



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase__					  =  {
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GPTNeoXJapaneseForCausalLM",
				    "GPTNeoXJapaneseLayer",
				    "GPTNeoXJapaneseModel",
				    "GPTNeoXJapanesePreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
				from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gpt_neox_japanese import (
								    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTNeoXJapaneseForCausalLM,
								    GPTNeoXJapaneseLayer,
								    GPTNeoXJapaneseModel,
								    GPTNeoXJapanesePreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@property
					def 			snake_case					(							self :     Any					):
									torch.manual_seed(0					)
									lowercase__					:   Tuple													=					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 			snake_case					(							self :     List[str]					):
									torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					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 			snake_case					(							self :     Dict					):
									torch.manual_seed(0					)
									lowercase__					:   str													=					CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-0_5				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_000				,       )
									return CLIPTextModel(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   Any													=					self.dummy_uncond_unet
									lowercase__					:   Dict													=					DDIMScheduler()
									lowercase__					:   Optional[Any]													=					self.dummy_vq_model

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

									lowercase__					:   int													=					torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"					).images

									lowercase__					:   str													=					torch.manual_seed(0					)
									lowercase__					:   List[Any]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"				,       return_dict=SCREAMING_SNAKE_CASE					)[0]

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

									assert image.shape == (1, 64, 64, 3)
									lowercase__					:   List[Any]													=					np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]					)
									lowercase__					:   Optional[Any]													=					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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					torch.manual_seed(0					)
									lowercase__					:   Tuple													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=5				,       output_type="numpy"					).images

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

									assert image.shape == (1, 256, 256, 3)
									lowercase__					:   Optional[Any]													=					np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]					)
									lowercase__					:   int													=					1E-2 if torch_device != "mps" else 3E-2

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




