


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      ):
		@property
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									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						)			->	List[str]:




									"""simple docstring"""





									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						)			->	Dict:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase					=      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=1000      ,   )
									return CLIPTextModel(_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.dummy_uncond_unet
									_UpperCAmelCase					=      DDIMScheduler()
									_UpperCAmelCase					=      self.dummy_vq_model

									_UpperCAmelCase					=      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						)

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

									_UpperCAmelCase					=      torch.manual_seed(0						)
									_UpperCAmelCase					=      ldm(generator=_SCREAMING_SNAKE_CASE      ,   num_inference_steps=2      ,   output_type='numpy'      ,   return_dict=_SCREAMING_SNAKE_CASE						)[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      ):
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'						)
									ldm.to(_SCREAMING_SNAKE_CASE						)
									ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

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

									assert image.shape == (1, 256, 256, 3)
									_UpperCAmelCase					=      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]						)
									_UpperCAmelCase					=      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_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

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