




import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible




class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[Any]:
							a    =			[
							    "safety_checker/pytorch_model.bin",
							    "safety_checker/model.safetensors",
							    "vae/diffusion_pytorch_model.bin",
							    "vae/diffusion_pytorch_model.safetensors",
							    "text_encoder/pytorch_model.bin",
							    "text_encoder/model.safetensors",
							    "unet/diffusion_pytorch_model.bin",
							    "unet/diffusion_pytorch_model.safetensors",
							]
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			[
							    "unet/diffusion_pytorch_model.bin",
							    "unet/diffusion_pytorch_model.safetensors",
							]
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							a    =			[
							    "safety_checker/pytorch_model.bin",
							    "safety_checker/model.safetensors",
							    "vae/diffusion_pytorch_model.bin",
							    "vae/diffusion_pytorch_model.safetensors",
							    "text_encoder/pytorch_model.bin",
							    "text_encoder/model.safetensors",
							    "unet/diffusion_pytorch_model.bin",
							    # Removed: 'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     int:
							a    =			[
							    "text_encoder/pytorch_model.bin",
							    "text_encoder/model.safetensors",
							]
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     int:
							a    =			[
							    "safety_checker/pytorch_model.bin",
							    "safety_checker/model.safetensors",
							    "vae/diffusion_pytorch_model.bin",
							    "vae/diffusion_pytorch_model.safetensors",
							    "text_encoder/pytorch_model.bin",
							    # Removed: 'text_encoder/model.safetensors',
							    "unet/diffusion_pytorch_model.bin",
							    "unet/diffusion_pytorch_model.safetensors",
							]
							self.assertFalse(is_safetensors_compatible(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    =			[
							    "safety_checker/pytorch_model.fp16.bin",
							    "safety_checker/model.fp16.safetensors",
							    "vae/diffusion_pytorch_model.fp16.bin",
							    "vae/diffusion_pytorch_model.fp16.safetensors",
							    "text_encoder/pytorch_model.fp16.bin",
							    "text_encoder/model.fp16.safetensors",
							    "unet/diffusion_pytorch_model.fp16.bin",
							    "unet/diffusion_pytorch_model.fp16.safetensors",
							]
							a    =			"fp16"
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Any:
							a    =			[
							    "unet/diffusion_pytorch_model.fp16.bin",
							    "unet/diffusion_pytorch_model.fp16.safetensors",
							]
							a    =			"fp16"
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     int:
							# pass variant but use the non-variant filenames
							a    =			[
							    "unet/diffusion_pytorch_model.bin",
							    "unet/diffusion_pytorch_model.safetensors",
							]
							a    =			"fp16"
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    =			[
							    "safety_checker/pytorch_model.fp16.bin",
							    "safety_checker/model.fp16.safetensors",
							    "vae/diffusion_pytorch_model.fp16.bin",
							    "vae/diffusion_pytorch_model.fp16.safetensors",
							    "text_encoder/pytorch_model.fp16.bin",
							    "text_encoder/model.fp16.safetensors",
							    "unet/diffusion_pytorch_model.fp16.bin",
							    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							a    =			"fp16"
							self.assertFalse(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     List[Any]:
							a    =			[
							    "text_encoder/pytorch_model.fp16.bin",
							    "text_encoder/model.fp16.safetensors",
							]
							a    =			"fp16"
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Tuple:
							# pass variant but use the non-variant filenames
							a    =			[
							    "text_encoder/pytorch_model.bin",
							    "text_encoder/model.safetensors",
							]
							a    =			"fp16"
							self.assertTrue(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[str]:
							a    =			[
							    "safety_checker/pytorch_model.fp16.bin",
							    "safety_checker/model.fp16.safetensors",
							    "vae/diffusion_pytorch_model.fp16.bin",
							    "vae/diffusion_pytorch_model.fp16.safetensors",
							    "text_encoder/pytorch_model.fp16.bin",
							    # 'text_encoder/model.fp16.safetensors',
							    "unet/diffusion_pytorch_model.fp16.bin",
							    "unet/diffusion_pytorch_model.fp16.safetensors",
							]
							a    =			"fp16"
							self.assertFalse(is_safetensors_compatible(__lowerCamelCase					,      variant=__lowerCamelCase							)							)










'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class      snake_case    (    lowercase			,						lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  StableUnCLIPPipeline
  _lowerCamelCase		    =  TEXT_TO_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_BATCH_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  32
   lowerCamelCase_      =  embedder_hidden_size

   # prior components

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=UpperCamelCase			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  PriorTransformer(
       num_attention_heads=2			,  attention_head_dim=12			,  embedding_dim=UpperCamelCase			,  num_layers=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDPMScheduler(
       variance_type="fixed_small_log"			,  prediction_type="sample"			,  num_train_timesteps=1000			,  clip_sample=UpperCamelCase			,  clip_sample_range=5.0			,  beta_schedule="squaredcos_cap_v2"			,  )

   # regular denoising components

   torch.manual_seed(0     )
   lowerCamelCase_      =  StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase     )
   lowerCamelCase_      =  DDPMScheduler(beta_schedule="squaredcos_cap_v2"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=32			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  UNetaDConditionModel(
       sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")			,  up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")			,  block_out_channels=(32, 64)			,  attention_head_dim=(2, 4)			,  class_embed_type="projection"			,  projection_class_embeddings_input_dim=embedder_projection_dim * 2			,  cross_attention_dim=UpperCamelCase			,  layers_per_block=1			,  upcast_attention=UpperCamelCase			,  use_linear_projection=UpperCamelCase			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDIMScheduler(
       beta_schedule="scaled_linear"			,  beta_start=0.00_085			,  beta_end=0.012			,  prediction_type="v_prediction"			,  set_alpha_to_one=UpperCamelCase			,  steps_offset=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  AutoencoderKL()

   lowerCamelCase_      =  {
       # prior components
       "prior_tokenizer": prior_tokenizer,
       "prior_text_encoder": prior_text_encoder,
       "prior": prior,
       "prior_scheduler": prior_scheduler,
       # image noising components
       "image_normalizer": image_normalizer,
       "image_noising_scheduler": image_noising_scheduler,
       # regular denoising components
       "tokenizer": tokenizer,
       "text_encoder": text_encoder,
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
   }

   return components






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(UpperCamelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(UpperCamelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
   lowerCamelCase_      =  {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "prior_num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device == "cpu"

   self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device in ["cpu", "mps"]

   self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase     )





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"     )

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   # stable unclip will oom when integration tests are run on a V100,
   # so turn on memory savings
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  torch.Generator(device="cpu"     ).manual_seed(0     )
   lowerCamelCase_      =  pipe("anime turle"			,  generator=UpperCamelCase			,  output_type="np"     )

   lowerCamelCase_      =  output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(UpperCamelCase			,  UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   lowerCamelCase_      =  pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  pipe(
       "anime turtle"			,  prior_num_inference_steps=2			,  num_inference_steps=2			,  output_type="np"			,  )

   lowerCamelCase_      =  torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9



