



'''simple docstring'''
from __future__ import annotations

from random import random
from typing import Generic, TypeVar

snake_case_ :	Tuple         =  TypeVar('KT')
snake_case_ :	Union[str, Any]         =  TypeVar('VT')






class 							lowercase__	(							Generic[KT, VT]   ):





	def __init__( self					:				Tuple ,lowerCamelCase__					:				KT | str = "root" ,lowerCamelCase__					:				VT | None = None ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   key
							_UpperCamelCase					:		Optional[int] 				=   value
							_UpperCamelCase					:		list[Node[KT, VT]] 				=   []





	def __repr__( self					:				Any ):







							'''simple docstring'''
							return F'Node({self.key}: {self.value})'





	@property
	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							return len(self.forward )






class 							lowercase__	(							Generic[KT, VT]   ):





	def __init__( self					:				List[str] ,lowerCamelCase__					:				float = 0.5 ,lowerCamelCase__					:				int = 16 ):







							'''simple docstring'''
							_UpperCamelCase					:		Node[KT, VT] 				=   Node[KT, VT]()
							_UpperCamelCase					:		int 				=   0
							_UpperCamelCase					:		int 				=   p
							_UpperCamelCase					:		List[str] 				=   max_level





	def __str__( self					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   list(self )

							if len(lowerCamelCase__ ) == 0:
													return F'SkipList(level={self.level})'

							_UpperCamelCase					:		List[str] 				=   max((len(str(lowerCamelCase__ ) ) for item in items) ,default=4 )
							_UpperCamelCase					:		Union[str, Any] 				=   max(lowerCamelCase__ ,4 ) + 4

							_UpperCamelCase					:		str 				=   self.head
							_UpperCamelCase					:		int 				=   []

							_UpperCamelCase					:		Union[str, Any] 				=   node.forward.copy()
							lines.append(F'[{node.key}]'.ljust(lowerCamelCase__ ,'-' ) + '* ' * len(lowerCamelCase__ ) )
							lines.append(' ' * label_size + '| ' * len(lowerCamelCase__ ) )

							while len(node.forward ) != 0:
													_UpperCamelCase					:		Optional[int] 				=   node.forward[0]

													lines.append(
													    F'[{node.key}]'.ljust(lowerCamelCase__ ,'-' )
													    + ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
													lines.append(' ' * label_size + '| ' * len(lowerCamelCase__ ) )
													_UpperCamelCase					:		str 				=   node.forward

							lines.append('None'.ljust(lowerCamelCase__ ) + '* ' * len(lowerCamelCase__ ) )
							return F'SkipList(level={self.level})\n' + "\n".join(lowerCamelCase__ )





	def __iter__( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   self.head

							while len(node.forward ) != 0:
													yield node.forward[0].key
													_UpperCamelCase					:		List[str] 				=   node.forward[0]





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   1
							while random() < self.p and level < self.max_level:
													level += 1

							return level





	def 	UpperCamelCase_  ( self					:				Optional[int] ,lowerCamelCase__					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   []

							_UpperCamelCase					:		List[str] 				=   self.head

							for i in reversed(range(self.level ) ):
													# i < node.level - When node level is lesser than `i` decrement `i`.
													# node.forward[i].key < key - Jumping to node with key value higher
													#                             or equal to searched key would result
													#                             in skipping searched key.
													while i < node.level and node.forward[i].key < key:
																			_UpperCamelCase					:		Optional[int] 				=   node.forward[i]
													# Each leftmost node (relative to searched node) will potentially have to
													# be updated.
													update_vector.append(lowerCamelCase__ )

							update_vector.reverse()  # Note that we were inserting values in reverse order.

