


'''simple docstring'''







import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class    lowercase_	(       a__    ,		unittest.TestCase  ):
    __UpperCAmelCase       =      UnCLIPPipeline
    __UpperCAmelCase       =      TEXT_TO_IMAGE_PARAMS - {
        'negative_prompt',
        'height',
        'width',
        'negative_prompt_embeds',
        'guidance_scale',
        'prompt_embeds',
        'cross_attention_kwargs',
    }
    __UpperCAmelCase       =      TEXT_TO_IMAGE_BATCH_PARAMS
    __UpperCAmelCase       =      [
        'generator',
        'return_dict',
        'prior_num_inference_steps',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    __UpperCAmelCase       =      False


    @property
    def   __a				(	self       ):
          return 32


    @property
    def   __a				(	self       ):
          return 32


    @property
    def   __a				(	self       ):
          return self.time_input_dim


    @property
    def   __a				(	self       ):
          return self.time_input_dim * 4


    @property
    def   __a				(	self       ):
          return 1_00


    @property
    def   __a				(	self       ):
          UpperCamelCase__      =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
          return tokenizer


    @property
    def   __a				(	self       ):
          torch.manual_seed(0       )
          UpperCamelCase__      =							CLIPTextConfig(
              bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=self.text_embedder_hidden_size    ,   projection_dim=self.text_embedder_hidden_size    ,   intermediate_size=37    ,   layer_norm_eps=1e-05    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   )
          return CLIPTextModelWithProjection(a       )


    @property
    def   __a				(	self       ):
          torch.manual_seed(0       )

          UpperCamelCase__      =							{
              "num_attention_heads": 2,
              "attention_head_dim": 12,
              "embedding_dim": self.text_embedder_hidden_size,
              "num_layers": 1,
          }

          UpperCamelCase__      =							PriorTransformer(**a       )
          return model


    @property
    def   __a				(	self       ):
          torch.manual_seed(0       )

          UpperCamelCase__      =							{
              "clip_embeddings_dim": self.text_embedder_hidden_size,
              "time_embed_dim": self.time_embed_dim,
              "cross_attention_dim": self.cross_attention_dim,
          }

          UpperCamelCase__      =							UnCLIPTextProjModel(**a       )
          return model


    @property
    def   __a				(	self       ):
          torch.manual_seed(0       )

          UpperCamelCase__      =							{
              "sample_size": 32,
              # RGB in channels
              "in_channels": 3,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 6,
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": "identity",
          }

          UpperCamelCase__      =							UNetaDConditionModel(**a       )
          return model


    @property
    def   __a				(	self       ):
          return {
              "sample_size": 64,
              "layers_per_block": 1,
              "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
              "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "in_channels": 6,
              "out_channels": 3,
          }


    @property
    def   __a				(	self       ):
          torch.manual_seed(0       )

          UpperCamelCase__      =							UNetaDModel(**self.dummy_super_res_kwargs       )
          return model


    @property
    def   __a				(	self       ):
          # seeded differently to get different unet than `self.dummy_super_res_first`
          torch.manual_seed(1       )

          UpperCamelCase__      =							UNetaDModel(**self.dummy_super_res_kwargs       )
          return model


    def   __a				(	self       ):
          UpperCamelCase__      =							self.dummy_prior
          UpperCamelCase__      =							self.dummy_decoder
          UpperCamelCase__      =							self.dummy_text_proj
          UpperCamelCase__      =							self.dummy_text_encoder
          UpperCamelCase__      =							self.dummy_tokenizer
          UpperCamelCase__      =							self.dummy_super_res_first
          UpperCamelCase__      =							self.dummy_super_res_last

          UpperCamelCase__      =							UnCLIPScheduler(
              variance_type="fixed_small_log"    ,   prediction_type="sample"    ,   num_train_timesteps=10_00    ,   clip_sample_range=5.0    ,   )

          UpperCamelCase__      =							UnCLIPScheduler(
              variance_type="learned_range"    ,   prediction_type="epsilon"    ,   num_train_timesteps=10_00    ,   )

          UpperCamelCase__      =							UnCLIPScheduler(
              variance_type="fixed_small_log"    ,   prediction_type="epsilon"    ,   num_train_timesteps=10_00    ,   )

