
"""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      _UpperCAmelCase				(					unittest.TestCase):
    @property
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						str						=   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 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						List[str]						=   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 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						int						=   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=10_00						,    )
      return CLIPTextModel(snake_case_     )


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   self.dummy_uncond_unet
      _snake_case     :						List[Any]						=   DDIMScheduler()
      _snake_case     :						Union[str, Any]						=   self.dummy_vq_model

      _snake_case     :						Optional[Any]						=   LDMPipeline(unet=snake_case_						,    vqvae=snake_case_						,    scheduler=snake_case_     )
      ldm.to(snake_case_     )
      ldm.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						List[str]						=   torch.manual_seed(0     )
      _snake_case     :						int						=   ldm(generator=snake_case_						,    num_inference_steps=2						,    output_type="numpy"     ).images

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

      _snake_case     :						Dict						=   image[0, -3:, -3:, -1]
      _snake_case     :						int						=   image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      _snake_case     :						str						=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]     )
      _snake_case     :						str						=   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):
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"     )
      ldm.to(snake_case_     )
      ldm.set_progress_bar_config(disable=snake_case_     )

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

      _snake_case     :						Any						=   image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_56, 2_56, 3)
      _snake_case     :						str						=   np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]     )
      _snake_case     :						Optional[Any]						=   1E-2 if torch_device != "mps" else 3E-2

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



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a      :			List[Any]					=							{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""BartphoTokenizer"""]

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

else:
    import sys

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

