






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


SCREAMING_SNAKE_CASE__      :			List[str]							=		datasets.utils.logging.get_logger(__name__)
@dataclass
class 						lowerCamelCase_				(      datasets.BuilderConfig ):
				a__					       =  1_00_00
				a__					       =  None
				a__					       =  None







class 						lowerCamelCase_				(      datasets.ArrowBasedBuilder ):
				a__					       =  ParquetConfig







				def 			A	(						self     ):
								"""simple docstring"""
								return datasets.DatasetInfo(features=self.config.features     )







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







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""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
												__magic_name__		:Optional[int]        =      table_cast(__lowerCAmelCase       ,					self.info.features.arrow_schema     )
								return pa_table







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      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(__lowerCAmelCase     )     ):
												with open(__lowerCAmelCase       ,					'''rb'''     ) as f:
																__magic_name__		:Union[str, Any]        =      pq.ParquetFile(__lowerCAmelCase     )
																try:
																				for batch_idx, record_batch in enumerate(
																				    parquet_file.iter_batches(batch_size=self.config.batch_size       ,					columns=self.config.columns     )     ):
																								__magic_name__		:List[str]        =      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(__lowerCAmelCase     )
																except ValueError as e:
																				logger.error(F'''Failed to read file \'{file}\' with error {type(__lowerCAmelCase     )}: {e}'''     )
																				raise








import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      argparse.ArgumentParser()
				parser.add_argument(
				    '''-m''',  '''--pretrained_model_name_or_path''',  type=snake_case,  default=snake_case,  required=snake_case,  help='''Path to pretrained model or model identifier from huggingface.co/models.''',  )
				parser.add_argument(
				    '''-c''',  '''--caption''',  type=snake_case,  default='''robotic cat with wings''',  help='''Text used to generate images.''',  )
				parser.add_argument(
				    '''-n''',  '''--images_num''',  type=snake_case,  default=4,  help='''How much images to generate.''',  )
				parser.add_argument(
				    '''-s''',  '''--seed''',  type=snake_case,  default=4_2,  help='''Seed for random process.''',  )
				parser.add_argument(
				    '''-ci''',  '''--cuda_id''',  type=snake_case,  default=0,  help='''cuda_id.''',  )
				__magic_name__		:Optional[Any]        =      parser.parse_args()
				return args

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not len(snake_case ) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.''' )

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

				for i, img in enumerate(snake_case ):
								grid.paste(snake_case,  box=(i % cols * w, i // cols * h) )
				return grid

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      torch.Generator(pipeline.device ).manual_seed(snake_case )
				__magic_name__		:str        =      pipeline(
				    snake_case,  guidance_scale=snake_case,  num_inference_steps=snake_case,  generator=snake_case,  num_images_per_prompt=snake_case,  ).images
				__magic_name__		:Tuple        =      int(math.sqrt(snake_case ) )
				__magic_name__		:Union[str, Any]        =      image_grid(snake_case,  rows=_rows,  cols=num_images_per_prompt // _rows )
				return grid, images


SCREAMING_SNAKE_CASE__      :			Any							=		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__      :			int							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE__      :			str							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE__      :			int							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__      :			str							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					SCREAMING_SNAKE_CASE__      :			str							=		load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					SCREAMING_SNAKE_CASE__      :			Dict							=		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE__      :			Any							=		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
SCREAMING_SNAKE_CASE__      :			int							=		os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
					image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))






