

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


_UpperCAmelCase						:  Union[str, Any]									=			"""true"""





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__=82  ,				UpperCamelCase__=16 ):







							'''simple docstring'''






							set_seed(42 )
							snake_case_     							=       RegressionModel()
							snake_case_     							=       deepcopy(UpperCamelCase__ )
							snake_case_     							=       RegressionDataset(length=UpperCamelCase__ )
							snake_case_     							=       DataLoader(UpperCamelCase__  ,				batch_size=UpperCamelCase__ )
							model.to(accelerator.device )
							snake_case_							,       snake_case_     							=       accelerator.prepare(UpperCamelCase__  ,				UpperCamelCase__ )
							return model, ddp_model, dataloader





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__=False ):







							'''simple docstring'''






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

							def tokenize_function(UpperCamelCase__ ):
														snake_case_     							=       tokenizer(examples['sentence1']  ,				examples['sentence2']  ,				truncation=UpperCamelCase__  ,				max_length=UpperCamelCase__ )
														return outputs

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

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

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

							return DataLoader(UpperCamelCase__  ,				shuffle=UpperCamelCase__  ,				collate_fn=UpperCamelCase__  ,				batch_size=16 )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       Accelerator(dispatch_batches=UpperCamelCase__  ,				split_batches=UpperCamelCase__ )
							snake_case_     							=       get_dataloader(UpperCamelCase__  ,				not dispatch_batches )
							snake_case_     							=       AutoModelForSequenceClassification.from_pretrained(
							    'hf-internal-testing/mrpc-bert-base-cased'  ,				return_dict=UpperCamelCase__ )
							snake_case_							,       snake_case_     							=       accelerator.prepare(UpperCamelCase__  ,				UpperCamelCase__ )
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       []
							for batch in dataloader:
														snake_case_							,       snake_case_     							=       batch.values()
														with torch.no_grad():
																					snake_case_     							=       model(UpperCamelCase__ )
																					snake_case_							,       snake_case_     							=       accelerator.gather_for_metrics((logit, target) )
																					logits_and_targets.append((logit, target) )
							snake_case_							,       snake_case_     							=       [], []
							for logit, targ in logits_and_targets:
														logits.append(UpperCamelCase__ )
														targs.append(UpperCamelCase__ )
							snake_case_							,       snake_case_     							=       torch.cat(UpperCamelCase__ ), torch.cat(UpperCamelCase__ )
							return logits, targs





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__=82  ,				UpperCamelCase__=False  ,				UpperCamelCase__=False  ,				UpperCamelCase__=16 ):







							'''simple docstring'''






							snake_case_							,       snake_case_							,       snake_case_     							=       get_basic_setup(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
							snake_case_							,       snake_case_     							=       generate_predictions(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
							assert (
							    len(UpperCamelCase__ ) == num_samples
							), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__ )}'''





def    __lowerCamelCase      (					UpperCamelCase__ = False  ,				UpperCamelCase__ = False ):







							'''simple docstring'''






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

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







							'''simple docstring'''






							snake_case_     							=       Accelerator(split_batches=UpperCamelCase__  ,				dispatch_batches=UpperCamelCase__ )
							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(UpperCamelCase__  ,				UpperCamelCase__ )
																												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]:
																					snake_case_     							=       Accelerator(split_batches=UpperCamelCase__  ,				dispatch_batches=UpperCamelCase__ )
																					if accelerator.is_local_main_process:
																												print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
																					test_torch_metrics(UpperCamelCase__  ,				99 )
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('**Test last batch is not dropped when perfectly divisible**' )
							snake_case_     							=       Accelerator()
							test_torch_metrics(UpperCamelCase__  ,				512 )
							accelerator.state._reset_state()





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							main()


if __name__ == "__main__":
					main()







_UpperCAmelCase						:  int									=			range(2, 20 + 1)
_UpperCAmelCase						:  Dict									=			[10**k for k in range(ks[-1] + 1)]
_UpperCAmelCase						:  dict[int, dict[int, list[list[int]]]]									=			{}





