



"""simple docstring"""






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_lowercase			:			List[Any]										=						datasets.utils.logging.get_logger(__name__)

@dataclass
class 	__SCREAMING_SNAKE_CASE     (				datasets.BuilderConfig       ):


					'''simple docstring'''





					_a             =							1_0_0_0_0
					_a             =							None
					_a             =							None





class 	__SCREAMING_SNAKE_CASE     (				datasets.ArrowBasedBuilder       ):


					'''simple docstring'''





					_a             =							ParquetConfig



					def 		snake_case	(	self							:  int					)-> Tuple:
								return datasets.DatasetInfo(features=self.config.features					)



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  List[Any]					)-> int:






								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}'''					)
								lowerCamelCase__		:		Tuple		     =dl_manager.download_and_extract(self.config.data_files					)
								if isinstance(_a,  (str, list, tuple)					):
											lowerCamelCase__		:		int		     =data_files
											if isinstance(_a,  _a					):
														lowerCamelCase__		:		Tuple		     =[files]
											# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
											lowerCamelCase__		:		int		     =[dl_manager.iter_files(_a					) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN,  gen_kwargs={'''files''': files}					)]
								lowerCamelCase__		:		Dict		     =[]
								for split_name, files in data_files.items():
											if isinstance(_a,  _a					):
														lowerCamelCase__		:		Any		     =[files]
											# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
											lowerCamelCase__		:		Optional[int]		     =[dl_manager.iter_files(_a					) 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(_a					):
																	with open(_a,  '''rb'''					) as f:
																				lowerCamelCase__		:		Dict		     =datasets.Features.from_arrow_schema(pq.read_schema(_a					)					)
																	break
											splits.append(datasets.SplitGenerator(name=_a,  gen_kwargs={'''files''': files}					)					)
								return splits



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  pa.Table					)-> List[Any]:






								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
											lowerCamelCase__		:		List[str]		     =table_cast(_a,  self.info.features.arrow_schema					)
								return pa_table




					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  Optional[int]					)-> Dict:
								lowerCamelCase__		:		Optional[int]		     =self.info.features.arrow_schema if self.info.features is not None else None
								if self.info.features is not None and self.config.columns is not None:
											if sorted(field.name for field in schema					) != sorted(self.config.columns					):
														raise ValueError(
														    F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''					)
								for file_idx, file in enumerate(itertools.chain.from_iterable(_a					)					):
											with open(_a,  '''rb'''					) as f:
														lowerCamelCase__		:		int		     =pq.ParquetFile(_a					)
														try:
																	for batch_idx, record_batch in enumerate(
																	    parquet_file.iter_batches(batch_size=self.config.batch_size,  columns=self.config.columns					)					):
																				lowerCamelCase__		:		Tuple		     =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(_a					)
														except ValueError as e:
																	logger.error(F'''Failed to read file \'{file}\' with error {type(_a					)}: {e}'''					)
																	raise









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





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


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

								lowerCamelCase__		:		Dict		     =image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]					)

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




