

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 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							@property
							def 				__lowercase				(			self       )    ->    Dict:
												torch.manual_seed(0       )
												_a	:    List[str]         =		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


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												torch.manual_seed(0       )
												_a	:    List[Any]         =		VQModel(
												    block_out_channels=[3_2, 6_4]       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,							latent_channels=3       ,							)
												return model


							@property
							def 				__lowercase				(			self       )    ->    Dict:
												torch.manual_seed(0       )
												_a	:    Optional[Any]         =		CLIPTextConfig(
												    bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=3_2       ,							intermediate_size=3_7       ,							layer_norm_eps=1e-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=1_0_0_0       ,							)
												return CLIPTextModel(_a       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    str         =		self.dummy_uncond_unet
												_a	:    int         =		DDIMScheduler()
												_a	:    List[str]         =		self.dummy_vq_model

												_a	:    Union[str, Any]         =		LDMPipeline(unet=_a       ,							vqvae=_a       ,							scheduler=_a       )
												ldm.to(_a       )
												ldm.set_progress_bar_config(disable=_a       )

												_a	:    Any         =		torch.manual_seed(0       )
												_a	:    Tuple         =		ldm(generator=_a       ,							num_inference_steps=2       ,							output_type='''numpy'''       ).images

												_a	:    Union[str, Any]         =		torch.manual_seed(0       )
												_a	:    Any         =		ldm(generator=_a       ,							num_inference_steps=2       ,							output_type='''numpy'''       ,							return_dict=_a       )[0]

												_a	:    Union[str, Any]         =		image[0, -3:, -3:, -1]
												_a	:    List[str]         =		image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 6_4, 6_4, 3)
												_a	:    Optional[int]         =		np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
												_a	:    Tuple         =		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 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    str:
												_a	:    Optional[int]         =		LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''       )
												ldm.to(_a       )
												ldm.set_progress_bar_config(disable=_a       )

												_a	:    Optional[Any]         =		torch.manual_seed(0       )
												_a	:    Tuple         =		ldm(generator=_a       ,							num_inference_steps=5       ,							output_type='''numpy'''       ).images

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

												assert image.shape == (1, 2_5_6, 2_5_6, 3)
												_a	:    Tuple         =		np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
												_a	:    int         =		1e-2 if torch_device != '''mps''' else 3e-2

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






import operator as op


a__				=		'''scaler.pt'''
a__				=		'''pytorch_model'''
a__				=		'''random_states'''
a__				=		'''optimizer'''
a__				=		'''scheduler'''
a__				=		'''pytorch_model.bin'''
a__				=		'''pytorch_model.bin.index.json'''
a__				=		'''model.safetensors'''
a__				=		'''model.safetensors.index.json'''
a__				=		'''1.10.2'''
a__				=		'''py38'''
a__				=		'''4.17.0'''
a__				=		['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
a__				=		['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
a__				=		['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
a__				=		['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
a__				=		['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
a__				=		'''2.0.1'''
a__				=		['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
a__				=		['''default''', '''reduce-overhead''', '''max-autotune''']

a__				=		{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
a__				=		[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

a__				=		['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
a__				=		['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']



