes:
									__A   			= RadixNode(prefix=snake_case__							,is_leaf=snake_case__ )

					else:
									__A   			= self.nodes[word[0]]
									__A   			= incoming_node.match(
									    snake_case__ )

									# Case 3: The node prefix is equal to the matching
									# Solution: We insert remaining word on the next node
									if remaining_prefix == "":
													self.nodes[matching_string[0]].insert(snake_case__ )

									# Case 4: The word is greater equal to the matching
									# Solution: Create a node in between both nodes, change
									# prefixes and add the new node for the remaining word
									else:
													__A   			= remaining_prefix

													__A   			= self.nodes[matching_string[0]]
													__A   			= RadixNode(snake_case__							,snake_case__ )
													__A   			= aux_node

													if remaining_word == "":
																	__A   			= True
													else:
																	self.nodes[matching_string[0]].insert(snake_case__ )






	def 				UpperCamelCase_  (       self					: str							,A					: Optional[Any] ):
					__A   			= self.nodes.get(word[0]							,snake_case__ )
					if not incoming_node:
									return False
					else:
									__A   			= incoming_node.match(
									    snake_case__ )
									# If there is remaining prefix, the word can't be on the tree
									if remaining_prefix != "":
													return False
									# This applies when the word and the prefix are equal
									elif remaining_word == "":
													return incoming_node.is_leaf
									# We have word remaining so we check the next node
									else:
													return incoming_node.find(snake_case__ )






	def 				UpperCamelCase_  (       self					: int							,A					: int ):
					__A   			= self.nodes.get(word[0]							,snake_case__ )
					if not incoming_node:
									return False
					else:
									__A   			= incoming_node.match(
									    snake_case__ )
									# If there is remaining prefix, the word can't be on the tree
									if remaining_prefix != "":
													return False
									# We have word remaining so we check the next node
									elif remaining_word != "":
													return incoming_node.delete(snake_case__ )
									else:
													# If it is not a leaf, we don't have to delete
													if not incoming_node.is_leaf:
																	return False
													else:
																	# We delete the nodes if no edges go from it
																	if len(incoming_node.nodes ) == 0:
																					del self.nodes[word[0]]
																					# We merge the current node with its only child
																					if len(self.nodes ) == 1 and not self.is_leaf:
																									__A   			= list(self.nodes.values() )[0]
																									__A   			= merging_node.is_leaf
																									self.prefix += merging_node.prefix
																									__A   			= merging_node.nodes
																									 # If there is more than 1 edge, we just mark it as non-leaf
																	elif len(incoming_node.nodes ) > 1:
																					__A   			= False
																	# If there is 1 edge, we merge it with its child
																	else:
																					__A   			= list(incoming_node.nodes.values() )[0]
																					__A   			= merging_node.is_leaf
																					incoming_node.prefix += merging_node.prefix
																					__A   			= merging_node.nodes

																	return True







	def 				UpperCamelCase_  (       self					: Dict							,A					: Optional[Any] = 0 ):





					if self.prefix != "":
									print("-" * height							,self.prefix							,"  (leaf)" if self.is_leaf else "" )

					for value in self.nodes.values():
									value.print_tree(height + 1 )





def 						UpperCAmelCase			(	)		->	List[str]:







				"""simple docstring"""



				__A   			= "banana bananas bandana band apple all beast".split()
				__A   			= RadixNode()
				root.insert_many(a_						)

				assert all(root.find(a_						) for word in words						)
				assert not root.find("bandanas"						)
				assert not root.find("apps"						)
				root.delete("all"						)
				assert not root.find("all"						)
				root.delete("banana"						)
				assert not root.find("banana"						)
				assert root.find("bananas"						)

				return True


def 						UpperCAmelCase			(	)		->	Union[str, Any]:







				"""simple docstring"""



				assert test_trie()


def 						UpperCAmelCase			(	)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= RadixNode()
				__A   			= "banana bananas bandanas bandana band apple all beast".split()
				root.insert_many(a_						)

				print("Words:"		,   a_						)
				print("Tree:"						)
				root.print_tree()


if __name__ == "__main__":
					main()




"""simple docstring"""







