"""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						(				__lowerCamelCase , unittest.TestCase					):
				lowerCamelCase__									=    UnCLIPPipeline
				lowerCamelCase__									=    TEXT_TO_IMAGE_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				    "guidance_scale",
				    "prompt_embeds",
				    "cross_attention_kwargs",
				}
				lowerCamelCase__									=    TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCamelCase__									=    [
				    "generator",
				    "return_dict",
				    "prior_num_inference_steps",
				    "decoder_num_inference_steps",
				    "super_res_num_inference_steps",
				]
				lowerCamelCase__									=    False

				@property
				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										return 3_2

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 3_2

				@property
				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										return self.time_input_dim

				@property
				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										return self.time_input_dim * 4

				@property
				def     A	(       self    :      Any       ):


										'''simple docstring'''
										return 1_0_0

				@property
				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
										return tokenizer

				@property
				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										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=3_7    ,      layer_norm_eps=1e-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1_0_0_0    ,      )
										return CLIPTextModelWithProjection(_UpperCamelCase       )

				@property
				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										torch.manual_seed(0       )

										UpperCAmelCase__   		=						{
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 1_2,
										    """embedding_dim""": self.text_embedder_hidden_size,
										    """num_layers""": 1,
										}

										UpperCAmelCase__   		=						PriorTransformer(**_UpperCamelCase       )
										return model

				@property
				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										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(**_UpperCamelCase       )
										return model

				@property
				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										torch.manual_seed(0       )

										UpperCAmelCase__   		=						{
										    """sample_size""": 3_2,
										    # 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(**_UpperCamelCase       )
										return model

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return {
										    "sample_size": 6_4,
										    "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    :      int       ):


										'''simple docstring'''
										torch.manual_seed(0       )

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

				@property
				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										torch.manual_seed(1       )

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

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										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=1_0_0_0    ,      clip_sample_range=5.0    ,      )

										UpperCAmelCase__   		=						UnCLIPScheduler(
										    variance_type="""learned_range"""    ,      prediction_type="""epsilon"""    ,      num_train_timesteps=1_0_0_0    ,      )

										UpperCAmelCase__   		=						UnCLIPScheduler(
										    variance_type="""fixed_small_log"""    ,      prediction_type="""epsilon"""    ,      num_train_timesteps=1_0_0_0    ,      )

										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    :      str    ,      _lowercase    :      Dict    ,      _lowercase    :      int=0       ):


										'''simple docstring'''





										if str(_UpperCamelCase       ).startswith("""mps"""       ):
																UpperCAmelCase__   		=						torch.manual_seed(_UpperCamelCase       )
										else:
																UpperCAmelCase__   		=						torch.Generator(device=_UpperCamelCase       ).manual_seed(_UpperCamelCase       )
										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    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""cpu"""

										UpperCAmelCase__   		=						self.get_dummy_components()

										UpperCAmelCase__   		=						self.pipeline_class(**_UpperCamelCase       )
										UpperCAmelCase__   		=						pipe.to(_UpperCamelCase       )

										pipe.set_progress_bar_config(disable=_UpperCamelCase       )

										UpperCAmelCase__   		=						pipe(**self.get_dummy_inputs(_UpperCamelCase       )       )
										UpperCAmelCase__   		=						output.images

										UpperCAmelCase__   		=						pipe(
										    **self.get_dummy_inputs(_UpperCamelCase       )    ,      return_dict=_UpperCamelCase    ,      )[0]

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

										assert image.shape == (1, 6_4, 6_4, 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    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						torch.device("""cpu"""       )


										class      _lowercase						:
														lowerCamelCase__									=    1
										UpperCAmelCase__   		=						self.get_dummy_components()

										UpperCAmelCase__   		=						self.pipeline_class(**_UpperCamelCase       )
										UpperCAmelCase__   		=						pipe.to(_UpperCamelCase       )

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

										UpperCAmelCase__   		=						torch.Generator(device=_UpperCamelCase       ).manual_seed(0       )
										UpperCAmelCase__   		=						prior.dtype
										UpperCAmelCase__   		=						1

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

										pipe.set_progress_bar_config(disable=_UpperCamelCase       )

										UpperCAmelCase__   		=						"""this is a prompt example"""

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

										UpperCAmelCase__   		=						tokenizer(
										    _UpperCamelCase    ,      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=_UpperCamelCase       ).manual_seed(0       )
										UpperCAmelCase__   		=						pipe(
										    generator=_UpperCamelCase    ,      prior_num_inference_steps=2    ,      decoder_num_inference_steps=2    ,      super_res_num_inference_steps=2    ,      prior_latents=_UpperCamelCase    ,      decoder_latents=_UpperCamelCase    ,      super_res_latents=_UpperCamelCase    ,      text_model_output=_UpperCamelCase    ,      text_attention_mask=_UpperCamelCase    ,      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    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						torch_device == """cpu"""

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

				@skip_mps
				def     A	(       self    :      Any       ):


										'''simple docstring'''
										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=_UpperCamelCase    ,      relax_max_difference=_UpperCamelCase    ,      additional_params_copy_to_batched_inputs=_UpperCamelCase    ,      )

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


										'''simple docstring'''
										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=_UpperCamelCase    ,      additional_params_copy_to_batched_inputs=_UpperCamelCase    ,      )
										else:
																self._test_inference_batch_consistent(
																    additional_params_copy_to_batched_inputs=_UpperCamelCase       )

				@skip_mps
				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										return super().test_dict_tuple_outputs_equivalent()

				@skip_mps
				def     A	(       self    :      int       ):


										'''simple docstring'''
										return super().test_save_load_local()

				@skip_mps
				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										return super().test_save_load_optional_components()


@nightly
class      _lowercase						(				unittest.TestCase					):

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


										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										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=_UpperCamelCase       )

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

										UpperCAmelCase__   		=						output.images[0]

										assert image.shape == (2_5_6, 2_5_6, 3)
										assert np.abs(expected_image - image       ).max() < 1e-1


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

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										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(_UpperCamelCase       )
										pipeline.set_progress_bar_config(disable=_UpperCamelCase       )

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

										UpperCAmelCase__   		=						output.images[0]

										assert image.shape == (2_5_6, 2_5_6, 3)

										assert_mean_pixel_difference(_UpperCamelCase    ,      _UpperCamelCase       )

				def     A	(       self    :      str       ):


										'''simple docstring'''
										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(_UpperCamelCase       )
										pipe.set_progress_bar_config(disable=_UpperCamelCase       )
										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 * 1_0**9


"""simple docstring"""







def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
						for gap in gaps:
												for i in range(UpperCAmelCase__						,     len(UpperCAmelCase__     )     ):
																		UpperCAmelCase__   		=						collection[i]
																		UpperCAmelCase__   		=						i
																		while j >= gap and collection[j - gap] > insert_value:
																								UpperCAmelCase__   		=						collection[j - gap]
																								j -= gap
																		if j != i:
																								UpperCAmelCase__   		=						insert_value
						return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						UpperCamelCase_    :    Union[str, Any]											=						input("""Enter numbers separated by a comma:\n""").strip()
						UpperCamelCase_    :    int											=						[int(item) for item in user_input.split(""",""")]
						print(shell_sort(unsorted))

