"""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


lowerCAmelCase_:					List[str]        =					"true"







def 					__a   (		A    ,				A=82    ,				A=16      ):






     '''simple docstring'''



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







def 					__a   (		A    ,				A=False      ):






     '''simple docstring'''



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

     def tokenize_function(A      ):
          lowercase__				 =							tokenizer(examples["sentence1"]    ,				examples["sentence2"]    ,				truncation=A    ,				max_length=A      )
          return outputs

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

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

     def collate_fn(A      ):
          if use_longest:
               return tokenizer.pad(A    ,				padding="longest"    ,				return_tensors="pt"      )
          return tokenizer.pad(A    ,				padding="max_length"    ,				max_length=1_28    ,				return_tensors="pt"      )

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







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



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







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



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







def 					__a   (		A    ,				A=82    ,				A=False    ,				A=False    ,				A=16      ):






     '''simple docstring'''



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







def 					__a   (		A = False    ,				A = False      ):






     '''simple docstring'''



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

     # Then do distributed
     lowercase__ ,						lowercase__ ,						lowercase__				 =							setup["ddp"]
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               lowercase__				 =							model(**A      )
          lowercase__				 =							outputs.logits.argmax(dim=-1      )
          lowercase__				 =							batch["labels"]
          lowercase__ ,						lowercase__				 =							accelerator.gather_for_metrics((preds, references)      )
          metric.add_batch(predictions=A    ,				references=A      )
     lowercase__				 =							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   (		):






     '''simple docstring'''



     lowercase__				 =							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]:
               lowercase__				 =							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**"      )
     lowercase__				 =							Accelerator()
     test_torch_metrics(A    ,				5_12      )
     accelerator.state._reset_state()







def 					__a   (		A      ):






     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




