


'''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


_UpperCAmelCase				: Optional[int]							    =				"""true"""

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=8_2,				lowerCamelCase=1_6):
						set_seed(4_2)
						__lowerCAmelCase							       =				RegressionModel()
						__lowerCAmelCase							       =				deepcopy(lowerCamelCase)
						__lowerCAmelCase							       =				RegressionDataset(length=lowerCamelCase)
						__lowerCAmelCase							       =				DataLoader(lowerCamelCase,				batch_size=lowerCamelCase)
						model.to(accelerator.device)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase,				lowerCamelCase)
						return model, ddp_model, dataloader

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''')
						__lowerCAmelCase							       =				load_dataset('''glue''',				'''mrpc''',				split='''validation''')

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

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

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

						def collate_fn(lowerCamelCase):
												if use_longest:
																		return tokenizer.pad(lowerCamelCase,				padding='''longest''',				return_tensors='''pt''')
												return tokenizer.pad(lowerCamelCase,				padding='''max_length''',				max_length=1_2_8,				return_tensors='''pt''')

						return DataLoader(lowerCamelCase,				shuffle=lowerCamelCase,				collate_fn=lowerCamelCase,				batch_size=1_6)

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

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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=8_2,				lowerCamelCase=False,				lowerCamelCase=False,				lowerCamelCase=1_6):
						__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				get_basic_setup(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				generate_predictions(lowerCamelCase,				lowerCamelCase,				lowerCamelCase)
						assert (
						    len(lowerCamelCase) == num_samples
						), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase)}"""

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

						# Then do distributed
						__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__lowerCAmelCase							       =				model(**lowerCamelCase)
												__lowerCAmelCase							       =				outputs.logits.argmax(dim=-1)
												__lowerCAmelCase							       =				batch['''labels''']
												__lowerCAmelCase       ,					__lowerCAmelCase							       =				accelerator.gather_for_metrics((preds, references))
												metric.add_batch(predictions=lowerCamelCase,				references=lowerCamelCase)
						__lowerCAmelCase							       =				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 							__magic_name__(						):
						__lowerCAmelCase							       =				Accelerator(split_batches=lowerCamelCase,				dispatch_batches=lowerCamelCase)
						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(lowerCamelCase,				lowerCamelCase)
																								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							       =				Accelerator(split_batches=lowerCamelCase,				dispatch_batches=lowerCamelCase)
																		if accelerator.is_local_main_process:
																								print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""")
																		test_torch_metrics(lowerCamelCase,				9_9)
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**''')
						__lowerCAmelCase							       =				Accelerator()
						test_torch_metrics(lowerCamelCase,				5_1_2)
						accelerator.state._reset_state()

def 							__magic_name__(						lowerCamelCase):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
	main()







'''simple docstring'''

import numpy as np

from transformers import Pipeline

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				np.max(lowerCamelCase,				axis=-1,				keepdims=lowerCamelCase)
						__lowerCAmelCase							       =				np.exp(outputs - maxes)
						return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=lowerCamelCase)




class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{}
										if "second_text" in kwargs:
																__lowerCAmelCase							       =				kwargs['''second_text''']
										return preprocess_kwargs, {}, {}




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										return self.tokenizer(__lowercase       ,			text_pair=__lowercase       ,			return_tensors=self.framework		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.model(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				model_outputs.logits[0].numpy()
										__lowerCAmelCase							       =				softmax(__lowercase		)

										__lowerCAmelCase							       =				np.argmax(__lowercase		)
										__lowerCAmelCase							       =				self.model.config.idalabel[best_class]
										__lowerCAmelCase							       =				probabilities[best_class].item()
										__lowerCAmelCase							       =				logits.tolist()
										return {"label": label, "score": score, "logits": logits}

