
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


lowerCamelCase      ="true"


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=8_2 ,		UpperCamelCase__=1_6		):
      set_seed(4_2		)
      UpperCamelCase__    :   Optional[Any]					=				RegressionModel()
      UpperCamelCase__    :   Optional[Any]					=				deepcopy(UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				RegressionDataset(length=UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				DataLoader(UpperCamelCase__ ,		batch_size=UpperCamelCase__		)
      model.to(accelerator.device		)
      UpperCamelCase__    :   str					=				accelerator.prepare(UpperCamelCase__ ,		UpperCamelCase__		)
      return model, ddp_model, dataloader


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=False		):
      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(UpperCamelCase__		):
            UpperCamelCase__    :   int					=				tokenizer(examples['''sentence1'''] ,		examples['''sentence2'''] ,		truncation=UpperCamelCase__ ,		max_length=UpperCamelCase__		)
            return outputs

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

      UpperCamelCase__    :   Optional[int]					=				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=1_2_8 ,		return_tensors='''pt'''		)

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


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


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=8_2 ,		UpperCamelCase__=False ,		UpperCamelCase__=False ,		UpperCamelCase__=1_6		):
      UpperCamelCase__    :   str					=				get_basic_setup(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
      UpperCamelCase__    :   Tuple					=				generate_predictions(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
      assert (
          len(UpperCamelCase__		) == num_samples
      ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__		)}'''


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

      # Then do distributed
      UpperCamelCase__    :   Any					=				setup['''ddp''']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  UpperCamelCase__    :   Tuple					=				model(**UpperCamelCase__		)
            UpperCamelCase__    :   Optional[int]					=				outputs.logits.argmax(dim=-1		)
            UpperCamelCase__    :   int					=				batch['''labels''']
            UpperCamelCase__    :   str					=				accelerator.gather_for_metrics((preds, references)		)
            metric.add_batch(predictions=UpperCamelCase__ ,		references=UpperCamelCase__		)
      UpperCamelCase__    :   int					=				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 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Dict					=				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]:
                  UpperCamelCase__    :   Optional[Any]					=				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__ ,		9_9		)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('''**Test last batch is not dropped when perfectly divisible**'''		)
      UpperCamelCase__    :   Tuple					=				Accelerator()
      test_torch_metrics(UpperCamelCase__ ,		5_1_2		)
      accelerator.state._reset_state()


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

           UpperCamelCase__    :   Tuple					=				AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,						return_dict=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.CosineSimilarity(3						,						1e-08  )
           UpperCamelCase__    :   List[Any]					=				torch.nn.Softmax(dim=1  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







           return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

           for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
                 if i == 0:
                       UpperCamelCase__    :   int					=				0
                 else:
                       UpperCamelCase__    :   Optional[int]					=				support_sizes[i - 1]

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

                 UpperCamelCase__    :   Tuple					=				torch.matmul(q[i]						,						s_start.T  ).sum(1  ).softmax(0  )
                 UpperCamelCase__    :   Optional[Any]					=				torch.matmul(q[i]						,						s_end.T  ).sum(1  ).softmax(0  )

                 if p_starts is not None:
                       UpperCamelCase__    :   Union[str, Any]					=				torch.vstack((p_starts, p_start)  )
                       UpperCamelCase__    :   str					=				torch.vstack((p_ends, p_end)  )
                 else:
                       UpperCamelCase__    :   List[Any]					=				p_start
                       UpperCamelCase__    :   List[str]					=				p_end

           return p_starts, p_ends


