





'''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  lowerCAmelCase_		(		unittest.TestCase		):




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   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		)     ->				Optional[int]:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   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		)     ->				Any:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   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=1000      ,  )
     return CLIPTextModel(_lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.dummy_uncond_unet
     _lowerCAmelCase						      =   DDIMScheduler()
     _lowerCAmelCase						      =   self.dummy_vq_model

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

     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   ldm(generator=_lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="numpy"		).images

     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   ldm(generator=_lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="numpy"      ,  return_dict=_lowerCAmelCase		)[0]

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

     assert image.shape == (1, 64, 64, 3)
     _lowerCAmelCase						      =   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]		)
     _lowerCAmelCase						      =   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  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"		)
     ldm.to(_lowerCAmelCase		)
     ldm.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   ldm(generator=_lowerCAmelCase      ,  num_inference_steps=5      ,  output_type="numpy"		).images

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

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


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)