							# len(node.forward) != 0 - If current node doesn't contain any further
							#                          references then searched key is not present.
							# node.forward[0].key == key - Next node key should be equal to search key
							#                              if key is present.
							if len(node.forward ) != 0 and node.forward[0].key == key:
													return node.forward[0], update_vector
							else:
													return None, update_vector





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				KT ):







							'''simple docstring'''
							_UpperCamelCase				,			_UpperCamelCase					:		Any 				=   self._locate_node(lowerCamelCase__ )

							if node is not None:
													for i, update_node in enumerate(lowerCamelCase__ ):
																			# Remove or replace all references to removed node.
																			if update_node.level > i and update_node.forward[i].key == key:
																									if node.level > i:
																															_UpperCamelCase					:		Union[str, Any] 				=   node.forward[i]
																									else:
																															_UpperCamelCase					:		Union[str, Any] 				=   update_node.forward[:i]





	def 	UpperCamelCase_  ( self					:				int ,lowerCamelCase__					:				KT ,lowerCamelCase__					:				VT ):







							'''simple docstring'''
							_UpperCamelCase				,			_UpperCamelCase					:		List[Any] 				=   self._locate_node(lowerCamelCase__ )
							if node is not None:
													_UpperCamelCase					:		List[str] 				=   value
							else:
													_UpperCamelCase					:		Tuple 				=   self.random_level()

													if level > self.level:
																			# After level increase we have to add additional nodes to head.
																			for _ in range(self.level - 1 ,lowerCamelCase__ ):
																									update_vector.append(self.head )
																			_UpperCamelCase					:		Any 				=   level

													_UpperCamelCase					:		int 				=   Node(lowerCamelCase__ ,lowerCamelCase__ )

													for i, update_node in enumerate(update_vector[:level] ):
																			# Change references to pass through new node.
																			if update_node.level > i:
																									new_node.forward.append(update_node.forward[i] )

																			if update_node.level < i + 1:
																									update_node.forward.append(lowerCamelCase__ )
																			else:
																									_UpperCamelCase					:		str 				=   new_node





	def 	UpperCamelCase_  ( self					:				int ,lowerCamelCase__					:				VT ):







							'''simple docstring'''
							_UpperCamelCase				,			_UpperCamelCase					:		List[str] 				=   self._locate_node(lowerCamelCase__ )

							if node is not None:
													return node.value

							return None






def A__    (     ):
						_UpperCamelCase					:		Dict 				=   SkipList()
						skip_list.insert('Key1'  ,  3							)
						skip_list.insert('Key2'  ,  1_2							)
						skip_list.insert('Key3'  ,  4_1							)
						skip_list.insert('Key4'  ,  -1_9							)

						_UpperCamelCase					:		str 				=   skip_list.head
						_UpperCamelCase					:		Tuple 				=   {}
						while node.level != 0:
												_UpperCamelCase					:		Tuple 				=   node.forward[0]
												_UpperCamelCase					:		Optional[Any] 				=   node.value

						assert len(UpperCAmelCase_							) == 4
						assert all_values["Key1"] == 3
						assert all_values["Key2"] == 1_2
						assert all_values["Key3"] == 4_1
						assert all_values["Key4"] == -1_9






def A__    (     ):
						_UpperCamelCase					:		List[Any] 				=   SkipList()
						skip_list.insert('Key1'  ,  1_0							)
						skip_list.insert('Key1'  ,  1_2							)

						skip_list.insert('Key5'  ,  7							)
						skip_list.insert('Key7'  ,  1_0							)
						skip_list.insert('Key10'  ,  5							)

						skip_list.insert('Key7'  ,  7							)
						skip_list.insert('Key5'  ,  5							)
						skip_list.insert('Key10'  ,  1_0							)

						_UpperCamelCase					:		Any 				=   skip_list.head
						_UpperCamelCase					:		Dict 				=   {}
						while node.level != 0:
												_UpperCamelCase					:		Optional[Any] 				=   node.forward[0]
												_UpperCamelCase					:		Any 				=   node.value

						if len(UpperCAmelCase_							) != 4:
												print()
						assert len(UpperCAmelCase_							) == 4
						assert all_values["Key1"] == 1_2
						assert all_values["Key7"] == 7
						assert all_values["Key5"] == 5
						assert all_values["Key10"] == 1_0






def A__    (     ):
						_UpperCamelCase					:		Any 				=   SkipList()
						assert skip_list.find('Some key'							) is None






def A__    (     ):
						_UpperCamelCase					:		List[Any] 				=   SkipList()

						skip_list.insert('Key2'  ,  2_0							)
						assert skip_list.find('Key2'							) == 2_0