          UpperCamelCase__      =							{
              "prior": prior,
              "decoder": decoder,
              "text_proj": text_proj,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "super_res_first": super_res_first,
              "super_res_last": super_res_last,
              "prior_scheduler": prior_scheduler,
              "decoder_scheduler": decoder_scheduler,
              "super_res_scheduler": super_res_scheduler,
          }

          return components


    def   __a				(	self    ,   a    ,   a=0       ):
          if str(a       ).startswith("mps"       ):
                UpperCamelCase__      =							torch.manual_seed(a       )
          else:
                UpperCamelCase__      =							torch.Generator(device=a       ).manual_seed(a       )
          UpperCamelCase__      =							{
              "prompt": "horse",
              "generator": generator,
              "prior_num_inference_steps": 2,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs


    def   __a				(	self       ):
          UpperCamelCase__      =							"cpu"

          UpperCamelCase__      =							self.get_dummy_components()

          UpperCamelCase__      =							self.pipeline_class(**a       )
          UpperCamelCase__      =							pipe.to(a       )

          pipe.set_progress_bar_config(disable=a       )

          UpperCamelCase__      =							pipe(**self.get_dummy_inputs(a       )       )
          UpperCamelCase__      =							output.images

          UpperCamelCase__      =							pipe(
              **self.get_dummy_inputs(a       )    ,   return_dict=a    ,   )[0]

          UpperCamelCase__      =							image[0, -3:, -3:, -1]
          UpperCamelCase__      =							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCamelCase__      =							np.array(
              [
                  0.9997,
                  0.9988,
                  0.0028,
                  0.9997,
                  0.9984,
                  0.9965,
                  0.0029,
                  0.9986,
                  0.0025,
              ]       )

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


    def   __a				(	self       ):
          UpperCamelCase__      =							torch.device("cpu"       )
          class    lowercase_	:
              __UpperCAmelCase       =      1

          UpperCamelCase__      =							self.get_dummy_components()

          UpperCamelCase__      =							self.pipeline_class(**a       )
          UpperCamelCase__      =							pipe.to(a       )

          UpperCamelCase__      =							components["prior"]
          UpperCamelCase__      =							components["decoder"]
          UpperCamelCase__      =							components["super_res_first"]
          UpperCamelCase__      =							components["tokenizer"]
          UpperCamelCase__      =							components["text_encoder"]

          UpperCamelCase__      =							torch.Generator(device=a       ).manual_seed(0       )
          UpperCamelCase__      =							prior.dtype
          UpperCamelCase__      =							1

          UpperCamelCase__      =							(batch_size, prior.config.embedding_dim)
          UpperCamelCase__      =							pipe.prepare_latents(
              a    ,   dtype=a    ,   device=a    ,   generator=a    ,   latents=a    ,   scheduler=DummyScheduler()       )
          UpperCamelCase__      =							(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
          UpperCamelCase__      =							pipe.prepare_latents(
              a    ,   dtype=a    ,   device=a    ,   generator=a    ,   latents=a    ,   scheduler=DummyScheduler()       )

          UpperCamelCase__      =							(
              batch_size,
              super_res_first.config.in_channels // 2,
              super_res_first.config.sample_size,
              super_res_first.config.sample_size,
          )
          UpperCamelCase__      =							pipe.prepare_latents(
              a    ,   dtype=a    ,   device=a    ,   generator=a    ,   latents=a    ,   scheduler=DummyScheduler()       )

          pipe.set_progress_bar_config(disable=a       )

          UpperCamelCase__      =							"this is a prompt example"

          UpperCamelCase__      =							torch.Generator(device=a       ).manual_seed(0       )
          UpperCamelCase__      =							pipe(
              [prompt]    ,   generator=a    ,   prior_num_inference_steps=2    ,   decoder_num_inference_steps=2    ,   super_res_num_inference_steps=2    ,   prior_latents=a    ,   decoder_latents=a    ,   super_res_latents=a    ,   output_type="np"    ,   )
          UpperCamelCase__      =							output.images

          UpperCamelCase__      =							tokenizer(
              a    ,   padding="max_length"    ,   max_length=tokenizer.model_max_length    ,   return_tensors="pt"    ,   )
          UpperCamelCase__      =							text_encoder(text_inputs.input_ids       )
          UpperCamelCase__      =							text_inputs.attention_mask

