
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__							:							int													=     datasets.utils.logging.get_logger(__name__)






@dataclass
class     SCREAMING_SNAKE_CASE__      (							datasets.BuilderConfig      ):

							"""simple docstring"""






							_snake_case       						=					None






class     SCREAMING_SNAKE_CASE__      (							datasets.ArrowBasedBuilder      ):

							"""simple docstring"""






							_snake_case       						=					ArrowConfig



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





													'''simple docstring'''






													return datasets.DatasetInfo(features=self.config.features     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													if not self.config.data_files:
																			raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"     )
													__UpperCamelCase       					=     dl_manager.download_and_extract(self.config.data_files     )
													if isinstance(SCREAMING_SNAKE_CASE_       ,		(str, list, tuple)     ):
																			__UpperCamelCase       					=     data_files
																			if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																									__UpperCamelCase       					=     [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCamelCase       					=     [dl_manager.iter_files(SCREAMING_SNAKE_CASE_     ) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,		gen_kwargs={'''files''': files}     )]
													__UpperCamelCase       					=     []
													for split_name, files in data_files.items():
																			if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																									__UpperCamelCase       					=     [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCamelCase       					=     [dl_manager.iter_files(SCREAMING_SNAKE_CASE_     ) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																									for file in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_     ):
																															with open(SCREAMING_SNAKE_CASE_       ,		'''rb'''     ) as f:
																																					__UpperCamelCase       					=     datasets.Features.from_arrow_schema(pa.ipc.open_stream(SCREAMING_SNAKE_CASE_     ).schema     )
																															break
																			splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_       ,		gen_kwargs={'''files''': files}     )     )
													return splits



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				pa.Table:





													'''simple docstring'''






													if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			__UpperCamelCase       					=     table_cast(SCREAMING_SNAKE_CASE_       ,		self.info.features.arrow_schema     )
													return pa_table



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





													'''simple docstring'''






													for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_     )     ):
																			with open(SCREAMING_SNAKE_CASE_       ,		'''rb'''     ) as f:
																									try:
																															for batch_idx, record_batch in enumerate(pa.ipc.open_stream(SCREAMING_SNAKE_CASE_     )     ):
																																					__UpperCamelCase       					=     pa.Table.from_batches([record_batch]     )
																																					# Uncomment for debugging (will print the Arrow table size and elements)
																																					# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																					# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																					yield F"{file_idx}_{batch_idx}", self._cast_table(SCREAMING_SNAKE_CASE_     )
																									except ValueError as e:
																															logger.error(F"Failed to read file '{file}' with error {type(SCREAMING_SNAKE_CASE_     )}: {e}"     )
																															raise






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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						unittest.TestCase      ):

							"""simple docstring"""






							_snake_case       						=					StableDiffusionParadigmsPipeline
							_snake_case       						=					TEXT_TO_IMAGE_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_IMAGE_PARAMS



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													torch.manual_seed(0     )
													__UpperCamelCase       					=     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=SCREAMING_SNAKE_CASE_       ,		)
													__UpperCamelCase       					=     DDIMParallelScheduler(
													    beta_start=0.0_0_0_8_5       ,		beta_end=0.0_1_2       ,		beta_schedule='''scaled_linear'''       ,		clip_sample=SCREAMING_SNAKE_CASE_       ,		set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,		)
													torch.manual_seed(0     )
													__UpperCamelCase       					=     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     )
													__UpperCamelCase       					=     CLIPTextConfig(
													    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=1000       ,		hidden_act='''gelu'''       ,		projection_dim=512       ,		)
													__UpperCamelCase       					=     CLIPTextModel(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

													__UpperCamelCase       					=     {
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''vae''': vae,
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''safety_checker''': None,
													    '''feature_extractor''': None,
													}
													return components



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=0     )->				List[Any]:





													'''simple docstring'''






													if str(SCREAMING_SNAKE_CASE_     ).startswith('''mps'''     ):
																			__UpperCamelCase       					=     torch.manual_seed(SCREAMING_SNAKE_CASE_     )
													else:
																			__UpperCamelCase       					=     torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {
													    '''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       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

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

													__UpperCamelCase       					=     np.array([0.4_7_7_3, 0.5_4_1_7, 0.4_7_2_3, 0.4_9_2_5, 0.5_6_3_1, 0.4_7_5_2, 0.5_2_4_0, 0.4_9_3_5, 0.5_0_2_3]     )

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



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





													'''simple docstring'''






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

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

													__UpperCamelCase       					=     np.array([0.3_5_7_3, 0.4_4_2_0, 0.4_9_6_0, 0.4_7_9_9, 0.3_7_9_6, 0.3_8_7_9, 0.4_8_1_9, 0.4_3_6_5, 0.4_4_6_8]     )

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



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





													'''simple docstring'''






													super().test_inference_batch_consistent(batch_sizes=[1, 2]     )



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





													'''simple docstring'''






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



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     '''french fries'''
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_       ,		negative_prompt=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     output.images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]

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

													__UpperCamelCase       					=     np.array([0.4_7_7_1, 0.5_4_2_0, 0.4_6_8_3, 0.4_9_1_8, 0.5_6_3_6, 0.4_7_2_5, 0.5_2_3_0, 0.4_9_2_3, 0.5_0_1_5]     )

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






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

							"""simple docstring"""






							def       A__						(					self     )->				Any:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=0     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {
													    '''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       A__						(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     '''stabilityai/stable-diffusion-2-base'''
													__UpperCamelCase       					=     DDIMParallelScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,		subfolder='''scheduler'''     )
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,		scheduler=SCREAMING_SNAKE_CASE_       ,		safety_checker=SCREAMING_SNAKE_CASE_     )
													pipe.to(SCREAMING_SNAKE_CASE_     )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )
													pipe.enable_attention_slicing()

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

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

													__UpperCamelCase       					=     np.array([0.9_6_2_2, 0.9_6_0_2, 0.9_7_4_8, 0.9_5_9_1, 0.9_6_3_0, 0.9_6_9_1, 0.9_6_6_1, 0.9_6_3_1, 0.9_7_4_1]     )

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




