




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode







def  lowerCAmelCase  (	UpperCAmelCase,	**UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					return RunBeamCommand(
					    args.dataset,	args.name,	args.cache_dir,	args.beam_pipeline_options,	args.data_dir,	args.all_configs,	args.save_info or args.save_infos,	args.ignore_verifications,	args.force_redownload,	**UpperCAmelCase,	)





class 		A__				(   __SCREAMING_SNAKE_CASE ):






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							int		   =							parser.add_parser('''run_beam'''				,     help='''Run a Beam dataset processing pipeline'''						)
						run_beam_parser.add_argument('''dataset'''				,     type=lowerCamelCase				,     help='''Name of the dataset to download'''						)
						run_beam_parser.add_argument('''--name'''				,     type=lowerCamelCase				,     default=lowerCamelCase				,     help='''Dataset config name'''						)
						run_beam_parser.add_argument(
						    '''--cache_dir'''				,     type=lowerCamelCase				,     default=lowerCamelCase				,     help='''Cache directory where the datasets are stored'''				,     )
						run_beam_parser.add_argument(
						    '''--beam_pipeline_options'''				,     type=lowerCamelCase				,     default=''''''				,     help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''				,     )
						run_beam_parser.add_argument(
						    '''--data_dir'''				,     type=lowerCamelCase				,     default=lowerCamelCase				,     help='''Can be used to specify a manual directory to get the files from'''				,     )
						run_beam_parser.add_argument('''--all_configs'''				,     action='''store_true'''				,     help='''Test all dataset configurations'''						)
						run_beam_parser.add_argument('''--save_info'''				,     action='''store_true'''				,     help='''Save the dataset infos file'''						)
						run_beam_parser.add_argument(
						    '''--ignore_verifications'''				,     action='''store_true'''				,     help='''Run the test without checksums and splits checks'''						)
						run_beam_parser.add_argument('''--force_redownload'''				,     action='''store_true'''				,     help='''Force dataset redownload'''						)
						# aliases
						run_beam_parser.add_argument('''--save_infos'''				,     action='''store_true'''				,     help='''alias for save_info'''						)
						run_beam_parser.set_defaults(func=lowerCamelCase						)






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     **lowerCamelCase				,     )      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							int		   =							dataset
						__magic_name__					:							str		   =							name
						__magic_name__					:							Any		   =							cache_dir
						__magic_name__					:							int		   =							beam_pipeline_options
						__magic_name__					:							List[str]		   =							data_dir
						__magic_name__					:							List[Any]		   =							all_configs
						__magic_name__					:							List[str]		   =							save_infos
						__magic_name__					:							str		   =							ignore_verifications
						__magic_name__					:							Tuple		   =							force_redownload
						__magic_name__					:							Tuple		   =							config_kwargs






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




						"""simple docstring"""






						import apache_beam as beam

						if self._name is not None and self._all_configs:
											print('''Both parameters `name` and `all_configs` can\'t be used at once.'''						)
											exit(1						)
						__magic_name__   , __magic_name__					:							Optional[Any]		   =							self._dataset, self._name
						__magic_name__					:							Tuple		   =							dataset_module_factory(lowerCamelCase						)
						__magic_name__					:							Tuple		   =							import_main_class(dataset_module.module_path						)
						__magic_name__					:							List[DatasetBuilder]		   =							[]
						if self._beam_pipeline_options:
											__magic_name__					:							List[Any]		   =							beam.options.pipeline_options.PipelineOptions(
											    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''						) if opt]						)
						else:
											__magic_name__					:							Optional[int]		   =							None
						if self._all_configs and len(builder_cls.BUILDER_CONFIGS						) > 0:
											for builder_config in builder_cls.BUILDER_CONFIGS:
																builders.append(
																    builder_cls(
																        config_name=builder_config.name				,     data_dir=self._data_dir				,     hash=dataset_module.hash				,     beam_options=lowerCamelCase				,     cache_dir=self._cache_dir				,     base_path=dataset_module.builder_kwargs.get('''base_path'''						)				,     )						)
						else:
											builders.append(
											    builder_cls(
											        config_name=lowerCamelCase				,     data_dir=self._data_dir				,     beam_options=lowerCamelCase				,     cache_dir=self._cache_dir				,     base_path=dataset_module.builder_kwargs.get('''base_path'''						)				,     **self._config_kwargs				,     )						)

