
'''simple docstring'''


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							:   Dict					=     '''true'''




def lowercase_	(   _lowercase			,       _lowercase=82			,       _lowercase=16				)     ->							List[str]:
					'''simple docstring'''

					set_seed(42				)
					lowerCamelCase_					:      List[str]     =		RegressionModel()
					lowerCamelCase_					:      int     =		deepcopy(_lowercase				)
					lowerCamelCase_					:      Any     =		RegressionDataset(length=_lowercase				)
					lowerCamelCase_					:      Any     =		DataLoader(_lowercase			,       batch_size=_lowercase				)
					model.to(accelerator.device				)
					lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		accelerator.prepare(_lowercase			,       _lowercase				)
					return model, ddp_model, dataloader




def lowercase_	(   _lowercase			,       _lowercase=False				)     ->							Optional[int]:
					'''simple docstring'''

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

					def tokenize_function(_lowercase				):
										lowerCamelCase_					:      Union[str, Any]     =		tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=_lowercase			,       max_length=_lowercase				)
										return outputs

					with accelerator.main_process_first():
										lowerCamelCase_					:      Union[str, Any]     =		dataset.map(
										    _lowercase			,       batched=_lowercase			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       )

					lowerCamelCase_					:      List[Any]     =		tokenized_datasets.rename_column('''label'''			,       '''labels'''				)

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

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




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

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




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		[]
					for batch in dataloader:
										lowerCamelCase_,      lowerCamelCase_					:      Any     =		batch.values()
										with torch.no_grad():
															lowerCamelCase_					:      Optional[Any]     =		model(_lowercase				)
															lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		accelerator.gather_for_metrics((logit, target)				)
															logits_and_targets.append((logit, target)				)
					lowerCamelCase_,      lowerCamelCase_					:      str     =		[], []
					for logit, targ in logits_and_targets:
										logits.append(_lowercase				)
										targs.append(_lowercase				)
					lowerCamelCase_,      lowerCamelCase_					:      Dict     =		torch.cat(_lowercase				), torch.cat(_lowercase				)
					return logits, targs




def lowercase_	(   _lowercase			,       _lowercase=82			,       _lowercase=False			,       _lowercase=False			,       _lowercase=16				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		get_basic_setup(_lowercase			,       _lowercase			,       _lowercase				)
					lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		generate_predictions(_lowercase			,       _lowercase			,       _lowercase				)
					assert (
					    len(_lowercase				) == num_samples
					), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowercase				)}"""




def lowercase_	(   _lowercase = False			,       _lowercase = False				)     ->							Tuple:
					'''simple docstring'''

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

					# Then do distributed
					lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		setup['''ddp''']
					model.eval()
					for batch in dataloader:
										with torch.inference_mode():
															lowerCamelCase_					:      int     =		model(**_lowercase				)
										lowerCamelCase_					:      Optional[Any]     =		outputs.logits.argmax(dim=-1				)
										lowerCamelCase_					:      Tuple     =		batch['''labels''']
										lowerCamelCase_,      lowerCamelCase_					:      str     =		accelerator.gather_for_metrics((preds, references)				)
										metric.add_batch(predictions=_lowercase			,       references=_lowercase				)
					lowerCamelCase_					:      List[str]     =		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 lowercase_	(   )     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		Accelerator(split_batches=_lowercase			,       dispatch_batches=_lowercase				)
					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(_lowercase			,       _lowercase				)
																				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]:
															lowerCamelCase_					:      Tuple     =		Accelerator(split_batches=_lowercase			,       dispatch_batches=_lowercase				)
															if accelerator.is_local_main_process:
																				print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""				)
															test_torch_metrics(_lowercase			,       99				)
															accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print('''**Test last batch is not dropped when perfectly divisible**'''				)
					lowerCamelCase_					:      int     =		Accelerator()
					test_torch_metrics(_lowercase			,       512				)
					accelerator.state._reset_state()




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					main()


if __name__ == "__main__":
					main()




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


