





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
SCREAMING_SNAKE_CASE_						   =       object()

# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE_						   =       object()



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						tuple((re.compile(x + """$"""	) for x in qs)	)
      for i in range(len(_SCREAMING_SNAKE_CASE	) - len(_SCREAMING_SNAKE_CASE	) + 1	):
            SCREAMING_SNAKE_CASE		=						[x.match(_SCREAMING_SNAKE_CASE	) for x, y in zip(_SCREAMING_SNAKE_CASE ,						ks[i:]	)]
            if matches and all(_SCREAMING_SNAKE_CASE	):
                  return True
      return False



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Optional[Any]:


      '''simple docstring'''


      def replace(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
            for rule, replacement in rules:
                  if _match(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
                        return replacement
            return val

      return replace



def    __lowercase   (      ) ->   Optional[Any]:


      '''simple docstring'''


      return [
          # embeddings
          (("transformer", "wpe", "embedding"), P("""mp""" ,						_SCREAMING_SNAKE_CASE	)),
          (("transformer", "wte", "embedding"), P("""mp""" ,						_SCREAMING_SNAKE_CASE	)),
          # atention
          (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_SCREAMING_SNAKE_CASE ,						"""mp"""	)),
          (("attention", "out_proj", "kernel"), P("""mp""" ,						_SCREAMING_SNAKE_CASE	)),
          (("attention", "out_proj", "bias"), None),
          # mlp
          (("mlp", "c_fc", "kernel"), P(_SCREAMING_SNAKE_CASE ,						"""mp"""	)),
          (("mlp", "c_fc", "bias"), P("""mp"""	)),
          (("mlp", "c_proj", "kernel"), P("""mp""" ,						_SCREAMING_SNAKE_CASE	)),
          (("mlp", "c_proj", "bias"), None),
          # layer norms
          ((r"ln_\d+", "bias"), None),
          ((r"\d+", r"ln_\d+", "scale"), None),
          (("ln_f", "bias"), None),
          (("ln_f", "scale"), None),
      ]



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						_get_partition_rules()
      SCREAMING_SNAKE_CASE		=						_replacement_rules(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						{k: _unmatched for k in flatten_dict(_SCREAMING_SNAKE_CASE	)}
      SCREAMING_SNAKE_CASE		=						{k: replace(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) for k, v in initd.items()}
      assert _unmatched not in result.values(), "Incomplete partition spec."
      return freeze(unflatten_dict(_SCREAMING_SNAKE_CASE	)	)









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 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

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

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

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						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 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						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