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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__=None		,     snake_case__=None		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         super().__init__(*snake_case__		,     **snake_case__						)

         if config is None:
           assert isinstance(self.model		,     snake_case__						), (
               "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
               f""" {self.model.__class__}"""
           )
           lowerCAmelCase     :     Optional[int]								=							self.model.config
         else:
           lowerCAmelCase     :     List[str]								=							config

         lowerCAmelCase     :     Any								=							data_args
         lowerCAmelCase     :     Tuple								=							self.config.tgt_vocab_size if isinstance(self.config		,     snake_case__						) 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:
           lowerCAmelCase     :     int								=							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
         else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           lowerCAmelCase     :     Tuple								=							label_smoothed_nll_loss

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.optimizer is None:
           lowerCAmelCase     :     Optional[int]								=							["bias", "LayerNorm.weight"]
           lowerCAmelCase     :     str								=							[
               {
                   "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,
               },
           ]
           lowerCAmelCase     :     Union[str, Any]								=							Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
             lowerCAmelCase     :     Dict								=							Adafactor
             lowerCAmelCase     :     Optional[int]								=							{"scale_parameter": False, "relative_step": False}
           else:
             lowerCAmelCase     :     int								=							AdamW
             lowerCAmelCase     :     int								=							{
                 "betas": (self.args.adam_betaa, self.args.adam_betaa),
                 "eps": self.args.adam_epsilon,
             }
           lowerCAmelCase     :     Any								=							self.args.learning_rate
           if self.sharded_ddp:
             lowerCAmelCase     :     int								=							OSS(
                 params=snake_case__		,     optim=snake_case__		,     **snake_case__		,     )
           else:
             lowerCAmelCase     :     Any								=							optimizer_cls(snake_case__		,     **snake_case__						)

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

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
           lowerCAmelCase     :     Tuple								=							schedule_func(self.optimizer						)
         elif self.args.lr_scheduler == "constant_w_warmup":
           lowerCAmelCase     :     Any								=							schedule_func(self.optimizer		,     num_warmup_steps=self.args.warmup_steps						)
         else:
           lowerCAmelCase     :     str								=							schedule_func(
               self.optimizer		,     num_warmup_steps=self.args.warmup_steps		,     num_training_steps=snake_case__						)
         return scheduler

       def        lowercase__							(						self						):






         """simple docstring"""





         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        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         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
             lowerCAmelCase     :     Dict								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
             lowerCAmelCase     :     List[Any]								=							self.loss_fn(logits.view(-1		,     logits.shape[-1]						)		,     labels.view(-1						)						)
           else:
             # compute usual loss via models
             lowerCAmelCase						,  lowerCAmelCase     :     str								=							model(**snake_case__		,     labels=snake_case__		,     use_cache=snake_case__						)[:2]
         else:
           # compute label smoothed loss
           lowerCAmelCase     :     int								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
           lowerCAmelCase     :     List[Any]								=							torch.nn.functional.log_softmax(snake_case__		,     dim=-1						)
           lowerCAmelCase						,  lowerCAmelCase     :     str								=							self.loss_fn(snake_case__		,     snake_case__		,     self.args.label_smoothing		,     ignore_index=self.config.pad_token_id						)
         return loss, logits

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							inputs.pop("labels"						)
         lowerCAmelCase						,  lowerCAmelCase     :     str								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)
         return loss

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self._prepare_inputs(snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							{
             "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:
           lowerCAmelCase     :     Dict								=							self.model.generate(
               inputs["input_ids"]		,     attention_mask=inputs["attention_mask"]		,     **snake_case__		,     )
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
             lowerCAmelCase     :     Dict								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         lowerCAmelCase     :     Optional[Any]								=							inputs.pop("labels"						)
         with torch.no_grad():
           # compute loss on predict data
           lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[str]								=							loss.mean().detach()
         if self.args.prediction_loss_only:
           return (loss, None, None)

         lowerCAmelCase     :     int								=							generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
           lowerCAmelCase     :     Optional[int]								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         return (loss, logits, labels)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							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}"""						)

         lowerCAmelCase     :     Optional[Any]								=							pad_token_id * torch.ones(
             (tensor.shape[0], max_length)		,     dtype=tensor.dtype		,     device=tensor.device						)
         lowerCAmelCase     :     int								=							tensor
         return padded_tensor





