






import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__      :			Optional[int]							=		"""docs/source/en/_toctree.yml"""

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      defaultdict(snake_case )
				__magic_name__		:Optional[int]        =      []
				__magic_name__		:int        =      []
				for doc in doc_list:
								if "local" in doc:
												counts[doc["local"]] += 1

								if doc["title"].lower() == "overview":
												overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']} )
								else:
												new_doc_list.append(snake_case )

				__magic_name__		:int        =      new_doc_list
				__magic_name__		:int        =      [key for key, value in counts.items() if value > 1]

				__magic_name__		:str        =      []
				for duplicate_key in duplicates:
								__magic_name__		:Any        =      list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key} )
								if len(snake_case ) > 1:
												raise ValueError(
												    f'''{duplicate_key} is present several times in the documentation table of content at '''
												    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
												    '''others.''' )
								# Only add this once
								new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )

				# Add none duplicate-keys
				new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1] )
				__magic_name__		:Dict        =      sorted(snake_case,  key=lambda snake_case : s["title"].lower() )

				# "overview" gets special treatment and is always first
				if len(snake_case ) > 1:
								raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.''' )

				overview_doc.extend(snake_case )

				# Sort
				return overview_doc

def      __lowercase					( snake_case=False ):







				"""simple docstring"""






				with open(snake_case,  encoding='''utf-8''' ) as f:
								__magic_name__		:Dict        =      yaml.safe_load(f.read() )

				# Get to the API doc
				__magic_name__		:Any        =      0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__magic_name__		:List[Any]        =      content[api_idx]['''sections''']

				# Then to the model doc
				__magic_name__		:int        =      0
				while api_doc[scheduler_idx]["title"] != "Schedulers":
								scheduler_idx += 1

				__magic_name__		:Union[str, Any]        =      api_doc[scheduler_idx]['''sections''']
				__magic_name__		:str        =      clean_doc_toc(snake_case )

				__magic_name__		:Optional[Any]        =      False
				if new_scheduler_doc != scheduler_doc:
								__magic_name__		:Optional[Any]        =      True
								if overwrite:
												__magic_name__		:Tuple        =      new_scheduler_doc

				if diff:
								if overwrite:
												__magic_name__		:List[Any]        =      api_doc
												with open(snake_case,  '''w''',  encoding='''utf-8''' ) as f:
																f.write(yaml.dump(snake_case,  allow_unicode=snake_case ) )
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )

def      __lowercase					( snake_case=False ):







				"""simple docstring"""






				with open(snake_case,  encoding='''utf-8''' ) as f:
								__magic_name__		:Any        =      yaml.safe_load(f.read() )

				# Get to the API doc
				__magic_name__		:int        =      0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__magic_name__		:Union[str, Any]        =      content[api_idx]['''sections''']

				# Then to the model doc
				__magic_name__		:Union[str, Any]        =      0
				while api_doc[pipeline_idx]["title"] != "Pipelines":
								pipeline_idx += 1

				__magic_name__		:List[Any]        =      False
				__magic_name__		:Dict        =      api_doc[pipeline_idx]['''sections''']
				__magic_name__		:Any        =      []

				# sort sub pipeline docs
				for pipeline_doc in pipeline_docs:
								if "section" in pipeline_doc:
												__magic_name__		:Optional[Any]        =      pipeline_doc['''section''']
												__magic_name__		:Any        =      clean_doc_toc(snake_case )
												if overwrite:
																__magic_name__		:Optional[Any]        =      new_sub_pipeline_doc
								new_pipeline_docs.append(snake_case )

				# sort overall pipeline doc
				__magic_name__		:Tuple        =      clean_doc_toc(snake_case )

				if new_pipeline_docs != pipeline_docs:
								__magic_name__		:Dict        =      True
								if overwrite:
												__magic_name__		:int        =      new_pipeline_docs

				if diff:
								if overwrite:
												__magic_name__		:Optional[int]        =      api_doc
												with open(snake_case,  '''w''',  encoding='''utf-8''' ) as f:
																f.write(yaml.dump(snake_case,  allow_unicode=snake_case ) )
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		parser.parse_args()

					check_scheduler_doc(args.fix_and_overwrite)
					check_pipeline_doc(args.fix_and_overwrite)








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)))






