


'''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


__snake_case											=					'''true'''


def       a				(      __a			, __a=82			, __a=16       )  ->     Union[str, Any]:



     '''simple docstring'''






     set_seed(42       )
     UpperCamelCase__       :Dict							 =			RegressionModel()
     UpperCamelCase__       :Dict							 =			deepcopy(lowercase__       )
     UpperCamelCase__       :Tuple							 =			RegressionDataset(length=lowercase__       )
     UpperCamelCase__       :Optional[Any]							 =			DataLoader(lowercase__			, batch_size=lowercase__       )
     model.to(accelerator.device       )
     UpperCamelCase__       :Union[str, Any]							 =			accelerator.prepare(lowercase__			, lowercase__       )
     return model, ddp_model, dataloader


def       a				(      __a			, __a=False       )  ->     Optional[Any]:



     '''simple docstring'''






     UpperCamelCase__       :List[str]							 =			AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''       )
     UpperCamelCase__       :List[Any]							 =			load_dataset('''glue'''			, '''mrpc'''			, split='''validation'''       )

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

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

     UpperCamelCase__       :Any							 =			tokenized_datasets.rename_column('''label'''			, '''labels'''       )

     def collate_fn(__a       ):
          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       a				(      __a			, __a       )  ->     Union[str, Any]:



     '''simple docstring'''






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


def       a				(      __a			, __a			, __a       )  ->     Dict:



     '''simple docstring'''






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


def       a				(      __a			, __a=82			, __a=False			, __a=False			, __a=16       )  ->     Union[str, Any]:



     '''simple docstring'''






     UpperCamelCase__       :List[str]							 =			get_basic_setup(lowercase__			, lowercase__			, lowercase__       )
     UpperCamelCase__       :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       a				(      __a = False			, __a = False       )  ->     Tuple:



     '''simple docstring'''






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

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



     '''simple docstring'''






     UpperCamelCase__       :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]:
               UpperCamelCase__       :List[str]							 =			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**'''       )
     UpperCamelCase__       :Optional[int]							 =			Accelerator()
     test_torch_metrics(lowercase__			, 512       )
     accelerator.state._reset_state()


def       a				(      __a       )  ->     str:



     '''simple docstring'''






     main()


if __name__ == "__main__":
  main()



'''simple docstring'''
import socket


def       a				(      )  ->     Dict:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			socket.socket(socket.AF_INET			, socket.SOCK_STREAM       )
					UpperCamelCase__       :List[Any]							 =			socket.gethostname()
					UpperCamelCase__       :List[str]							 =			12312

					sock.connect((host, port)       )
					sock.send(B'''Hello server!'''       )

					with open('''Received_file'''			, '''wb'''       ) as out_file:
										print('''File opened'''       )
										print('''Receiving data...'''       )
										while True:
															UpperCamelCase__       :str							 =			sock.recv(1024       )
															if not data:
																				break
															out_file.write(__a       )

					print('''Successfully received the file'''       )
					sock.close()
					print('''Connection closed'''       )


if __name__ == "__main__":
		main()