





'''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


_SCREAMING_SNAKE_CASE   =      '''true'''




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					int=8_2						,    lowerCamelCase_     :					Dict=1_6			):
   set_seed(4_2			)
   __lowercase			  =							RegressionModel()
   __lowercase			  =							deepcopy(lowerCamelCase_			)
   __lowercase			  =							RegressionDataset(length=lowerCamelCase_			)
   __lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=lowerCamelCase_			)
   model.to(accelerator.device			)
   __lowercase  ,						__lowercase			  =							accelerator.prepare(lowerCamelCase_						,    lowerCamelCase_			)
   return model, ddp_model, dataloader




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					Tuple=False			):
   __lowercase			  =							AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''			)
   __lowercase			  =							load_dataset('''glue'''						,    '''mrpc'''						,    split='''validation'''			)

   def tokenize_function(lowerCamelCase_     :					int			):
      __lowercase			  =							tokenizer(examples['''sentence1''']						,    examples['''sentence2''']						,    truncation=lowerCamelCase_						,    max_length=lowerCamelCase_			)
      return outputs

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

   __lowercase			  =							tokenized_datasets.rename_column('''label'''						,    '''labels'''			)

   def collate_fn(lowerCamelCase_     :					List[str]			):
      if use_longest:
         return tokenizer.pad(lowerCamelCase_						,    padding='''longest'''						,    return_tensors='''pt'''			)
      return tokenizer.pad(lowerCamelCase_						,    padding='''max_length'''						,    max_length=1_2_8						,    return_tensors='''pt'''			)

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




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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					str			):
   __lowercase			  =							[]
   for batch in dataloader:
      __lowercase  ,						__lowercase			  =							batch.values()
      with torch.no_grad():
         __lowercase			  =							model(lowerCamelCase_			)
         __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(lowerCamelCase_			)
      targs.append(lowerCamelCase_			)
   __lowercase  ,						__lowercase			  =							torch.cat(lowerCamelCase_			), torch.cat(lowerCamelCase_			)
   return logits, targs




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					List[str]=8_2						,    lowerCamelCase_     :					int=False						,    lowerCamelCase_     :					str=False						,    lowerCamelCase_     :					Any=1_6			):
   __lowercase  ,						__lowercase  ,						__lowercase			  =							get_basic_setup(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
   __lowercase  ,						__lowercase			  =							generate_predictions(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
   assert (
       len(lowerCamelCase_			) == num_samples
   ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase_			)}"




def 				_lowerCAmelCase  (			lowerCamelCase_     :					bool = False						,    lowerCamelCase_     :					bool = False			):
   __lowercase			  =							evaluate.load('''glue'''						,    '''mrpc'''			)
   __lowercase  ,						__lowercase			  =							get_mrpc_setup(lowerCamelCase_						,    lowerCamelCase_			)
   # First do baseline
   __lowercase  ,						__lowercase  ,						__lowercase			  =							setup['''no''']
   model.to(lowerCamelCase_			)
   model.eval()
   for batch in dataloader:
      batch.to(lowerCamelCase_			)
      with torch.inference_mode():
         __lowercase			  =							model(**lowerCamelCase_			)
      __lowercase			  =							outputs.logits.argmax(dim=-1			)
      metric.add_batch(predictions=lowerCamelCase_						,    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(**lowerCamelCase_			)
      __lowercase			  =							outputs.logits.argmax(dim=-1			)
      __lowercase			  =							batch['''labels''']
      __lowercase  ,						__lowercase			  =							accelerator.gather_for_metrics((preds, references)			)
      metric.add_batch(predictions=lowerCamelCase_						,    references=lowerCamelCase_			)
   __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 				_lowerCAmelCase  (			):
   __lowercase			  =							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]:
         __lowercase			  =							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_						,    9_9			)
         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(lowerCamelCase_						,    5_1_2			)
   accelerator.state._reset_state()




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()












'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "dpt"


	def __init__(self       ,_lowerCamelCase=768       ,_lowerCamelCase=12       ,_lowerCamelCase=12       ,_lowerCamelCase=3072       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-1_2       ,_lowerCamelCase=384       ,_lowerCamelCase=16       ,_lowerCamelCase=3       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=[2, 5, 8, 11]       ,_lowerCamelCase="project"       ,_lowerCamelCase=[4, 2, 1, 0.5]       ,_lowerCamelCase=[96, 192, 384, 768]       ,_lowerCamelCase=256       ,_lowerCamelCase=-1       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0.4       ,_lowerCamelCase=255       ,_lowerCamelCase=0.1       ,_lowerCamelCase=[1, 1024, 24, 24]       ,_lowerCamelCase=[0, 1]       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Tuple:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)

				__lowercase			  =							hidden_size
				__lowercase			  =							is_hybrid

				if self.is_hybrid:
							if backbone_config is None:
										logger.info('''Initializing the config with a `BiT` backbone.'''	)
										__lowercase			  =							{
										    '''global_padding''': '''same''',
										    '''layer_type''': '''bottleneck''',
										    '''depths''': [3, 4, 9],
										    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
										    '''embedding_dynamic_padding''': True,
										}
										__lowercase			  =							BitConfig(**_lowerCamelCase	)
							elif isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										logger.info('''Initializing the config with a `BiT` backbone.'''	)
										__lowercase			  =							BitConfig(**_lowerCamelCase	)
							elif isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										__lowercase			  =							backbone_config
							else:
										raise ValueError(
										    f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."	)

							__lowercase			  =							backbone_featmap_shape
							__lowercase			  =							neck_ignore_stages

							if readout_type != "project":
										raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''	)
				else:
							__lowercase			  =							None
							__lowercase			  =							None
							__lowercase			  =							[]

				__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			  =							qkv_bias
				__lowercase			  =							backbone_out_indices
				if readout_type not in ["ignore", "add", "project"]:
							raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''	)
				__lowercase			  =							readout_type
				__lowercase			  =							reassemble_factors
				__lowercase			  =							neck_hidden_sizes
				__lowercase			  =							fusion_hidden_size
				__lowercase			  =							head_in_index
				__lowercase			  =							use_batch_norm_in_fusion_residual
				# auxiliary head attributes (semantic segmentation)
				__lowercase			  =							use_auxiliary_head
				__lowercase			  =							auxiliary_loss_weight
				__lowercase			  =							semantic_loss_ignore_index
				__lowercase			  =							semantic_classifier_dropout


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







				'''simple docstring'''





				__lowercase			  =							copy.deepcopy(self.__dict__	)

				if output["backbone_config"] is not None:
							__lowercase			  =							self.backbone_config.to_dict()

				__lowercase			  =							self.__class__.model_type
				return output





