





'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					=	{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AdaptiveEmbedding',
      'TransfoXLForSequenceClassification',
      'TransfoXLLMHeadModel',
      'TransfoXLModel',
      'TransfoXLPreTrainedModel',
      'load_tf_weights_in_transfo_xl',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFAdaptiveEmbedding',
      'TFTransfoXLForSequenceClassification',
      'TFTransfoXLLMHeadModel',
      'TFTransfoXLMainLayer',
      'TFTransfoXLModel',
      'TFTransfoXLPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

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







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			str													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Any													=np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			int													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			Any													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sd_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=15   , output_type='''np'''   , use_karras_sigmas=__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(
           [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2