



"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, 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  __lowerCAmelCase	(					self		)							->					List[Any]:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Dict											=    UNetaDModel(
             block_out_channels=(3_2, 6_4)       ,	layers_per_block=2       ,	sample_size=3_2       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,	)
         return model





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Any											=    self.dummy_uncond_unet
         _UpperCAmelCase							:  int											=    PNDMScheduler()

         _UpperCAmelCase							:  Tuple											=    PNDMPipeline(unet=A       ,	scheduler=A		)
         pndm.to(A		)
         pndm.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    pndm(generator=A       ,	num_inference_steps=2_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  List[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Any											=    pndm(generator=A       ,	num_inference_steps=2_0       ,	output_type='''numpy'''       ,	return_dict=A		)[0]

         _UpperCAmelCase							:  str											=    image[0, -3:, -3:, -1]
         _UpperCAmelCase							:  List[str]											=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         _UpperCAmelCase							:  Optional[int]											=    np.array([1.0, 1.0, 0.0, 1.0, 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  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  int											=    '''google/ddpm-cifar10-32'''

         _UpperCAmelCase							:  Tuple											=    UNetaDModel.from_pretrained(A		)
         _UpperCAmelCase							:  Any											=    PNDMScheduler()

         _UpperCAmelCase							:  int											=    PNDMPipeline(unet=A       ,	scheduler=A		)
         pndm.to(A		)
         pndm.set_progress_bar_config(disable=A		)
         _UpperCAmelCase							:  List[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pndm(generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Dict											=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

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







"""simple docstring"""
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
_lowerCAmelCase    :str     =					object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase    :str     =					object()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Dict											=    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        _UpperCAmelCase							:  str											=    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__     ,       ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    def replace(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Tuple							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__     ,       UpperCamelCase__							):
                return replacement
        return val

    return replace





def  lowerCamelCase_						():
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("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  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    _get_partition_rules()
    _UpperCAmelCase							:  List[str]											=    _replacement_rules(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    _UpperCAmelCase							:  int											=    {k: replace(UpperCamelCase__     ,       UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)


