



'''simple docstring'''





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    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  def 		_lowercase	(					self :      List[Any]  )   ->   str:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  @property
  def 		_lowercase	(					self :      Optional[Any]  )   ->   Union[str, Any]:
    __lowerCamelCase			:			str       			=					1
    __lowerCamelCase			:			str       			=					3
    __lowerCamelCase			:			Dict       			=					(32, 32)

    __lowerCamelCase			:			Any       			=					floats_tensor((batch_size, num_channels) + sizes     ,      rng=random.Random(0  )  ).to(_a  )
    return image




  @property
  def 		_lowercase	(					self :      Optional[Any]  )   ->   Any:
    torch.manual_seed(0  )
    __lowerCamelCase			:			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]  )   ->   str:
    torch.manual_seed(0  )
    __lowerCamelCase			:			List[Any]       			=					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 :      Optional[Any]  )   ->   Tuple:
    torch.manual_seed(0  )
    __lowerCamelCase			:			List[str]       			=					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=5006     ,      )
    return RobertaSeriesModelWithTransformation(_a  )




  @property
  def 		_lowercase	(					self :      Tuple  )   ->   Dict:
    def extract(*_a :      Optional[Any]     ,      **_a :      List[Any]  ):






      class    lowerCamelCase_       :
        """simple docstring"""




        def __init__(					self :      Any  )   ->   List[str]:
          __lowerCamelCase			:			Any       			=					torch.ones([0]  )




        def 		_lowercase	(					self :      List[str]     ,      _a :      Optional[Any]  )   ->   Dict:
          self.pixel_values.to(_a  )
          return self




      return Out()

    return extract




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

    __lowerCamelCase			:			Optional[int]       			=					self.dummy_image.to(_a  )
    __lowerCamelCase			:			Optional[Any]       			=					init_image / 2 + 0.5

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

    __lowerCamelCase			:			List[Any]       			=					'A painting of a squirrel eating a burger'
    __lowerCamelCase			:			List[str]       			=					torch.Generator(device=_a  ).manual_seed(0  )
    __lowerCamelCase			:			List[str]       			=					alt_pipe(
        [prompt]     ,      generator=_a     ,      guidance_scale=6.0     ,      num_inference_steps=2     ,      output_type='np'     ,      image=_a     ,      )

    __lowerCamelCase			:			Optional[Any]       			=					output.images

    __lowerCamelCase			:			Dict       			=					torch.Generator(device=_a  ).manual_seed(0  )
    __lowerCamelCase			:			Any       			=					alt_pipe(
        [prompt]     ,      generator=_a     ,      guidance_scale=6.0     ,      num_inference_steps=2     ,      output_type='np'     ,      image=_a     ,      return_dict=_a     ,      )[0]

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

    assert image.shape == (1, 32, 32, 3)
    __lowerCamelCase			:			str       			=					np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]  )

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

    __lowerCamelCase			:			Optional[Any]       			=					self.dummy_image.to(_a  )

    # put models in fp16
    __lowerCamelCase			:			str       			=					unet.half()
    __lowerCamelCase			:			Union[str, Any]       			=					vae.half()
    __lowerCamelCase			:			Dict       			=					bert.half()

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

    __lowerCamelCase			:			Optional[Any]       			=					'A painting of a squirrel eating a burger'
    __lowerCamelCase			:			Union[str, Any]       			=					torch.manual_seed(0  )
    __lowerCamelCase			:			Dict       			=					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]  )   ->   str:
    __lowerCamelCase			:			str       			=					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
    __lowerCamelCase			:			Union[str, Any]       			=					init_image.resize((760, 504)  )

    __lowerCamelCase			:			Union[str, Any]       			=					'BAAI/AltDiffusion'
    __lowerCamelCase			:			int       			=					AltDiffusionImgaImgPipeline.from_pretrained(
        _a     ,      safety_checker=_a     ,      )
    pipe.to(_a  )
    pipe.set_progress_bar_config(disable=_a  )
    pipe.enable_attention_slicing()

    __lowerCamelCase			:			List[Any]       			=					'A fantasy landscape, trending on artstation'

    __lowerCamelCase			:			List[str]       			=					torch.manual_seed(0  )
    __lowerCamelCase			:			Any       			=					pipe(
        prompt=_a     ,      image=_a     ,      strength=0.75     ,      guidance_scale=7.5     ,      generator=_a     ,      output_type='np'     ,      )
    __lowerCamelCase			:			List[Any]       			=					output.images[0]

    __lowerCamelCase			:			Dict       			=					image[255:258, 383:386, -1]

    assert image.shape == (504, 760, 3)
    __lowerCamelCase			:			List[str]       			=					np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]  )

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






@slow
@require_torch_gpu
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  def 		_lowercase	(					self :      Optional[int]  )   ->   Dict:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  def 		_lowercase	(					self :      Tuple  )   ->   Dict:
    __lowerCamelCase			:			int       			=					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/img2img/sketch-mountains-input.jpg'  )
    __lowerCamelCase			:			Optional[int]       			=					init_image.resize((768, 512)  )
    __lowerCamelCase			:			Union[str, Any]       			=					load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'  )

    __lowerCamelCase			:			Any       			=					'BAAI/AltDiffusion'
    __lowerCamelCase			:			List[str]       			=					AltDiffusionImgaImgPipeline.from_pretrained(
        _a     ,      safety_checker=_a     ,      )
    pipe.to(_a  )
    pipe.set_progress_bar_config(disable=_a  )
    pipe.enable_attention_slicing()

    __lowerCamelCase			:			Union[str, Any]       			=					'A fantasy landscape, trending on artstation'

    __lowerCamelCase			:			Optional[int]       			=					torch.manual_seed(0  )
    __lowerCamelCase			:			Dict       			=					pipe(
        prompt=_a     ,      image=_a     ,      strength=0.75     ,      guidance_scale=7.5     ,      generator=_a     ,      output_type='np'     ,      )
    __lowerCamelCase			:			Optional[int]       			=					output.images[0]

    assert image.shape == (512, 768, 3)
    # img2img is flaky across GPUs even in fp32, so using MAE here
    assert np.abs(expected_image - image  ).max() < 1e-2














import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





