





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 				A_				:







       """simple docstring"""





       def __init__(    self	:	Tuple						,__A	:	int=2						,__A	:	List[Any]=3						,__A	:	Optional[Any]=64						,__A	:	List[str]=None  )						->					Tuple:
         _lowercase							= np.random.default_rng(__A  )
         _lowercase							= length
         _lowercase							= rng.normal(size=(length,)  ).astype(np.floataa  )
         _lowercase							= a * self.x + b + rng.normal(scale=0.1						,size=(length,)  ).astype(np.floataa  )







       def __len__(    self	:	Dict  )						->					int:
         return self.length







       def __getitem__(    self	:	List[str]						,__A	:	List[Any]  )						->					Tuple:
         return {"x": self.x[i], "y": self.y[i]}





class 				A_				(      torch.nn.Module	):







       """simple docstring"""





       def __init__(    self	:	Optional[int]						,__A	:	Optional[int]=0						,__A	:	Any=0						,__A	:	Dict=False  )						->					List[Any]:
         super().__init__()
         _lowercase							= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
         _lowercase							= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
         _lowercase							= True







       def    __UpperCAmelCase (    self	:	int						,__A	:	str=None  )						->					Dict:
         if self.first_batch:
           print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
           _lowercase							= False
         return x * self.a[0] + self.b[0]





class 				A_				(      torch.nn.Module	):







       """simple docstring"""





       def __init__(    self	:	Optional[Any]						,__A	:	Union[str, Any]=0						,__A	:	Optional[int]=0						,__A	:	str=False  )						->					Optional[Any]:
         super().__init__()
         _lowercase							= torch.nn.Parameter(torch.tensor(__A  ).float()  )
         _lowercase							= torch.nn.Parameter(torch.tensor(__A  ).float()  )
         _lowercase							= True







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Optional[Any]=None  )						->					str:
         if self.first_batch:
           print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
           _lowercase							= False
         return x * self.a + self.b

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :int = 16     )      ->						int:
  from datasets import load_dataset
  from transformers import AutoTokenizer

  _lowercase							= AutoTokenizer.from_pretrained('bert-base-cased'     )
  _lowercase							= {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
  _lowercase							= load_dataset('csv'			,							data_files=snake_case__     )
  _lowercase							= datasets['train'].unique('label'     )

  _lowercase							= {v: i for i, v in enumerate(snake_case__     )}

  def tokenize_function(snake_case__  :str     ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase							= tokenizer(
        examples['sentence1']			,							examples['sentence2']			,							truncation=snake_case__			,							max_length=snake_case__			,							padding='max_length'     )
    if "label" in examples:
      _lowercase							= [label_to_id[l] for l in examples['label']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  _lowercase							= datasets.map(
      snake_case__			,							batched=snake_case__			,							remove_columns=['sentence1', 'sentence2', 'label']			,							)

  def collate_fn(snake_case__  :Optional[int]     ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(snake_case__			,							padding='max_length'			,							max_length=128			,							return_tensors='pt'     )
    return tokenizer.pad(snake_case__			,							padding='longest'			,							return_tensors='pt'     )

  # Instantiate dataloaders.
  _lowercase							= DataLoader(tokenized_datasets['train']			,							shuffle=snake_case__			,							collate_fn=snake_case__			,							batch_size=2     )
  _lowercase							= DataLoader(tokenized_datasets['validation']			,							shuffle=snake_case__			,							collate_fn=snake_case__			,							batch_size=1     )

  return train_dataloader, eval_dataloader






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)