






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						:    Union[str, Any]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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=1_000,  )
								return CLIPTextModel(lowerCamelCase  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.dummy_uncond_unet
								lowercase__    =			DDIMScheduler()
								lowercase__    =			self.dummy_vq_model

								lowercase__    =			LDMPipeline(unet=lowerCamelCase,  vqvae=lowerCamelCase,  scheduler=lowerCamelCase  )
								ldm.to(lowerCamelCase  )
								ldm.set_progress_bar_config(disable=lowerCamelCase  )

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

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

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

								assert image.shape == (1, 64, 64, 3)
								lowercase__    =			np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]  )
								lowercase__    =			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						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''  )
								ldm.to(lowerCamelCase  )
								ldm.set_progress_bar_config(disable=lowerCamelCase  )

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

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

								assert image.shape == (1, 256, 256, 3)
								lowercase__    =			np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]  )
								lowercase__    =			1E-2 if torch_device != '''mps''' else 3E-2

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__	:			Optional[int]			   = {}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Optional[int]			   = ['GPTSw3Tokenizer']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_swa import GPTSwaTokenizer

else:
							import sys

							A__	:			Optional[int]			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




