






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class 			snake_case__   (							unittest.TestCase):

							'''simple docstring'''



							def 					__lowercase						(							self    ) ->      Tuple:






												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



							@property
							def 					__lowercase						(							self    ) ->      List[Any]:






												'''simple docstring'''





												__snake_case			:List[Any]          =						1
												__snake_case			:List[str]          =						3
												__snake_case			:List[Any]          =						(32, 32)

												__snake_case			:Optional[Any]          =						floats_tensor((batch_size, num_channels) + sizes			,						rng=random.Random(0    )    ).to(a__    )
												return image



							@property
							def 					__lowercase						(							self    ) ->      int:






												'''simple docstring'''





												torch.manual_seed(0    )
												__snake_case			:int          =						UNetaDConditionModel(
												    block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=4			,						out_channels=4			,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,						cross_attention_dim=32			,						)
												return model



							@property
							def 					__lowercase						(							self    ) ->      Union[str, Any]:






												'''simple docstring'''





												torch.manual_seed(0    )
												__snake_case			:int          =						AutoencoderKL(
												    block_out_channels=[32, 64]			,						in_channels=3			,						out_channels=3			,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,						latent_channels=4			,						)
												return model



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






												'''simple docstring'''





												torch.manual_seed(0    )
												__snake_case			:Optional[int]          =						RobertaSeriesConfig(
												    hidden_size=32			,						project_dim=32			,						intermediate_size=37			,						layer_norm_eps=1e-05			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=50_06			,						)
												return RobertaSeriesModelWithTransformation(a__    )



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






												'''simple docstring'''





												def extract(*a__			,						**a__    ):




																	class 			snake_case__   :

																								'''simple docstring'''



																								def __init__(							self    ) ->      Optional[Any]:






																													'''simple docstring'''





																													__snake_case			:str          =						torch.ones([0]    )



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






																													'''simple docstring'''





																													self.pixel_values.to(a__    )
																													return self
																	return Out()

												return extract



							def 					__lowercase						(							self    ) ->      str:






												'''simple docstring'''





												__snake_case			:Dict          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												__snake_case			:Optional[int]          =						self.dummy_cond_unet
												__snake_case			:Tuple          =						PNDMScheduler(skip_prk_steps=a__    )
												__snake_case			:Any          =						self.dummy_vae
												__snake_case			:Tuple          =						self.dummy_text_encoder
												__snake_case			:Dict          =						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
												__snake_case			:Optional[Any]          =						77

												__snake_case			:Any          =						self.dummy_image.to(a__    )
												__snake_case			:List[str]          =						init_image / 2 + 0.5

												# make sure here that pndm scheduler skips prk
												__snake_case			:Optional[int]          =						AltDiffusionImgaImgPipeline(
												    unet=a__			,						scheduler=a__			,						vae=a__			,						text_encoder=a__			,						tokenizer=a__			,						safety_checker=a__			,						feature_extractor=self.dummy_extractor			,						)
												__snake_case			:int          =						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor			,						do_normalize=a__    )
												__snake_case			:str          =						alt_pipe.to(a__    )
												alt_pipe.set_progress_bar_config(disable=a__    )

												__snake_case			:Tuple          =						"""A painting of a squirrel eating a burger"""
												__snake_case			:Union[str, Any]          =						torch.Generator(device=a__    ).manual_seed(0    )
												__snake_case			:int          =						alt_pipe(
												    [prompt]			,						generator=a__			,						guidance_scale=6.0			,						num_inference_steps=2			,						output_type="""np"""			,						image=a__			,						)

												__snake_case			:Dict          =						output.images

												__snake_case			:Optional[int]          =						torch.Generator(device=a__    ).manual_seed(0    )
												__snake_case			:List[str]          =						alt_pipe(
												    [prompt]			,						generator=a__			,						guidance_scale=6.0			,						num_inference_steps=2			,						output_type="""np"""			,						image=a__			,						return_dict=a__			,						)[0]

												__snake_case			:Optional[int]          =						image[0, -3:, -3:, -1]
												__snake_case			:Any          =						image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												__snake_case			:Tuple          =						np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99]    )

												assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3



							@unittest.skipIf(torch_device != """cuda"""			,						"""This test requires a GPU"""    )
							def 					__lowercase						(							self    ) ->      Tuple:






												'''simple docstring'''





