
'''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 							UpperCAmelCase_							(    A					,				A					,				A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    List[Any]				=							StableUnCLIPPipeline
 lowercase_					:    Any				=							TEXT_TO_IMAGE_PARAMS
 lowercase_					:    Union[str, Any]				=							TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_					:    str				=							TEXT_TO_IMAGE_IMAGE_PARAMS
 lowercase_					:    Union[str, Any]				=							TEXT_TO_IMAGE_IMAGE_PARAMS

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







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							32
   UpperCAmelCase__      :					Tuple				       =							embedder_hidden_size

   # prior components

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

   torch.manual_seed(0 )
   UpperCAmelCase__      :					str				       =							CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0			,				eos_token_id=2			,				hidden_size=snake_case__			,				projection_dim=snake_case__			,				intermediate_size=37			,				layer_norm_eps=1e-05			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=10_00			,				) )

   torch.manual_seed(0 )
   UpperCAmelCase__      :					int				       =							PriorTransformer(
       num_attention_heads=2			,				attention_head_dim=12			,				embedding_dim=snake_case__			,				num_layers=1			,				)

   torch.manual_seed(0 )
   UpperCAmelCase__      :					Any				       =							DDPMScheduler(
       variance_type="fixed_small_log"			,				prediction_type="sample"			,				num_train_timesteps=10_00			,				clip_sample=snake_case__			,				clip_sample_range=5.0			,				beta_schedule="squaredcos_cap_v2"			,				)

   # regular denoising components

   torch.manual_seed(0 )
   UpperCAmelCase__      :					Optional[Any]				       =							StableUnCLIPImageNormalizer(embedding_dim=snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							DDPMScheduler(beta_schedule="squaredcos_cap_v2" )

   torch.manual_seed(0 )
   UpperCAmelCase__      :					List[str]				       =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

   torch.manual_seed(0 )
   UpperCAmelCase__      :					Optional[int]				       =							CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0			,				eos_token_id=2			,				hidden_size=snake_case__			,				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=10_00			,				) )

   torch.manual_seed(0 )
   UpperCAmelCase__      :					str				       =							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=snake_case__			,				layers_per_block=1			,				upcast_attention=snake_case__			,				use_linear_projection=snake_case__			,				)

   torch.manual_seed(0 )
   UpperCAmelCase__      :					Tuple				       =							DDIMScheduler(
       beta_schedule="scaled_linear"			,				beta_start=0.00085			,				beta_end=0.012			,				prediction_type="v_prediction"			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				)

   torch.manual_seed(0 )
   UpperCAmelCase__      :					Optional[int]				       =							AutoencoderKL()

   UpperCAmelCase__      :					Optional[int]				       =							{
       # 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 							UpperCamelCase  (					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Dict=0 ):



   '''simple docstring'''




   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Any				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					List[Any]				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							{
       "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 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							torch_device == "cpu"

   self._test_attention_slicing_forward_pass(test_max_difference=snake_case__ )







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							torch_device in ["cpu", "mps"]

   self._test_inference_batch_single_identical(test_max_difference=snake_case__ )






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

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




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

   UpperCAmelCase__      :					List[Any]				       =							StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,				torch_dtype=torch.floataa )
   pipe.to(snake_case__ )
   pipe.set_progress_bar_config(disable=snake_case__ )
   # 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()

   UpperCAmelCase__      :					str				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__      :					int				       =							pipe("anime turle"			,				generator=snake_case__			,				output_type="np" )

   UpperCAmelCase__      :					List[Any]				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   UpperCAmelCase__      :					List[Any]				       =							StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[Any]				       =							pipe.to(snake_case__ )
   pipe.set_progress_bar_config(disable=snake_case__ )
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   UpperCAmelCase__      :					Any				       =							pipe(
       "anime turtle"			,				prior_num_inference_steps=2			,				num_inference_steps=2			,				output_type="np"			,				)

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9





'''simple docstring'''





import operator as op







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[Any]				       =							[]
  UpperCAmelCase__      :					Any				       =							lambda lowercase__   ,       lowercase__		: int(x / y		)  # noqa: E731 integer division operation
  UpperCAmelCase__      :					Any				       =							{
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8		)   ,       "Action".center(1_2		)   ,       "Stack"   ,       sep=" | "		)
  print("-" * (3_0 + len(lowercase__		))		)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowercase__		)  # append x to stack
      # output in tabular format
      print(x.rjust(8		)   ,       ("push(" + x + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)
    else:
      UpperCAmelCase__      :					str				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      UpperCAmelCase__      :					List[Any]				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + a + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      stack.append(
          str(opr[x](int(lowercase__		)   ,       int(lowercase__		)		)		)		)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8		)   ,       ("push(" + a + x + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "   ,       )

  return int(stack[0]		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
      print("""\n\tResult = """, solve(Postfix))



