
'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ):






   """simple docstring"""

   _a				:	Any			=   [tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:] )







class UpperCAmelCase__	( SCREAMING_SNAKE_CASE__    ,				SCREAMING_SNAKE_CASE__    ,				SCREAMING_SNAKE_CASE__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				str   =					StableDiffusionLatentUpscalePipeline
  __UpperCAmelCase				:				Dict   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      "height",
      "width",
      "cross_attention_kwargs",
      "negative_prompt_embeds",
      "prompt_embeds",
  }
  __UpperCAmelCase				:				int   =					PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
  __UpperCAmelCase				:				Optional[int]   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  __UpperCAmelCase				:				Optional[Any]   =					frozenset(
      []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  __UpperCAmelCase				:				List[Any]   =					frozenset([]	)

  __UpperCAmelCase				:				Tuple   =					True


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	str			=   1
     _a				:	Dict			=   4
     _a				:	Union[str, Any]			=   (16, 16)

     _a				:	List[str]			=   floats_tensor((batch_size, num_channels) + sizes							,rng=random.Random(0       )       ).to(lowerCamelCase__       )
     return image


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	List[Any]			=   UNetaDConditionModel(
         act_fn='gelu'							,attention_head_dim=8							,norm_num_groups=lowerCamelCase__							,block_out_channels=[32, 32, 64, 64]							,time_cond_proj_dim=160							,conv_in_kernel=1							,conv_out_kernel=1							,cross_attention_dim=32							,down_block_types=(
             'KDownBlock2D',
             'KCrossAttnDownBlock2D',
             'KCrossAttnDownBlock2D',
             'KCrossAttnDownBlock2D',
         )							,in_channels=8							,mid_block_type=lowerCamelCase__							,only_cross_attention=lowerCamelCase__							,out_channels=5							,resnet_time_scale_shift='scale_shift'							,time_embedding_type='fourier'							,timestep_post_act='gelu'							,up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')							,)
     _a				:	Union[str, Any]			=   AutoencoderKL(
         block_out_channels=[32, 32, 64, 64]							,in_channels=3							,out_channels=3							,down_block_types=[
             'DownEncoderBlock2D',
             'DownEncoderBlock2D',
             'DownEncoderBlock2D',
             'DownEncoderBlock2D',
         ]							,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']							,latent_channels=4							,)
     _a				:	List[str]			=   EulerDiscreteScheduler(prediction_type='sample'       )
     _a				:	Union[str, 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							,hidden_act='quick_gelu'							,projection_dim=512							,)
     _a				:	int			=   CLIPTextModel(lowerCamelCase__       )
     _a				:	List[Any]			=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

     _a				:	Dict			=   {
         """unet""": model.eval(),
         """vae""": vae.eval(),
         """scheduler""": scheduler,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
     }

     return components


  def   __lowercase		(		self      :     List[str]							,_a      :     Optional[Any]							,_a      :     int=0       ):


     '''simple docstring'''






     if str(lowerCamelCase__       ).startswith('mps'       ):
        _a				:	Union[str, Any]			=   torch.manual_seed(lowerCamelCase__       )
     else:
        _a				:	Dict			=   torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
     _a				:	List[Any]			=   {
         """prompt""": """A painting of a squirrel eating a burger""",
         """image""": self.dummy_image.cpu(),
         """generator""": generator,
         """num_inference_steps""": 2,
         """output_type""": """numpy""",
     }
     return inputs


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Tuple			=   """cpu"""

     _a				:	Any			=   self.get_dummy_components()
     _a				:	str			=   self.pipeline_class(**lowerCamelCase__       )
     pipe.to(lowerCamelCase__       )
     pipe.set_progress_bar_config(disable=lowerCamelCase__       )

