




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 	__A	(			__lowerCamelCase					,    **__lowerCamelCase					)   -> Optional[Any]:
	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					,    **__lowerCamelCase					,    )



class 	__lowerCAmelCase			(			__magic_name__				):
							@staticmethod
							def    lowerCamelCase__							(		__magic_name__		:ArgumentParser       ):







								'''simple docstring'''







								a		       =							parser.add_parser("""run_beam"""	,   help="""Run a Beam dataset processing pipeline"""       )
								run_beam_parser.add_argument("""dataset"""	,   type=__magic_name__	,   help="""Name of the dataset to download"""       )
								run_beam_parser.add_argument("""--name"""	,   type=__magic_name__	,   default=__magic_name__	,   help="""Dataset config name"""       )
								run_beam_parser.add_argument(
								    """--cache_dir"""	,   type=__magic_name__	,   default=__magic_name__	,   help="""Cache directory where the datasets are stored"""	,   )
								run_beam_parser.add_argument(
								    """--beam_pipeline_options"""	,   type=__magic_name__	,   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=__magic_name__	,   default=__magic_name__	,   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=__magic_name__       )
							def __init__(		self		:Tuple	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:bool	,   __magic_name__		:bool	,   __magic_name__		:bool	,   __magic_name__		:bool	,   **__magic_name__		:Optional[Any]	,   ):







								'''simple docstring'''







								a		       =							dataset
								a		       =							name
								a		       =							cache_dir
								a		       =							beam_pipeline_options
								a		       =							data_dir
								a		       =							all_configs
								a		       =							save_infos
								a		       =							ignore_verifications
								a		       =							force_redownload
								a		       =							config_kwargs





							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''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       )
								a       ,				a		       =							self._dataset, self._name
								a		       =							dataset_module_factory(__magic_name__       )
								a		       =							import_main_class(dataset_module.module_path       )
								a		       =							[]
								if self._beam_pipeline_options:
									a		       =							beam.options.pipeline_options.PipelineOptions(
									    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(""","""       ) if opt]       )
								else:
									a		       =							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=__magic_name__	,   cache_dir=self._cache_dir	,   base_path=dataset_module.builder_kwargs.get("""base_path"""       )	,   )       )
								else:
									builders.append(
									    builder_cls(
									        config_name=__magic_name__	,   data_dir=self._data_dir	,   beam_options=__magic_name__	,   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=__magic_name__	,   )
									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:
									a		       =							os.path.join(builder_cls.get_imported_module_dir()	,   config.DATASETDICT_INFOS_FILENAME       )

									a		       =							Path(__magic_name__       ).name + """.py"""

									a		       =							os.path.join(__magic_name__	,   __magic_name__       )
									if os.path.isfile(__magic_name__       ):
										a		       =							os.path.dirname(__magic_name__       )
									elif os.path.isfile(__magic_name__       ):
										a		       =							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
									a		       =							os.path.join(__magic_name__	,   config.DATASETDICT_INFOS_FILENAME       )
									copyfile(__magic_name__	,   __magic_name__       )
									print(F'Dataset Infos file saved at {user_dataset_infos_path}'       )







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	__lowerCAmelCase			(			__magic_name__   , __magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						StableDiffusionParadigmsPipeline
							UpperCamelCase__        =						TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__        =						TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCamelCase__        =						TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__        =						TEXT_TO_IMAGE_IMAGE_PARAMS
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								torch.manual_seed(0       )
								a		       =							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	,   attention_head_dim=(2, 4)	,   use_linear_projection=__magic_name__	,   )
								a		       =							DDIMParallelScheduler(
								    beta_start=0.00085	,   beta_end=0.012	,   beta_schedule="""scaled_linear"""	,   clip_sample=__magic_name__	,   set_alpha_to_one=__magic_name__	,   )
								torch.manual_seed(0       )
								a		       =							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	,   sample_size=128	,   )
								torch.manual_seed(0       )
								a		       =							CLIPTextConfig(
								    bos_token_id=0	,   eos_token_id=2	,   hidden_size=32	,   intermediate_size=37	,   layer_norm_eps=1E-0_5	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=1000	,   hidden_act="""gelu"""	,   projection_dim=512	,   )
								a		       =							CLIPTextModel(__magic_name__       )
								a		       =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

								a		       =							{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components
							def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:List[Any]	,   __magic_name__		:Tuple=0       ):