          UpperCamelCase__      =							torch.Generator(device=a       ).manual_seed(0       )
          UpperCamelCase__      =							pipe(
              generator=a    ,   prior_num_inference_steps=2    ,   decoder_num_inference_steps=2    ,   super_res_num_inference_steps=2    ,   prior_latents=a    ,   decoder_latents=a    ,   super_res_latents=a    ,   text_model_output=a    ,   text_attention_mask=a    ,   output_type="np"    ,   )[0]

          # make sure passing text embeddings manually is identical
          assert np.abs(image - image_from_text       ).max() < 1e-4


    @skip_mps
    def   __a				(	self       ):
          UpperCamelCase__      =							torch_device == "cpu"

          self._test_attention_slicing_forward_pass(test_max_difference=a    ,   expected_max_diff=0.01       )


    @skip_mps
    def   __a				(	self       ):
          UpperCamelCase__      =							torch_device == "cpu"
          UpperCamelCase__      =							True
          UpperCamelCase__      =							[
              "prior_num_inference_steps",
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          self._test_inference_batch_single_identical(
              test_max_difference=a    ,   relax_max_difference=a    ,   additional_params_copy_to_batched_inputs=a    ,   )


    def   __a				(	self       ):
          UpperCamelCase__      =							[
              "prior_num_inference_steps",
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          if torch_device == "mps":
                # TODO: MPS errors with larger batch sizes
                UpperCamelCase__      =							[2, 3]
                self._test_inference_batch_consistent(
                    batch_sizes=a    ,   additional_params_copy_to_batched_inputs=a    ,   )
          else:
                self._test_inference_batch_consistent(
                    additional_params_copy_to_batched_inputs=a       )


    @skip_mps
    def   __a				(	self       ):
          return super().test_dict_tuple_outputs_equivalent()


    @skip_mps
    def   __a				(	self       ):
          return super().test_save_load_local()


    @skip_mps
    def   __a				(	self       ):
          return super().test_save_load_optional_components()

@nightly
class    lowercase_	(       unittest.TestCase  ):


    def   __a				(	self       ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def   __a				(	self       ):
          UpperCamelCase__      =							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_horse_cpu.npy"       )

          UpperCamelCase__      =							UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       )
          pipeline.set_progress_bar_config(disable=a       )

          UpperCamelCase__      =							torch.manual_seed(0       )
          UpperCamelCase__      =							pipeline(
              "horse"    ,   num_images_per_prompt=1    ,   generator=a    ,   output_type="np"    ,   )

          UpperCamelCase__      =							output.images[0]

          assert image.shape == (2_56, 2_56, 3)
          assert np.abs(expected_image - image       ).max() < 1e-1

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


    def   __a				(	self       ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def   __a				(	self       ):
          UpperCamelCase__      =							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_horse_fp16.npy"       )

          UpperCamelCase__      =							UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"    ,   torch_dtype=torch.floataa       )
          UpperCamelCase__      =							pipeline.to(a       )
          pipeline.set_progress_bar_config(disable=a       )

          UpperCamelCase__      =							torch.Generator(device="cpu"       ).manual_seed(0       )
          UpperCamelCase__      =							pipeline(
              "horse"    ,   generator=a    ,   output_type="np"    ,   )

          UpperCamelCase__      =							output.images[0]

          assert image.shape == (2_56, 2_56, 3)

          assert_mean_pixel_difference(a    ,   a       )


    def   __a				(	self       ):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCamelCase__      =							UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"    ,   torch_dtype=torch.floataa       )
          UpperCamelCase__      =							pipe.to(a       )
          pipe.set_progress_bar_config(disable=a       )
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

          UpperCamelCase__      =							pipe(
              "horse"    ,   num_images_per_prompt=1    ,   prior_num_inference_steps=2    ,   decoder_num_inference_steps=2    ,   super_res_num_inference_steps=2    ,   output_type="np"    ,   )

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









'''simple docstring'''







def _UpperCamelCase			(	__A ,     __A       )				->						int:


      '''simple docstring'''



      return (input_a, input_a).count(0       ) % 2







def _UpperCamelCase			(	)				->						None:


      '''simple docstring'''



      assert xor_gate(0 ,     0       ) == 0
      assert xor_gate(0 ,     1       ) == 1
      assert xor_gate(1 ,     0       ) == 1
      assert xor_gate(1 ,     1       ) == 0


if __name__ == "__main__":
  print(xor_gate(0, 0))
  print(xor_gate(0, 1))