     _a				:	List[Any]			=   self.get_dummy_inputs(lowerCamelCase__       )
     _a				:	Any			=   pipe(**lowerCamelCase__       ).images
     _a				:	List[str]			=   image[0, -3:, -3:, -1]

     self.assertEqual(image.shape							,(1, 256, 256, 3)       )
     _a				:	Dict			=   np.array(
         [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]       )
     _a				:	List[str]			=   np.abs(image_slice.flatten() - expected_slice       ).max()
     self.assertLessEqual(lowerCamelCase__							,1E-3       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     super().test_attention_slicing_forward_pass(expected_max_diff=7E-3       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     super().test_cpu_offload_forward_pass(expected_max_diff=3E-3       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     super().test_inference_batch_single_identical(expected_max_diff=7E-3       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3       )


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     super().test_save_load_local(expected_max_difference=3E-3       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     super().test_save_load_optional_components(expected_max_difference=3E-3       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   [
         """DDIMScheduler""",
         """DDPMScheduler""",
         """PNDMScheduler""",
         """HeunDiscreteScheduler""",
         """EulerAncestralDiscreteScheduler""",
         """KDPM2DiscreteScheduler""",
         """KDPM2AncestralDiscreteScheduler""",
         """DPMSolverSDEScheduler""",
     ]
     _a				:	int			=   self.get_dummy_components()
     _a				:	Any			=   self.pipeline_class(**lowerCamelCase__       )

     # make sure that PNDM does not need warm-up
     pipe.scheduler.register_to_config(skip_prk_steps=lowerCamelCase__       )

     pipe.to(lowerCamelCase__       )
     pipe.set_progress_bar_config(disable=lowerCamelCase__       )
     _a				:	Union[str, Any]			=   self.get_dummy_inputs(lowerCamelCase__       )
     _a				:	str			=   2

     _a				:	Tuple			=   []
     for scheduler_enum in KarrasDiffusionSchedulers:
        if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

        _a				:	Dict			=   getattr(lowerCamelCase__							,scheduler_enum.name       )
        _a				:	List[str]			=   scheduler_cls.from_config(pipe.scheduler.config       )
        _a				:	Tuple			=   pipe(**lowerCamelCase__       )[0]
        outputs.append(lowerCamelCase__       )

     assert check_same_shape(lowerCamelCase__       )







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





  """simple docstring"""

  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   torch.manual_seed(33       )

     _a				:	int			=   StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'							,torch_dtype=torch.floataa       )
     pipe.to('cuda'       )

     _a				:	str			=   StableDiffusionLatentUpscalePipeline.from_pretrained(
         'stabilityai/sd-x2-latent-upscaler'							,torch_dtype=torch.floataa       )
     upscaler.to('cuda'       )

     _a				:	str			=   """a photo of an astronaut high resolution, unreal engine, ultra realistic"""

     _a				:	Union[str, Any]			=   pipe(lowerCamelCase__							,generator=lowerCamelCase__							,output_type='latent'       ).images

     _a				:	Union[str, Any]			=   upscaler(
         prompt=lowerCamelCase__							,image=lowerCamelCase__							,num_inference_steps=20							,guidance_scale=0							,generator=lowerCamelCase__							,output_type='np'							,).images[0]

     _a				:	Optional[Any]			=   load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'       )
     assert np.abs((expected_image - image).mean()       ) < 5E-2


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	str			=   torch.manual_seed(33       )

     _a				:	Union[str, Any]			=   StableDiffusionLatentUpscalePipeline.from_pretrained(
         'stabilityai/sd-x2-latent-upscaler'							,torch_dtype=torch.floataa       )
     upscaler.to('cuda'       )

     _a				:	List[str]			=   """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

     _a				:	List[Any]			=   load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'       )

     _a				:	Optional[Any]			=   upscaler(
         prompt=lowerCamelCase__							,image=lowerCamelCase__							,num_inference_steps=20							,guidance_scale=0							,generator=lowerCamelCase__							,output_type='np'							,).images[0]

     _a				:	str			=   load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'       )
     assert np.abs((expected_image - image).max()       ) < 5E-2








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:str             =						{
    """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:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """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:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """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

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


