




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_									=	'true'




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					List[str]=82  ,			A__       :					int=16	):





       '''simple docstring'''



       set_seed(42	)
       __lowerCamelCase   =						RegressionModel()
       __lowerCamelCase   =						deepcopy(A__	)
       __lowerCamelCase   =						RegressionDataset(length=A__	)
       __lowerCamelCase   =						DataLoader(A__  ,			batch_size=A__	)
       model.to(accelerator.device	)
       __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(A__  ,			A__	)
       return model, ddp_model, dataloader




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					List[Any]=False	):





       '''simple docstring'''



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

       def tokenize_function(A__       :					str	):
              __lowerCamelCase   =						tokenizer(examples["""sentence1"""]  ,			examples["""sentence2"""]  ,			truncation=A__  ,			max_length=A__	)
              return outputs

       with accelerator.main_process_first():
              __lowerCamelCase   =						dataset.map(
                  A__  ,			batched=A__  ,			remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,			)

       __lowerCamelCase   =						tokenized_datasets.rename_column("""label"""  ,			"""labels"""	)

       def collate_fn(A__       :					List[str]	):
              if use_longest:
                     return tokenizer.pad(A__  ,			padding="""longest"""  ,			return_tensors="""pt"""	)
              return tokenizer.pad(A__  ,			padding="""max_length"""  ,			max_length=128  ,			return_tensors="""pt"""	)

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




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					List[Any]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					List[Any]  ,			A__       :					List[str]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for batch in dataloader:
              __lowerCamelCase,   __lowerCamelCase   =						batch.values()
              with torch.no_grad():
                     __lowerCamelCase   =						model(A__	)
                     __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(A__	)
              targs.append(A__	)
       __lowerCamelCase,   __lowerCamelCase   =						torch.cat(A__	), torch.cat(A__	)
       return logits, targs




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					List[Any]=82  ,			A__       :					List[Any]=False  ,			A__       :					str=False  ,			A__       :					Union[str, Any]=16	):





       '''simple docstring'''



       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						get_basic_setup(A__  ,			A__  ,			A__	)
       __lowerCamelCase,   __lowerCamelCase   =						generate_predictions(A__  ,			A__  ,			A__	)
       assert (
           len(A__	) == num_samples
       ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__	)}'




def 					lowerCamelCase__       (						A__       :					bool = False  ,			A__       :					bool = False	):





       '''simple docstring'''



       __lowerCamelCase   =						evaluate.load("""glue"""  ,			"""mrpc"""	)
       __lowerCamelCase,   __lowerCamelCase   =						get_mrpc_setup(A__  ,			A__	)
       # First do baseline
       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						setup["""no"""]
       model.to(A__	)
       model.eval()
       for batch in dataloader:
              batch.to(A__	)
              with torch.inference_mode():
                     __lowerCamelCase   =						model(**A__	)
              __lowerCamelCase   =						outputs.logits.argmax(dim=-1	)
              metric.add_batch(predictions=A__  ,			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(**A__	)
              __lowerCamelCase   =						outputs.logits.argmax(dim=-1	)
              __lowerCamelCase   =						batch["""labels"""]
              __lowerCamelCase,   __lowerCamelCase   =						accelerator.gather_for_metrics((preds, references)	)
              metric.add_batch(predictions=A__  ,			references=A__	)
       __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 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						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]:
                     __lowerCamelCase   =						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**"""	)
       __lowerCamelCase   =						Accelerator()
       test_torch_metrics(A__  ,			512	)
       accelerator.state._reset_state()




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       main()


if __name__ == "__main__":
       main()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    					=       {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EncoderDecoderModel']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['TFEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_encoder_decoder import EncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_encoder_decoder import EncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
   import sys

   a_    					=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



