






import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard




@slow
@require_flax
class 			snake_case__   (							unittest.TestCase):

							'''simple docstring'''



							def 					__lowercase						(							self    ) ->      List[str]:






												'''simple docstring'''





												super().tearDown()
												gc.collect()



							def 					__lowercase						(							self    ) ->      str:






												'''simple docstring'''





												__snake_case       , __snake_case			:int          =						FlaxStableDiffusionPipeline.from_pretrained(
												    """stabilityai/stable-diffusion-2"""			,						revision="""bf16"""			,						dtype=jnp.bfloataa			,						)

												__snake_case			:Optional[int]          =						"""A painting of a squirrel eating a burger"""
												__snake_case			:Optional[int]          =						jax.device_count()
												__snake_case			:Optional[Any]          =						num_samples * [prompt]
												__snake_case			:int          =						sd_pipe.prepare_inputs(a__    )

												__snake_case			:Dict          =						replicate(a__    )
												__snake_case			:Optional[Any]          =						shard(a__    )

												__snake_case			:Any          =						jax.random.PRNGKey(0    )
												__snake_case			:Tuple          =						jax.random.split(a__			,						jax.device_count()    )

												__snake_case			:List[str]          =						sd_pipe(a__			,						a__			,						a__			,						num_inference_steps=25			,						jit=a__    )[0]
												assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

												__snake_case			:Union[str, Any]          =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
												__snake_case			:Any          =						images[0, 2_53:2_56, 2_53:2_56, -1]

												__snake_case			:int          =						jnp.asarray(jax.device_get(image_slice.flatten()    )    )
												__snake_case			:Any          =						jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]    )
												print(F'''output_slice: {output_slice}'''    )
												assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2



							def 					__lowercase						(							self    ) ->      Tuple:






												'''simple docstring'''





												__snake_case			:List[str]          =						"""stabilityai/stable-diffusion-2"""
												__snake_case       , __snake_case			:Dict          =						FlaxDPMSolverMultistepScheduler.from_pretrained(a__			,						subfolder="""scheduler"""    )
												__snake_case       , __snake_case			:Optional[int]          =						FlaxStableDiffusionPipeline.from_pretrained(
												    a__			,						scheduler=a__			,						revision="""bf16"""			,						dtype=jnp.bfloataa			,						)
												__snake_case			:Optional[int]          =						scheduler_params

												__snake_case			:int          =						"""A painting of a squirrel eating a burger"""
												__snake_case			:Optional[int]          =						jax.device_count()
												__snake_case			:Optional[int]          =						num_samples * [prompt]
												__snake_case			:Any          =						sd_pipe.prepare_inputs(a__    )

												__snake_case			:Union[str, Any]          =						replicate(a__    )
												__snake_case			:Dict          =						shard(a__    )

												__snake_case			:Any          =						jax.random.PRNGKey(0    )
												__snake_case			:Dict          =						jax.random.split(a__			,						jax.device_count()    )

												__snake_case			:Dict          =						sd_pipe(a__			,						a__			,						a__			,						num_inference_steps=25			,						jit=a__    )[0]
												assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

												__snake_case			:str          =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
												__snake_case			:List[Any]          =						images[0, 2_53:2_56, 2_53:2_56, -1]

												__snake_case			:Any          =						jnp.asarray(jax.device_get(image_slice.flatten()    )    )
												__snake_case			:Dict          =						jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]    )
												print(F'''output_slice: {output_slice}'''    )
												assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2










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

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						np.random.default_rng(a__    )
            __snake_case			:List[str]          =						length
            __snake_case			:Optional[Any]          =						rng.normal(size=(length,)    ).astype(np.floataa    )
            __snake_case			:Optional[int]          =						a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return {"x": self.x[i], "y": self.y[i]}




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:List[Any]          =						False
            return x * self.a + self.b






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

     __snake_case			:List[Any]          =						{v: i for i, v in enumerate(snake_case__  )}

     def tokenize_function(snake_case__     :   List[Any]  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[Any]          =						tokenizer(
              examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""  )
          if "label" in examples:
               __snake_case			:Dict          =						[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
     __snake_case			:List[str]          =						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.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


