
"""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       __magic_name__							(							unittest.TestCase			):
       @property
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         torch.manual_seed(0							)
         _lowercase:      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 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         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 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         torch.manual_seed(0							)
         _lowercase:      Any   			=  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(A_							)



       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.dummy_uncond_unet
         _lowercase:      List[str]   			=  DDIMScheduler()
         _lowercase:      Union[str, Any]   			=  self.dummy_vq_model

         _lowercase:      str   			=  LDMPipeline(unet=A_			,					vqvae=A_			,					scheduler=A_							)
         ldm.to(A_							)
         ldm.set_progress_bar_config(disable=A_							)

         _lowercase:      Union[str, Any]   			=  torch.manual_seed(0							)
         _lowercase:      Tuple   			=  ldm(generator=A_			,					num_inference_steps=2			,					output_type='''numpy'''							).images

         _lowercase:      Union[str, Any]   			=  torch.manual_seed(0							)
         _lowercase:      Optional[Any]   			=  ldm(generator=A_			,					num_inference_steps=2			,					output_type='''numpy'''			,					return_dict=A_							)[0]

         _lowercase:      Dict   			=  image[0, -3:, -3:, -1]
         _lowercase:      List[str]   			=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         _lowercase:      int   			=  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]							)
         _lowercase:      Tuple   			=  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       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      int   			=  LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''							)
         ldm.to(A_							)
         ldm.set_progress_bar_config(disable=A_							)

         _lowercase:      Union[str, Any]   			=  torch.manual_seed(0							)
         _lowercase:      str   			=  ldm(generator=A_			,					num_inference_steps=5			,					output_type='''numpy'''							).images

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

         assert image.shape == (1, 256, 256, 3)
         _lowercase:      List[str]   			=  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]							)
         _lowercase:      Union[str, 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_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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