






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


A_		:     Tuple        =     '''true'''


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :Any=82 ,					UpperCAmelCase__ :Optional[int]=16					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Accelerator ,					UpperCAmelCase__ :Union[str, Any]=False					):






 '''simple docstring'''

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

 def tokenize_function(UpperCAmelCase__ :int					):
  a						=		tokenizer(examples["sentence1"] ,					examples["sentence2"] ,					truncation=snake_case__ ,					max_length=snake_case__					)
  return outputs

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

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

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

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[Any]					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Accelerator ,					UpperCAmelCase__ :Optional[int]=82 ,					UpperCAmelCase__ :Optional[Any]=False ,					UpperCAmelCase__ :List[Any]=False ,					UpperCAmelCase__ :List[Any]=16					):






 '''simple docstring'''

 a						=		get_basic_setup(snake_case__ ,					snake_case__ ,					snake_case__					)
 a						=		generate_predictions(snake_case__ ,					snake_case__ ,					snake_case__					)
 assert (
     len(snake_case__					) == num_samples
 ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__					)}"""


def 	UpperCAmelCase__      (      UpperCAmelCase__ :bool = False ,					UpperCAmelCase__ :bool = False					):






 '''simple docstring'''

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

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






 '''simple docstring'''

 a						=		Accelerator(split_batches=snake_case__ ,					dispatch_batches=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(snake_case__ ,					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						=		Accelerator(split_batches=snake_case__ ,					dispatch_batches=snake_case__					)
   if accelerator.is_local_main_process:
    print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""					)
   test_torch_metrics(snake_case__ ,					99					)
   accelerator.state._reset_state()
 if accelerator.is_local_main_process:
  print("**Test last batch is not dropped when perfectly divisible**"					)
 a						=		Accelerator()
 test_torch_metrics(snake_case__ ,					5_12					)
 accelerator.state._reset_state()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any]					):






 '''simple docstring'''

 main()


if __name__ == "__main__":
     main()






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A_		:     int        =     logging.get_logger(__name__)

A_		:     str        =     {
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''focalnet'''

   def __init__(      self			:       int							,   __lowerCAmelCase			:       Optional[Any]=224							,   __lowerCAmelCase			:       Any=4							,   __lowerCAmelCase			:       Any=3							,   __lowerCAmelCase			:       Tuple=96							,   __lowerCAmelCase			:       Dict=False							,   __lowerCAmelCase			:       Optional[int]=[192, 384, 768, 768]							,   __lowerCAmelCase			:       Union[str, Any]=[2, 2, 6, 2]							,   __lowerCAmelCase			:       Optional[int]=[2, 2, 2, 2]							,   __lowerCAmelCase			:       Union[str, Any]=[3, 3, 3, 3]							,   __lowerCAmelCase			:       str="gelu"							,   __lowerCAmelCase			:       Any=4.0							,   __lowerCAmelCase			:       Optional[int]=0.0							,   __lowerCAmelCase			:       Tuple=0.1							,   __lowerCAmelCase			:       str=False							,   __lowerCAmelCase			:       Optional[int]=1E-4							,   __lowerCAmelCase			:       Optional[Any]=False							,   __lowerCAmelCase			:       Union[str, Any]=False							,   __lowerCAmelCase			:       str=False							,   __lowerCAmelCase			:       Any=0.0_2							,   __lowerCAmelCase			:       str=1E-5							,   __lowerCAmelCase			:       Optional[Any]=32							,   __lowerCAmelCase			:       Dict=None							,   __lowerCAmelCase			:       str=None							,   **__lowerCAmelCase			:       Any							,   )       ->				List[str]:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase							)

    a						=		image_size
    a						=		patch_size
    a						=		num_channels
    a						=		embed_dim
    a						=		use_conv_embed
    a						=		hidden_sizes
    a						=		depths
    a						=		focal_levels
    a						=		focal_windows
    a						=		hidden_act
    a						=		mlp_ratio
    a						=		hidden_dropout_prob
    a						=		drop_path_rate
    a						=		use_layerscale
    a						=		layerscale_value
    a						=		use_post_layernorm
    a						=		use_post_layernorm_in_modulation
    a						=		normalize_modulator
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		encoder_stride
    a						=		["stem"] + [f"""stage{idx}""" for idx in range(1							,   len(self.depths							) + 1							)]
    a		,  a						=		get_aligned_output_features_output_indices(
        out_features=__lowerCAmelCase							,   out_indices=__lowerCAmelCase							,   stage_names=self.stage_names							)