						skip_list.insert('Some Key'  ,  1_0							)
						skip_list.insert('Key2'  ,  8							)
						skip_list.insert('V'  ,  1_3							)

						assert skip_list.find('Y'							) is None
						assert skip_list.find('Key2'							) == 8
						assert skip_list.find('Some Key'							) == 1_0
						assert skip_list.find('V'							) == 1_3






def A__    (     ):
						_UpperCamelCase					:		Optional[int] 				=   SkipList()
						skip_list.delete('Some key'							)

						assert len(skip_list.head.forward							) == 0






def A__    (     ):
						_UpperCamelCase					:		Optional[int] 				=   SkipList()

						skip_list.insert('Key1'  ,  1_2							)
						skip_list.insert('V'  ,  1_3							)
						skip_list.insert('X'  ,  1_4							)
						skip_list.insert('Key2'  ,  1_5							)

						skip_list.delete('V'							)
						skip_list.delete('Key2'							)

						assert skip_list.find('V'							) is None
						assert skip_list.find('Key2'							) is None






def A__    (     ):
						_UpperCamelCase					:		Optional[Any] 				=   SkipList()

						skip_list.insert('Key1'  ,  1_2							)
						skip_list.insert('V'  ,  1_3							)
						skip_list.insert('X'  ,  1_4							)
						skip_list.insert('Key2'  ,  1_5							)

						skip_list.delete('V'							)
						assert skip_list.find('V'							) is None
						assert skip_list.find('X'							) == 1_4
						assert skip_list.find('Key1'							) == 1_2
						assert skip_list.find('Key2'							) == 1_5

						skip_list.delete('X'							)
						assert skip_list.find('V'							) is None
						assert skip_list.find('X'							) is None
						assert skip_list.find('Key1'							) == 1_2
						assert skip_list.find('Key2'							) == 1_5

						skip_list.delete('Key1'							)
						assert skip_list.find('V'							) is None
						assert skip_list.find('X'							) is None
						assert skip_list.find('Key1'							) is None
						assert skip_list.find('Key2'							) == 1_5

						skip_list.delete('Key2'							)
						assert skip_list.find('V'							) is None
						assert skip_list.find('X'							) is None
						assert skip_list.find('Key1'							) is None
						assert skip_list.find('Key2'							) is None






def A__    (     ):
						_UpperCamelCase					:		Tuple 				=   SkipList()

						skip_list.insert('Key1'  ,  1_2							)
						skip_list.insert('V'  ,  1_3							)
						skip_list.insert('X'  ,  1_4_2							)
						skip_list.insert('Key2'  ,  1_5							)

						skip_list.delete('X'							)

						def traverse_keys(UpperCAmelCase_							):
												yield node.key
												for forward_node in node.forward:
																		yield from traverse_keys(UpperCAmelCase_							)

						assert len(set(traverse_keys(skip_list.head							)							)							) == 4






def A__    (     ):
						def is_sorted(UpperCAmelCase_							):
												return all(next_item >= item for item, next_item in zip(UpperCAmelCase_  ,  lst[1:]							)							)

						_UpperCamelCase					:		Any 				=   SkipList()
						for i in range(1_0							):
												skip_list.insert(UpperCAmelCase_  ,  UpperCAmelCase_							)
						assert is_sorted(list(UpperCAmelCase_							)							)
						skip_list.delete(5							)
						skip_list.delete(8							)
						skip_list.delete(2							)
						assert is_sorted(list(UpperCAmelCase_							)							)
						skip_list.insert(-1_2  ,  -1_2							)
						skip_list.insert(7_7  ,  7_7							)
						assert is_sorted(list(UpperCAmelCase_							)							)






def A__    (     ):
						for _ in range(1_0_0							):
												# Repeat test 100 times due to the probabilistic nature of skip list
												# random values == random bugs
												test_insert()
												test_insert_overrides_existing_value()

												test_searching_empty_list_returns_none()
												test_search()

												test_deleting_item_from_empty_list_do_nothing()
												test_deleted_items_are_not_founded_by_find_method()
												test_delete_removes_only_given_key()
												test_delete_doesnt_leave_dead_nodes()

