



import argparse
from collections import defaultdict

import yaml


a_						:     Dict	     =		'docs/source/en/_toctree.yml'

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		defaultdict(UpperCAmelCase__ )
      for doc in model_doc:
            counts[doc["local"]] += 1
      lowerCamelCase								=		[key for key, value in counts.items() if value > 1]

      lowerCamelCase								=		[]
      for duplicate_key in duplicates:
            lowerCamelCase								=		list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key} )
            if len(UpperCAmelCase__ ) > 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 model_doc if counts[doc["local"]] == 1] )

      # Sort
      return sorted(UpperCAmelCase__   ,   key=lambda UpperCAmelCase__ : s["title"].lower() )

def     __lowercase(	UpperCAmelCase__=False ):


      """simple docstring"""



      with open(UpperCAmelCase__   ,   encoding="utf-8" ) as f:
            lowerCamelCase								=		yaml.safe_load(f.read() )

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

      # Then to the model doc
      lowerCamelCase								=		0
      while api_doc[model_idx]["title"] != "Models":
            model_idx += 1

      lowerCamelCase								=		api_doc[model_idx]["sections"]

      lowerCamelCase								=		[(idx, section) for idx, section in enumerate(UpperCAmelCase__ ) if "sections" in section]
      lowerCamelCase								=		False
      for idx, modality_doc in modalities_docs:
            lowerCamelCase								=		modality_doc["sections"]
            lowerCamelCase								=		clean_model_doc_toc(UpperCAmelCase__ )

            if old_modality_doc != new_modality_doc:
                  lowerCamelCase								=		True
                  if overwrite:
                        lowerCamelCase								=		new_modality_doc

      if diff:
            if overwrite:
                  lowerCamelCase								=		model_doc
                  lowerCamelCase								=		api_doc
                  with open(UpperCAmelCase__   ,   "w"   ,   encoding="utf-8" ) as f:
                        f.write(yaml.dump(UpperCAmelCase__   ,   allow_unicode=UpperCAmelCase__ ) )
            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__":
     a_						:     Tuple	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     List[Any]	     =		parser.parse_args()

     check_model_doc(args.fix_and_overwrite)




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

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





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

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