								'''simple docstring'''







								if str(__magic_name__       ).startswith("""mps"""       ):
									a		       =							torch.manual_seed(__magic_name__       )
								else:
									a		       =							torch.Generator(device=__magic_name__       ).manual_seed(__magic_name__       )
								a		       =							{
								    """prompt""": """a photograph of an astronaut riding a horse""",
								    """generator""": generator,
								    """num_inference_steps""": 10,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """parallel""": 3,
								    """debug""": True,
								}
								return inputs
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								a		       =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								a		       =							self.get_dummy_components()
								a		       =							StableDiffusionParadigmsPipeline(**__magic_name__       )
								a		       =							sd_pipe.to(__magic_name__       )
								sd_pipe.set_progress_bar_config(disable=__magic_name__       )

								a		       =							self.get_dummy_inputs(__magic_name__       )
								a		       =							sd_pipe(**__magic_name__       ).images
								a		       =							image[0, -3:, -3:, -1]
								assert image.shape == (1, 64, 64, 3)

								a		       =							np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								a		       =							self.get_dummy_components()
								torch.manual_seed(0       )
								a		       =							DDPMParallelScheduler()
								torch.manual_seed(0       )
								a		       =							StableDiffusionParadigmsPipeline(**__magic_name__       )
								a		       =							sd_pipe.to(__magic_name__       )
								sd_pipe.set_progress_bar_config(disable=__magic_name__       )

								a		       =							self.get_dummy_inputs(__magic_name__       )
								a		       =							sd_pipe(**__magic_name__       ).images
								a		       =							image[0, -3:, -3:, -1]
								assert image.shape == (1, 64, 64, 3)

								a		       =							np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								super().test_inference_batch_consistent(batch_sizes=[1, 2]       )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								super().test_inference_batch_single_identical(batch_size=2	,   expected_max_diff=3E-3       )





							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								a		       =							self.get_dummy_components()
								a		       =							StableDiffusionParadigmsPipeline(**__magic_name__       )
								a		       =							sd_pipe.to(__magic_name__       )
								sd_pipe.set_progress_bar_config(disable=__magic_name__       )

								a		       =							self.get_dummy_inputs(__magic_name__       )
								a		       =							"""french fries"""
								a		       =							sd_pipe(**__magic_name__	,   negative_prompt=__magic_name__       )
								a		       =							output.images
								a		       =							image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								a		       =							np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]       )

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







@slow
@require_torch_gpu
class 	__lowerCAmelCase			(			unittest.TestCase				):
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Union[str, Any]=0       ):







								'''simple docstring'''







								a		       =							torch.Generator(device=__magic_name__       ).manual_seed(__magic_name__       )
								a		       =							{
								    """prompt""": """a photograph of an astronaut riding a horse""",
								    """generator""": generator,
								    """num_inference_steps""": 10,
								    """guidance_scale""": 7.5,
								    """output_type""": """numpy""",
								    """parallel""": 3,
								    """debug""": True,
								}
								return inputs





							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								a		       =							"""stabilityai/stable-diffusion-2-base"""
								a		       =							DDIMParallelScheduler.from_pretrained(__magic_name__	,   subfolder="""scheduler"""       )
								a		       =							StableDiffusionParadigmsPipeline.from_pretrained(__magic_name__	,   scheduler=__magic_name__	,   safety_checker=__magic_name__       )
								pipe.to(__magic_name__       )
								pipe.set_progress_bar_config(disable=__magic_name__       )
								pipe.enable_attention_slicing()

								a		       =							self.get_inputs()
								a		       =							pipe(**__magic_name__       ).images
								a		       =							image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

								a		       =							np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]       )

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

