
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				UnCLIPImageVariationPipeline
   __lowerCAmelCase            =				IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
   __lowerCAmelCase            =				IMAGE_VARIATION_BATCH_PARAMS

   __lowerCAmelCase            =				[
       """generator""",
       """return_dict""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]
   __lowerCAmelCase            =				False
   @property
   def 			__magic_name__			(			self				):
          return 32
   @property
   def 			__magic_name__			(			self				):
          return 32
   @property
   def 			__magic_name__			(			self				):
          return self.time_input_dim
   @property
   def 			__magic_name__			(			self				):
          return self.time_input_dim * 4
   @property
   def 			__magic_name__			(			self				):
          return 100
   @property
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
          return tokenizer
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)
          lowercase    :				Tuple       		=							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=1_000	,    )
          return CLIPTextModelWithProjection(_a				)
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)
          lowercase    :				Any       		=							CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size	,    projection_dim=self.text_embedder_hidden_size	,    num_hidden_layers=5	,    num_attention_heads=4	,    image_size=32	,    intermediate_size=37	,    patch_size=1	,    )
          return CLIPVisionModelWithProjection(_a				)
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)

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

          lowercase    :				Optional[int]       		=							UnCLIPTextProjModel(**_a				)
          return model
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)

          lowercase    :				int       		=							{
              "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",
          }

          lowercase    :				List[Any]       		=							UNetaDConditionModel(**_a				)
          return model
   @property
   def 			__magic_name__			(			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 			__magic_name__			(			self				):
          torch.manual_seed(0				)

          lowercase    :				Union[str, Any]       		=							UNetaDModel(**self.dummy_super_res_kwargs				)
          return model
   @property
   def 			__magic_name__			(			self				):
          # seeded differently to get different unet than `self.dummy_super_res_first`
          torch.manual_seed(1				)

          lowercase    :				List[Any]       		=							UNetaDModel(**self.dummy_super_res_kwargs				)
          return model
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.dummy_decoder
          lowercase    :				List[str]       		=							self.dummy_text_proj
          lowercase    :				int       		=							self.dummy_text_encoder
          lowercase    :				Dict       		=							self.dummy_tokenizer
          lowercase    :				List[Any]       		=							self.dummy_super_res_first
          lowercase    :				Union[str, Any]       		=							self.dummy_super_res_last

          lowercase    :				str       		=							UnCLIPScheduler(
              variance_type="learned_range"	,    prediction_type="epsilon"	,    num_train_timesteps=1_000	,    )

          lowercase    :				List[str]       		=							UnCLIPScheduler(
              variance_type="fixed_small_log"	,    prediction_type="epsilon"	,    num_train_timesteps=1_000	,    )

          lowercase    :				Tuple       		=							CLIPImageProcessor(crop_size=32	,    size=32				)

          lowercase    :				Dict       		=							self.dummy_image_encoder

          return {
              "decoder": decoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_proj": text_proj,
              "feature_extractor": feature_extractor,
              "image_encoder": image_encoder,
              "super_res_first": super_res_first,
              "super_res_last": super_res_last,
              "decoder_scheduler": decoder_scheduler,
              "super_res_scheduler": super_res_scheduler,
          }
   def 			__magic_name__			(			self	,    _a	,    _a=0	,    _a=True				):
          lowercase    :				Tuple       		=							floats_tensor((1, 3, 32, 32)	,    rng=random.Random(_a				)				).to(_a				)
          if str(_a				).startswith("mps"				):
                 lowercase    :				Dict       		=							torch.manual_seed(_a				)
          else:
                 lowercase    :				Any       		=							torch.Generator(device=_a				).manual_seed(_a				)

          if pil_image:
                 lowercase    :				int       		=							input_image * 0.5 + 0.5
                 lowercase    :				str       		=							input_image.clamp(0	,    1				)
                 lowercase    :				List[str]       		=							input_image.cpu().permute(0	,    2	,    3	,    1				).float().numpy()
                 lowercase    :				str       		=							DiffusionPipeline.numpy_to_pil(_a				)[0]

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							"cpu"

          lowercase    :				int       		=							self.get_dummy_components()

          lowercase    :				str       		=							self.pipeline_class(**_a				)
          lowercase    :				str       		=							pipe.to(_a				)

          pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				Tuple       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)

          lowercase    :				Optional[Any]       		=							pipe(**_a				)
          lowercase    :				int       		=							output.images

          lowercase    :				str       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)

          lowercase    :				List[Any]       		=							pipe(
              **_a	,    return_dict=_a	,    )[0]

          lowercase    :				Union[str, Any]       		=							image[0, -3:, -3:, -1]
          lowercase    :				int       		=							image_from_tuple[0, -3:, -3:, -1]

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

          lowercase    :				Tuple       		=							np.array(
              [
                  0.9_9_9_7,
                  0.0_0_0_2,
                  0.9_9_9_7,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.0_0_2_3,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.9_9_7_0,
              ]				)

          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 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							"cpu"

          lowercase    :				Any       		=							self.get_dummy_components()

          lowercase    :				str       		=							self.pipeline_class(**_a				)
          lowercase    :				Tuple       		=							pipe.to(_a				)

          pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				Dict       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)

          lowercase    :				int       		=							pipe(**_a				)
          lowercase    :				List[Any]       		=							output.images

          lowercase    :				List[Any]       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)

          lowercase    :				Dict       		=							pipe(
              **_a	,    return_dict=_a	,    )[0]

          lowercase    :				Tuple       		=							image[0, -3:, -3:, -1]
          lowercase    :				Optional[int]       		=							image_from_tuple[0, -3:, -3:, -1]