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       sum(a_i[j] for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) )
							snake_case_     							=       sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase__ )  ,				UpperCamelCase__ ) ) )

							snake_case_							,       snake_case_     							=       0, 0
							snake_case_     							=       n - i

							snake_case_     							=       memo.get(UpperCamelCase__ )

							if sub_memo is not None:
														snake_case_     							=       sub_memo.get(UpperCamelCase__ )

														if jumps is not None and len(UpperCamelCase__ ) > 0:
																					# find and make the largest jump without going over
																					snake_case_     							=       -1
																					for _k in range(len(UpperCamelCase__ ) - 1  ,				-1  ,				-1 ):
																												if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																																			snake_case_     							=       _k
																																			break

																					if max_jump >= 0:
																												snake_case_							,       snake_case_							,       snake_case_     							=       jumps[max_jump]
																												# since the difference between jumps is cached, add c
																												snake_case_     							=       diff + c
																												for j in range(min(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) ):
																																			snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																												if new_c > 0:
																																			add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

														else:
																					snake_case_     							=       []
							else:
														snake_case_     							=       {c: []}
														snake_case_     							=       sub_memo

							if dn >= max_dn or c + diff >= base[k]:
														return diff, dn

							if k > ks[0]:
														while True:
																					# keep doing smaller jumps
																					snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				k - 1  ,				i + dn  ,				UpperCamelCase__ )
																					diff += _diff
																					dn += terms_jumped

																					if dn >= max_dn or c + diff >= base[k]:
																												break
							else:
														# would be too small a jump, just compute sequential terms instead
														snake_case_							,       snake_case_     							=       compute(UpperCamelCase__  ,				UpperCamelCase__  ,				i + dn  ,				UpperCamelCase__ )
														diff += _diff
														dn += terms_jumped

							snake_case_     							=       sub_memo[c]

							# keep jumps sorted by # of terms skipped
							snake_case_     							=       0
							while j < len(UpperCamelCase__ ):
														if jumps[j][1] > dn:
																					break
														j += 1

							# cache the jump for this value digitsum(b) and c
							sub_memo[c].insert(UpperCamelCase__  ,				(diff, dn, k) )
							return (diff, dn)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							if i >= n:
														return 0, i
							if k > len(UpperCamelCase__ ):
														a_i.extend([0 for _ in range(k - len(UpperCamelCase__ ) )] )

							# note: a_i -> b * 10^k + c
							# ds_b -> digitsum(b)
							# ds_c -> digitsum(c)
							snake_case_     							=       i
							snake_case_							,       snake_case_							,       snake_case_     							=       0, 0, 0
							for j in range(len(UpperCamelCase__ ) ):
														if j >= k:
																					ds_b += a_i[j]
														else:
																					ds_c += a_i[j]

							while i < n:
														i += 1
														snake_case_     							=       ds_c + ds_b
														diff += addend
														snake_case_     							=       0
														for j in range(UpperCamelCase__ ):
																					snake_case_     							=       a_i[j] + addend
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )

																					ds_c += a_i[j]

														if addend > 0:
																					break

							if addend > 0:
														add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
							return diff, i - start_i





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ):
														snake_case_     							=       digits[j] + addend
														if s >= 10:
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																					snake_case_     							=       addend // 10 + quotient
														else:
																					snake_case_     							=       s
																					snake_case_     							=       addend // 10

														if addend == 0:
																					break

							while addend > 0:
														snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
														digits.append(UpperCamelCase__ )





def    __lowerCamelCase      (					UpperCamelCase__ = 10**15 ):







							'''simple docstring'''






							snake_case_     							=       [1]
							snake_case_     							=       1
							snake_case_     							=       0
							while True:
														snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				20  ,				i + dn  ,				UpperCamelCase__ )
														dn += terms_jumped
														if dn == n - i:
																					break

							snake_case_     							=       0
							for j in range(len(UpperCamelCase__ ) ):
														a_n += digits[j] * 10**j
							return a_n


if __name__ == "__main__":
					print(F'''{solution() = }''')




