



"""simple docstring"""


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						@property
						def      UpperCAmelCase__					( self			:int )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			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






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			VQModel(
												    block_out_channels=[32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=3       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      int:
												torch.manual_seed(0 )
												UpperCAmelCase					=			CLIPTextConfig(
												    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=10_00       ,  )
												return CLIPTextModel(lowercase_ )






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[Any]:
												UpperCAmelCase					=			self.dummy_uncond_unet
												UpperCAmelCase					=			DDIMScheduler()
												UpperCAmelCase					=			self.dummy_vq_model

												UpperCAmelCase					=			LDMPipeline(unet=lowercase_       ,  vqvae=lowercase_       ,  scheduler=lowercase_ )
												ldm.to(lowercase_ )
												ldm.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			ldm(generator=lowercase_       ,  num_inference_steps=2       ,  output_type='numpy' ).images

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			ldm(generator=lowercase_       ,  num_inference_steps=2       ,  output_type='numpy'       ,  return_dict=lowercase_ )[0]

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

												assert image.shape == (1, 64, 64, 3)
												UpperCAmelCase					=			np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
												UpperCAmelCase					=			1E-2 if torch_device != 'mps' else 3E-2

												assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance







@slow
@require_torch
class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:int )    ->      Tuple:
												UpperCAmelCase					=			LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
												ldm.to(lowercase_ )
												ldm.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			ldm(generator=lowercase_       ,  num_inference_steps=5       ,  output_type='numpy' ).images

												UpperCAmelCase					=			image[0, -3:, -3:, -1]

												assert image.shape == (1, 2_56, 2_56, 3)
												UpperCAmelCase					=			np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
												UpperCAmelCase					=			1E-2 if torch_device != 'mps' else 3E-2

												assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance










"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase					=			pt_tensor.transpose(2		,     3		,     1		,     0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase					=			flax_model.init_weights(PRNGKey(lowercase_						)						)

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