						for builder in builders:
											builder.download_and_prepare(
											    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
											    if not self._force_redownload
											    else DownloadMode.FORCE_REDOWNLOAD				,     download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH						)				,     verification_mode=VerificationMode.NO_CHECKS
											    if self._ignore_verifications
											    else VerificationMode.ALL_CHECKS				,     try_from_hf_gcs=lowerCamelCase				,     )
											if self._save_infos:
																builder._save_infos()

						print('''Apache beam run successful.'''						)

						# If save_infos=True, the dataset infos file is created next to the loaded module file.
						# Let's move it to the original directory of the dataset script, to allow the user to
						# upload them on S3 at the same time afterwards.
						if self._save_infos:
											__magic_name__					:							Union[str, Any]		   =							os.path.join(builder_cls.get_imported_module_dir()				,     config.DATASETDICT_INFOS_FILENAME						)

											__magic_name__					:							Any		   =							Path(lowerCamelCase						).name + '''.py'''

											__magic_name__					:							List[Any]		   =							os.path.join(lowerCamelCase				,     lowerCamelCase						)
											if os.path.isfile(lowerCamelCase						):
																__magic_name__					:							List[str]		   =							os.path.dirname(lowerCamelCase						)
											elif os.path.isfile(lowerCamelCase						):
																__magic_name__					:							int		   =							path
											else:  # in case of a remote dataset
																print(F'''Dataset Infos file saved at {dataset_infos_path}'''						)
																exit(1						)

											# Move datasetinfo back to the user
											__magic_name__					:							Optional[int]		   =							os.path.join(lowerCamelCase				,     config.DATASETDICT_INFOS_FILENAME						)
											copyfile(lowerCamelCase				,     lowerCamelCase						)
											print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''						)










"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class    lowercase_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        torch.manual_seed(0   )
        snake_case__										= UNetaDConditionModel(
            block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=9      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=32      ,    )
        snake_case__										= PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
        torch.manual_seed(0   )
        snake_case__										= 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      ,    )
        torch.manual_seed(0   )
        snake_case__										= CLIPVisionConfig(
            hidden_size=32      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    image_size=32      ,    patch_size=4      ,    )
        snake_case__										= PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE      ,    proj_size=32   )
        snake_case__										= CLIPImageProcessor(crop_size=32      ,    size=32   )

        snake_case__										= {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "safety_checker": None,
            "feature_extractor": feature_extractor,
        }
        return components

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= np.array(image.convert("RGB"   )   )
        snake_case__										= image[None].transpose(0      ,    3      ,    1      ,    2   )
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

        snake_case__										= floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((32, 32)   )

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "example_image": example_image,
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= pipe.to("cpu"   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= "cpu"
        snake_case__										= self.get_dummy_inputs()
        inputs.pop("mask_image"   )
        snake_case__										= self.convert_to_pt(inputs.pop("image"   )   )
        snake_case__										= image.clamp(0      ,    1   ) / 2

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**self.get_dummy_components()   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= mask_image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

        snake_case__										= pipe(**self.get_dummy_inputs()   )
        snake_case__										= output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/dog_in_bucket.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/mask.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/panda.jpg"   )

        snake_case__										= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= torch.manual_seed(321   )
        snake_case__										= pipe(
            image=__SCREAMING_SNAKE_CASE      ,    mask_image=__SCREAMING_SNAKE_CASE      ,    example_image=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    guidance_scale=5.0      ,    num_inference_steps=50      ,    output_type="np"      ,    )

        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        snake_case__										= np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

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