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

          lowercase    :				Any       		=							np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]				)

          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 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							"cpu"

          lowercase    :				int       		=							self.get_dummy_components()

          lowercase    :				int       		=							self.pipeline_class(**_a				)
          lowercase    :				int       		=							pipe.to(_a				)

          pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				Optional[Any]       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)
          lowercase    :				Union[str, Any]       		=							[
              pipeline_inputs["image"],
              pipeline_inputs["image"],
          ]

          lowercase    :				Dict       		=							pipe(**_a				)
          lowercase    :				Optional[int]       		=							output.images

          lowercase    :				Any       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)
          lowercase    :				Optional[Any]       		=							[
              tuple_pipeline_inputs["image"],
              tuple_pipeline_inputs["image"],
          ]

          lowercase    :				List[str]       		=							pipe(
              **_a	,    return_dict=_a	,    )[0]

          lowercase    :				Optional[Any]       		=							image[0, -3:, -3:, -1]
          lowercase    :				Optional[int]       		=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (2, 64, 64, 3)

          lowercase    :				str       		=							np.array(
              [
                  0.9_9_9_7,
                  0.9_9_8_9,
                  0.0_0_0_8,
                  0.0_0_2_1,
                  0.9_9_6_0,
                  0.0_0_1_8,
                  0.0_0_1_4,
                  0.0_0_0_2,
                  0.9_9_3_3,
              ]				)

          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 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							torch.device("cpu"				)

          class 		a__						:
             __lowerCAmelCase            =				1


          lowercase    :				Any       		=							self.get_dummy_components()

          lowercase    :				Union[str, Any]       		=							self.pipeline_class(**_a				)
          lowercase    :				List[Any]       		=							pipe.to(_a				)

          pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				Dict       		=							torch.Generator(device=_a				).manual_seed(0				)
          lowercase    :				Optional[int]       		=							pipe.decoder.dtype
          lowercase    :				Optional[Any]       		=							1

          lowercase    :				Any       		=							(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          lowercase    :				List[str]       		=							pipe.prepare_latents(
              _a	,    dtype=_a	,    device=_a	,    generator=_a	,    latents=_a	,    scheduler=DummyScheduler()				)

          lowercase    :				int       		=							(
              batch_size,
              pipe.super_res_first.config.in_channels // 2,
              pipe.super_res_first.config.sample_size,
              pipe.super_res_first.config.sample_size,
          )
          lowercase    :				List[Any]       		=							pipe.prepare_latents(
              _a	,    dtype=_a	,    device=_a	,    generator=_a	,    latents=_a	,    scheduler=DummyScheduler()				)

          lowercase    :				Dict       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)

          lowercase    :				List[Any]       		=							pipe(
              **_a	,    decoder_latents=_a	,    super_res_latents=_a				).images

          lowercase    :				Union[str, Any]       		=							self.get_dummy_inputs(_a	,    pil_image=_a				)
          # Don't pass image, instead pass embedding
          lowercase    :				Dict       		=							pipeline_inputs.pop("image"				)
          lowercase    :				Dict       		=							pipe.image_encoder(_a				).image_embeds

          lowercase    :				Dict       		=							pipe(
              **_a	,    decoder_latents=_a	,    super_res_latents=_a	,    image_embeddings=_a	,    ).images

          # make sure passing text embeddings manually is identical
          assert np.abs(img_out_a - img_out_a				).max() < 1E-4
   @skip_mps
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							torch_device == "cpu"

          # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
          lowercase    :				int       		=							1E-2

          self._test_attention_slicing_forward_pass(
              test_max_difference=_a	,    expected_max_diff=_a				)
   @skip_mps
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							torch_device == "cpu"
          lowercase    :				Dict       		=							True
          lowercase    :				Union[str, Any]       		=							[
              "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 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							[
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          if torch_device == "mps":
                 # TODO: MPS errors with larger batch sizes
                 lowercase    :				str       		=							[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 			__magic_name__			(			self				):
          return super().test_dict_tuple_outputs_equivalent()
   @skip_mps
   def 			__magic_name__			(			self				):
          return super().test_save_load_local()

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


@slow
@require_torch_gpu
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"				)
          lowercase    :				Tuple       		=							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"				)

          lowercase    :				Union[str, Any]       		=							UnCLIPImageVariationPipeline.from_pretrained(
              "kakaobrain/karlo-v1-alpha-image-variations"	,    torch_dtype=torch.floataa				)
          lowercase    :				Union[str, Any]       		=							pipeline.to(_a				)
          pipeline.set_progress_bar_config(disable=_a				)

          lowercase    :				Dict       		=							torch.Generator(device="cpu"				).manual_seed(0				)
          lowercase    :				Any       		=							pipeline(
              _a	,    generator=_a	,    output_type="np"	,    )

          lowercase    :				Any       		=							output.images[0]

          assert image.shape == (256, 256, 3)

          assert_mean_pixel_difference(_a	,    _a	,    15				)




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


if __name__ == "__main__":
   _A			:       Optional[int]   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Optional[Any]   		=       [int(item) for item in user_input.split(""",""")]
   print(patience_sort(unsorted))


