






'''simple docstring'''

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			,     __lowerCAmelCase=2			,     __lowerCAmelCase=3			,     __lowerCAmelCase=64			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   List[Any]					    =							np.random.default_rng(_UpperCamelCase )
       UpperCamelCase_				:   Dict					    =							length
       UpperCamelCase_				:   Union[str, Any]					    =							rng.normal(size=(length,) ).astype(np.floataa )
       UpperCamelCase_				:   Tuple					    =							a * self.x + b + rng.normal(scale=0.1			,     size=(length,) ).astype(np.floataa )
 def __len__(							self ):
       return self.length





 def __getitem__(							self			,     __lowerCAmelCase ):
       return {"x": self.x[i], "y": self.y[i]}






class     A    (      torch.nn.Module						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase=0			,     __lowerCAmelCase=0			,     __lowerCAmelCase=False ):
       super().__init__()
       UpperCamelCase_				:   Optional[int]					    =							torch.nn.Parameter(torch.tensor([2, 3] ).float() )
       UpperCamelCase_				:   Optional[int]					    =							torch.nn.Parameter(torch.tensor([2, 3] ).float() )
       UpperCamelCase_				:   List[Any]					    =							True





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None ):


       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}" )
             UpperCamelCase_				:   Union[str, Any]					    =							False
       return x * self.a[0] + self.b[0]






class     A    (      torch.nn.Module						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase=0			,     __lowerCAmelCase=0			,     __lowerCAmelCase=False ):
       super().__init__()
       UpperCamelCase_				:   Optional[Any]					    =							torch.nn.Parameter(torch.tensor(_UpperCamelCase ).float() )
       UpperCamelCase_				:   int					    =							torch.nn.Parameter(torch.tensor(_UpperCamelCase ).float() )
       UpperCamelCase_				:   List[Any]					    =							True





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None ):


       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}" )
             UpperCamelCase_				:   str					    =							False
       return x * self.a + self.b






def 							snake_case					(     a_							: Dict    ,   a_							: str = 16	)       ->				str:





      """simple docstring"""

      from datasets import load_dataset
      from transformers import AutoTokenizer

      UpperCamelCase_				:   Optional[int]					    =							AutoTokenizer.from_pretrained("""bert-base-cased"""	)
      UpperCamelCase_				:   List[str]					    =							{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
      UpperCamelCase_				:   int					    =							load_dataset("""csv"""    ,   data_files=__A	)
      UpperCamelCase_				:   int					    =							datasets["""train"""].unique("""label"""	)

      UpperCamelCase_				:   int					    =							{v: i for i, v in enumerate(__A	)}

      def tokenize_function(a_							: Optional[int]	):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase_				:   List[str]					    =							tokenizer(
                examples["""sentence1"""]    ,   examples["""sentence2"""]    ,   truncation=__A    ,   max_length=__A    ,   padding="""max_length"""	)
            if "label" in examples:
                  UpperCamelCase_				:   str					    =							[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
      UpperCamelCase_				:   Optional[Any]					    =							datasets.map(
          __A    ,   batched=__A    ,   remove_columns=["""sentence1""", """sentence2""", """label"""]    ,   )

      def collate_fn(a_							: Optional[Any]	):
            # 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(__A    ,   padding="""max_length"""    ,   max_length=128    ,   return_tensors="""pt"""	)
            return tokenizer.pad(__A    ,   padding="""longest"""    ,   return_tensors="""pt"""	)

      # Instantiate dataloaders.
      UpperCamelCase_				:   Optional[int]					    =							DataLoader(tokenized_datasets["""train"""]    ,   shuffle=__A    ,   collate_fn=__A    ,   batch_size=2	)
      UpperCamelCase_				:   Optional[Any]					    =							DataLoader(tokenized_datasets["""validation"""]    ,   shuffle=__A    ,   collate_fn=__A    ,   batch_size=1	)

      return train_dataloader, eval_dataloader














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCamelCase							="A photo of sks dog in a bucket"
UpperCamelCase							=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")