												test_iter_always_yields_sorted_values()






def A__    (     ):
						_UpperCamelCase					:		Union[str, Any] 				=   SkipList()
						skip_list.insert(2  ,  '2'							)
						skip_list.insert(4  ,  '4'							)
						skip_list.insert(6  ,  '4'							)
						skip_list.insert(4  ,  '5'							)
						skip_list.insert(8  ,  '4'							)
						skip_list.insert(9  ,  '4'							)

						skip_list.delete(4							)

						print(UpperCAmelCase_							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase_       		=  "true"
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=16      )							->      Optional[Any]:





    '''simple docstring'''






    set_seed(42      )
    A__          =   RegressionModel()
    A__          =   deepcopy(SCREAMING_SNAKE_CASE__      )
    A__          =   RegressionDataset(length=SCREAMING_SNAKE_CASE__      )
    A__          =   DataLoader(SCREAMING_SNAKE_CASE__    ,			batch_size=SCREAMING_SNAKE_CASE__      )
    model.to(accelerator.device      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return model, ddp_model, dataloader
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      int:





    '''simple docstring'''






    A__          =   AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
    A__          =   load_dataset('glue'    ,			'mrpc'    ,			split='validation'      )

    def tokenize_function(SCREAMING_SNAKE_CASE__			:	List[Any]      ):
        A__          =   tokenizer(examples['sentence1']    ,			examples['sentence2']    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__      )
        return outputs

    with accelerator.main_process_first():
        A__          =   dataset.map(
            SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

    A__          =   tokenized_datasets.rename_column('label'    ,			'labels'      )

    def collate_fn(SCREAMING_SNAKE_CASE__			:	Dict      ):
        if use_longest:
            return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='longest'    ,			return_tensors='pt'      )
        return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='max_length'    ,			max_length=128    ,			return_tensors='pt'      )

    return DataLoader(SCREAMING_SNAKE_CASE__    ,			shuffle=SCREAMING_SNAKE_CASE__    ,			collate_fn=SCREAMING_SNAKE_CASE__    ,			batch_size=16      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      str:





    '''simple docstring'''






    A__          =   Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__    ,			split_batches=SCREAMING_SNAKE_CASE__      )
    A__          =   get_dataloader(SCREAMING_SNAKE_CASE__    ,			not dispatch_batches      )
    A__          =   AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'    ,			return_dict=SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      List[str]:





    '''simple docstring'''






    A__          =   []
    for batch in dataloader:
        A__      ,						A__          =   batch.values()
        with torch.no_grad():
            A__          =   model(SCREAMING_SNAKE_CASE__      )
            A__      ,						A__          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    A__      ,						A__          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__      )
        targs.append(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   torch.cat(SCREAMING_SNAKE_CASE__      ), torch.cat(SCREAMING_SNAKE_CASE__      )
    return logits, targs
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	int=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False    ,			SCREAMING_SNAKE_CASE__			:	Any=False    ,			SCREAMING_SNAKE_CASE__			:	Tuple=16      )							->      List[Any]:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   get_basic_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   generate_predictions(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    assert (
        len(SCREAMING_SNAKE_CASE__      ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__      )}'
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	bool = False    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      str:





    '''simple docstring'''






    A__          =   evaluate.load('glue'    ,			'mrpc'      )
    A__      ,						A__          =   get_mrpc_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    # First do baseline
    A__      ,						A__      ,						A__          =   setup['no']
    model.to(SCREAMING_SNAKE_CASE__      )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE__      )
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=batch['labels']      )
    A__          =   metric.compute()

    # Then do distributed
    A__      ,						A__      ,						A__          =   setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        A__          =   batch['labels']
        A__      ,						A__          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=SCREAMING_SNAKE_CASE__      )
    A__          =   metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]    ,			distributed[key]      ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def 						_snake_case(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print('**Testing gather_for_metrics**'      )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'      )
                test_mrpc(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test torch metrics**'      )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'      )
            test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			99      )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test last batch is not dropped when perfectly divisible**'      )
    A__          =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			512      )
    accelerator.state._reset_state()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()

