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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=8_2  ,_lowerCAmelCase=1_6     ):
   '''simple docstring'''


   set_seed(4_2     )
   A_     :				Optional[int]			      =	RegressionModel()
   A_     :				List[str]			      =	deepcopy(__SCREAMING_SNAKE_CASE     )
   A_     :				Optional[int]			      =	RegressionDataset(length=__SCREAMING_SNAKE_CASE     )
   A_     :				str			      =	DataLoader(__SCREAMING_SNAKE_CASE  ,batch_size=__SCREAMING_SNAKE_CASE     )
   model.to(accelerator.device     )
   A_     :				Dict			      =	accelerator.prepare(__SCREAMING_SNAKE_CASE  ,__SCREAMING_SNAKE_CASE     )
   return model, ddp_model, dataloader



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""     )
   A_     :				Tuple			      =	load_dataset("""glue"""  ,"""mrpc"""  ,split="""validation"""     )

   def tokenize_function(_lowerCAmelCase     ):
      A_     :				Optional[int]			      =	tokenizer(examples["""sentence1"""]  ,examples["""sentence2"""]  ,truncation=__SCREAMING_SNAKE_CASE  ,max_length=__SCREAMING_SNAKE_CASE     )
      return outputs

   with accelerator.main_process_first():
      A_     :				Tuple			      =	dataset.map(
          __SCREAMING_SNAKE_CASE  ,batched=__SCREAMING_SNAKE_CASE  ,remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,)

   A_     :				Dict			      =	tokenized_datasets.rename_column("""label"""  ,"""labels"""     )

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

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=8_2  ,_lowerCAmelCase=False  ,_lowerCAmelCase=False  ,_lowerCAmelCase=1_6     ):
   '''simple docstring'''


   A_     :				List[str]			      =	get_basic_setup(__SCREAMING_SNAKE_CASE  ,__SCREAMING_SNAKE_CASE  ,__SCREAMING_SNAKE_CASE     )
   A_     :				Union[str, Any]			      =	generate_predictions(__SCREAMING_SNAKE_CASE  ,__SCREAMING_SNAKE_CASE  ,__SCREAMING_SNAKE_CASE     )
   assert (
       len(__SCREAMING_SNAKE_CASE     ) == num_samples
   ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE     )}"""



def     _lowerCAmelCase		(  _lowerCAmelCase = False  ,_lowerCAmelCase = False     ):
   '''simple docstring'''


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

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


   A_     :				Union[str, Any]			      =	Accelerator(split_batches=__SCREAMING_SNAKE_CASE  ,dispatch_batches=__SCREAMING_SNAKE_CASE     )
   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(__SCREAMING_SNAKE_CASE  ,__SCREAMING_SNAKE_CASE     )
            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]:
         A_     :				int			      =	Accelerator(split_batches=__SCREAMING_SNAKE_CASE  ,dispatch_batches=__SCREAMING_SNAKE_CASE     )
         if accelerator.is_local_main_process:
            print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""     )
         test_torch_metrics(__SCREAMING_SNAKE_CASE  ,9_9     )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test last batch is not dropped when perfectly divisible**"""     )
   A_     :				Any			      =	Accelerator()
   test_torch_metrics(__SCREAMING_SNAKE_CASE  ,5_1_2     )
   accelerator.state._reset_state()



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   main()


if __name__ == "__main__":
      main()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase 				=     {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NllbMoeForConditionalGeneration""",
          """NllbMoeModel""",
          """NllbMoePreTrainedModel""",
          """NllbMoeTop2Router""",
          """NllbMoeSparseMLP""",
      ]


if TYPE_CHECKING:
      from .configuration_nllb_moe import (
          NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
          NllbMoeConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nllb_moe import (
                NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
                NllbMoeForConditionalGeneration,
                NllbMoeModel,
                NllbMoePreTrainedModel,
                NllbMoeSparseMLP,
                NllbMoeTopaRouter,
            )


else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