												__snake_case			:Any          =						self.dummy_cond_unet
												__snake_case			:Optional[int]          =						PNDMScheduler(skip_prk_steps=a__    )
												__snake_case			:int          =						self.dummy_vae
												__snake_case			:Tuple          =						self.dummy_text_encoder
												__snake_case			:List[str]          =						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
												__snake_case			:Optional[int]          =						77

												__snake_case			:Optional[int]          =						self.dummy_image.to(a__    )

												# put models in fp16
												__snake_case			:Optional[int]          =						unet.half()
												__snake_case			:Optional[int]          =						vae.half()
												__snake_case			:Optional[Any]          =						bert.half()

												# make sure here that pndm scheduler skips prk
												__snake_case			:List[Any]          =						AltDiffusionImgaImgPipeline(
												    unet=a__			,						scheduler=a__			,						vae=a__			,						text_encoder=a__			,						tokenizer=a__			,						safety_checker=a__			,						feature_extractor=self.dummy_extractor			,						)
												__snake_case			:Optional[int]          =						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor			,						do_normalize=a__    )
												__snake_case			:List[str]          =						alt_pipe.to(a__    )
												alt_pipe.set_progress_bar_config(disable=a__    )

												__snake_case			:Optional[Any]          =						"""A painting of a squirrel eating a burger"""
												__snake_case			:Tuple          =						torch.manual_seed(0    )
												__snake_case			:List[str]          =						alt_pipe(
												    [prompt]			,						generator=a__			,						num_inference_steps=2			,						output_type="""np"""			,						image=a__			,						).images

												assert image.shape == (1, 32, 32, 3)



							@unittest.skipIf(torch_device != """cuda"""			,						"""This test requires a GPU"""    )
							def 					__lowercase						(							self    ) ->      Optional[Any]:






												'''simple docstring'''





												__snake_case			:Tuple          =						load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""    )
												# resize to resolution that is divisible by 8 but not 16 or 32
												__snake_case			:List[str]          =						init_image.resize((7_60, 5_04)    )

												__snake_case			:str          =						"""BAAI/AltDiffusion"""
												__snake_case			:str          =						AltDiffusionImgaImgPipeline.from_pretrained(
												    a__			,						safety_checker=a__			,						)
												pipe.to(a__    )
												pipe.set_progress_bar_config(disable=a__    )
												pipe.enable_attention_slicing()

												__snake_case			:List[str]          =						"""A fantasy landscape, trending on artstation"""

												__snake_case			:Dict          =						torch.manual_seed(0    )
												__snake_case			:str          =						pipe(
												    prompt=a__			,						image=a__			,						strength=0.75			,						guidance_scale=7.5			,						generator=a__			,						output_type="""np"""			,						)
												__snake_case			:Optional[int]          =						output.images[0]

												__snake_case			:Union[str, Any]          =						image[2_55:2_58, 3_83:3_86, -1]

												assert image.shape == (5_04, 7_60, 3)
												__snake_case			:Dict          =						np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00]    )

												assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




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

							'''simple docstring'''



							def 					__lowercase						(							self    ) ->      Any:






												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



							def 					__lowercase						(							self    ) ->      Any:






												'''simple docstring'''





												__snake_case			:Dict          =						load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""    )
												__snake_case			:Optional[Any]          =						init_image.resize((7_68, 5_12)    )
												__snake_case			:str          =						load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

												__snake_case			:Any          =						"""BAAI/AltDiffusion"""
												__snake_case			:Optional[int]          =						AltDiffusionImgaImgPipeline.from_pretrained(
												    a__			,						safety_checker=a__			,						)
												pipe.to(a__    )
												pipe.set_progress_bar_config(disable=a__    )
												pipe.enable_attention_slicing()

												__snake_case			:Dict          =						"""A fantasy landscape, trending on artstation"""

												__snake_case			:Optional[int]          =						torch.manual_seed(0    )
												__snake_case			:List[str]          =						pipe(
												    prompt=a__			,						image=a__			,						strength=0.75			,						guidance_scale=7.5			,						generator=a__			,						output_type="""np"""			,						)
												__snake_case			:Optional[Any]          =						output.images[0]

												assert image.shape == (5_12, 7_68, 3)
												# img2img is flaky across GPUs even in fp32, so using MAE here
												assert np.abs(expected_image - image    ).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


