

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


_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      '''true'''
def     UpperCAmelCase_       (						_A				,		_A=82				,		_A=16    ):





     '''simple docstring'''




     set_seed(42    )
     SCREAMING_SNAKE_CASE__   =   RegressionModel()
     SCREAMING_SNAKE_CASE__   =   deepcopy(_A    )
     SCREAMING_SNAKE_CASE__   =   RegressionDataset(length=_A    )
     SCREAMING_SNAKE_CASE__   =   DataLoader(_A				,		batch_size=_A    )
     model.to(accelerator.device    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   accelerator.prepare(_A				,		_A    )
     return model, ddp_model, dataloader
def     UpperCAmelCase_       (						_A				,		_A=False    ):





     '''simple docstring'''




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

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

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

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

     def collate_fn(_A    ):
          if use_longest:
               return tokenizer.pad(_A				,		padding='''longest'''				,		return_tensors='''pt'''    )
          return tokenizer.pad(_A				,		padding='''max_length'''				,		max_length=1_28				,		return_tensors='''pt'''    )

     return DataLoader(_A				,		shuffle=_A				,		collate_fn=_A				,		batch_size=16    )
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []
     for batch in dataloader:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   batch.values()
          with torch.no_grad():
               SCREAMING_SNAKE_CASE__   =   model(_A    )
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   accelerator.gather_for_metrics((logit, target)    )
               logits_and_targets.append((logit, target)    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   [], []
     for logit, targ in logits_and_targets:
          logits.append(_A    )
          targs.append(_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   torch.cat(_A    ), torch.cat(_A    )
     return logits, targs
def     UpperCAmelCase_       (						_A				,		_A=82				,		_A=False				,		_A=False				,		_A=16    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   get_basic_setup(_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   generate_predictions(_A				,		_A				,		_A    )
     assert (
         len(_A    ) == num_samples
     ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_A    )}'''
def     UpperCAmelCase_       (						_A = False				,		_A = False    ):





     '''simple docstring'''




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

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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   Accelerator(split_batches=_A				,		dispatch_batches=_A    )
     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(_A				,		_A    )
                    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]:
               SCREAMING_SNAKE_CASE__   =   Accelerator(split_batches=_A				,		dispatch_batches=_A    )
               if accelerator.is_local_main_process:
                    print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''    )
               test_torch_metrics(_A				,		99    )
               accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print('''**Test last batch is not dropped when perfectly divisible**'''    )
     SCREAMING_SNAKE_CASE__   =   Accelerator()
     test_torch_metrics(_A				,		5_12    )
     accelerator.state._reset_state()
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     main()


if __name__ == "__main__":
       main()





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


