
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 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any=82				,					__lowerCamelCase     : int=16    ):
       set_seed(42    )
       snake_case       :   Optional[int]							=      RegressionModel()
       snake_case       :   Optional[Any]							=      deepcopy(__lowerCamelCase    )
       snake_case       :   Any							=      RegressionDataset(length=__lowerCamelCase    )
       snake_case       :   List[Any]							=      DataLoader(__lowerCamelCase				,					batch_size=__lowerCamelCase    )
       model.to(accelerator.device    )
       snake_case				,					snake_case       :   Tuple							=      accelerator.prepare(__lowerCamelCase				,					__lowerCamelCase    )
       return model, ddp_model, dataloader


def 			UpperCamelCase					(    __lowerCamelCase     : Accelerator				,					__lowerCamelCase     : List[str]=False    ):
       snake_case       :   Tuple							=      AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"    )
       snake_case       :   Any							=      load_dataset("glue"				,					"mrpc"				,					split="validation"    )

       def tokenize_function(__lowerCamelCase     : Optional[Any]    ):
              snake_case       :   List[Any]							=      tokenizer(examples["sentence1"]				,					examples["sentence2"]				,					truncation=__lowerCamelCase				,					max_length=__lowerCamelCase    )
              return outputs

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

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

       def collate_fn(__lowerCamelCase     : Any    ):
              if use_longest:
                     return tokenizer.pad(__lowerCamelCase				,					padding="longest"				,					return_tensors="pt"    )
              return tokenizer.pad(__lowerCamelCase				,					padding="max_length"				,					max_length=128				,					return_tensors="pt"    )

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


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


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


def 			UpperCamelCase					(    __lowerCamelCase     : Accelerator				,					__lowerCamelCase     : Any=82				,					__lowerCamelCase     : int=False				,					__lowerCamelCase     : Any=False				,					__lowerCamelCase     : Optional[int]=16    ):
       snake_case				,					snake_case				,					snake_case       :   Optional[Any]							=      get_basic_setup(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
       snake_case				,					snake_case       :   Dict							=      generate_predictions(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
       assert (
           len(__lowerCamelCase    ) == num_samples
       ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCamelCase    )}"""


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

       # Then do distributed
       snake_case				,					snake_case				,					snake_case       :   Optional[int]							=      setup["ddp"]
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     snake_case       :   str							=      model(**__lowerCamelCase    )
              snake_case       :   List[Any]							=      outputs.logits.argmax(dim=-1    )
              snake_case       :   Optional[int]							=      batch["labels"]
              snake_case				,					snake_case       :   Dict							=      accelerator.gather_for_metrics((preds, references)    )
              metric.add_batch(predictions=__lowerCamelCase				,					references=__lowerCamelCase    )
       snake_case       :   Optional[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 			UpperCamelCase					(    ):
       snake_case       :   List[Any]							=      Accelerator(split_batches=__lowerCamelCase				,					dispatch_batches=__lowerCamelCase    )
       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(__lowerCamelCase				,					__lowerCamelCase    )
                            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]:
                     snake_case       :   Optional[Any]							=      Accelerator(split_batches=__lowerCamelCase				,					dispatch_batches=__lowerCamelCase    )
                     if accelerator.is_local_main_process:
                            print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""    )
                     test_torch_metrics(__lowerCamelCase				,					99    )
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print("**Test last batch is not dropped when perfectly divisible**"    )
       snake_case       :   Optional[int]							=      Accelerator()
       test_torch_metrics(__lowerCamelCase				,					512    )
       accelerator.state._reset_state()


def 			UpperCamelCase					(    __lowerCamelCase     : Tuple    ):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
   main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "megatron-bert"



   def __init__(self		:					Optional[int]    ,						snake_case__		:					List[str]=2_90_56    ,						snake_case__		:					List[Any]=10_24    ,						snake_case__		:					str=24    ,						snake_case__		:					Tuple=16    ,						snake_case__		:					Union[str, Any]=40_96    ,						snake_case__		:					str="gelu"    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					Tuple=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[Any]=1e-12    ,						snake_case__		:					int=0    ,						snake_case__		:					Tuple="absolute"    ,						snake_case__		:					Any=True    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

          snake_case       :   Tuple							=      vocab_size
          snake_case       :   str							=      hidden_size
          snake_case       :   str							=      num_hidden_layers
          snake_case       :   str							=      num_attention_heads
          snake_case       :   Optional[int]							=      hidden_act
          snake_case       :   int							=      intermediate_size
          snake_case       :   List[str]							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   List[str]							=      type_vocab_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   Tuple							=      layer_norm_eps
          snake_case       :   int							=      position_embedding_type
          snake_case       :   str							=      use_cache





