


'''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 _snake_case (			a__     , unittest.TestCase ):
							"""simple docstring"""





							__a				       =  UnCLIPPipeline
							__a				       =  TEXT_TO_IMAGE_PARAMS - {
							    '''negative_prompt''',
							    '''height''',
							    '''width''',
							    '''negative_prompt_embeds''',
							    '''guidance_scale''',
							    '''prompt_embeds''',
							    '''cross_attention_kwargs''',
							}
							__a				       =  TEXT_TO_IMAGE_BATCH_PARAMS
							__a				       =  [
							    '''generator''',
							    '''return_dict''',
							    '''prior_num_inference_steps''',
							    '''decoder_num_inference_steps''',
							    '''super_res_num_inference_steps''',
							]
							__a				       =  False







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->str:



											"""simple docstring"""







											return 32







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->str:



											"""simple docstring"""







											return 32







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Tuple:



											"""simple docstring"""







											return self.time_input_dim







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->str:



											"""simple docstring"""







											return self.time_input_dim * 4







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[Any]:



											"""simple docstring"""







											return 100







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
											return tokenizer







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Union[str, Any]:



											"""simple docstring"""







											torch.manual_seed(0						)
											__lowerCamelCase		:   int           =       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=1000      ,					)
											return CLIPTextModelWithProjection(__A						)







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->str:



											"""simple docstring"""







											torch.manual_seed(0						)

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

											__lowerCamelCase		:   Dict           =       PriorTransformer(**__A						)
											return model







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											torch.manual_seed(0						)

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

											__lowerCamelCase		:   List[Any]           =       UnCLIPTextProjModel(**__A						)
											return model







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											torch.manual_seed(0						)

											__lowerCamelCase		:   Tuple           =       {
											    "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",
											}

											__lowerCamelCase		:   Tuple           =       UNetaDConditionModel(**__A						)
											return model







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											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 		__SCREAMING_SNAKE_CASE( self						)  ->str:



											"""simple docstring"""







											torch.manual_seed(0						)

											__lowerCamelCase		:   Tuple           =       UNetaDModel(**self.dummy_super_res_kwargs						)
											return model







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											torch.manual_seed(1						)

											__lowerCamelCase		:   Union[str, Any]           =       UNetaDModel(**self.dummy_super_res_kwargs						)
											return model







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       self.dummy_prior
											__lowerCamelCase		:   Optional[Any]           =       self.dummy_decoder
											__lowerCamelCase		:   Any           =       self.dummy_text_proj
											__lowerCamelCase		:   str           =       self.dummy_text_encoder
											__lowerCamelCase		:   Dict           =       self.dummy_tokenizer
											__lowerCamelCase		:   Union[str, Any]           =       self.dummy_super_res_first
											__lowerCamelCase		:   str           =       self.dummy_super_res_last

											__lowerCamelCase		:   Optional[int]           =       UnCLIPScheduler(
											    variance_type="fixed_small_log"      ,					prediction_type="sample"      ,					num_train_timesteps=1000      ,					clip_sample_range=5.0      ,					)

											__lowerCamelCase		:   List[str]           =       UnCLIPScheduler(
											    variance_type="learned_range"      ,					prediction_type="epsilon"      ,					num_train_timesteps=1000      ,					)

											__lowerCamelCase		:   Optional[Any]           =       UnCLIPScheduler(
											    variance_type="fixed_small_log"      ,					prediction_type="epsilon"      ,					num_train_timesteps=1000      ,					)

											__lowerCamelCase		:   Any           =       {
											    "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 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=0						)  ->Optional[Any]:



											"""simple docstring"""







											if str(__A						).startswith("mps"						):
															__lowerCamelCase		:   Any           =       torch.manual_seed(__A						)
											else:
															__lowerCamelCase		:   List[str]           =       torch.Generator(device=__A						).manual_seed(__A						)
											__lowerCamelCase		:   List[str]           =       {
											    "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 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       "cpu"

											__lowerCamelCase		:   Any           =       self.get_dummy_components()

											__lowerCamelCase		:   List[Any]           =       self.pipeline_class(**__A						)
											__lowerCamelCase		:   Tuple           =       pipe.to(__A						)

											pipe.set_progress_bar_config(disable=__A						)

											__lowerCamelCase		:   int           =       pipe(**self.get_dummy_inputs(__A						)						)
											__lowerCamelCase		:   Dict           =       output.images

											__lowerCamelCase		:   int           =       pipe(
											    **self.get_dummy_inputs(__A						)      ,					return_dict=__A      ,					)[0]

											__lowerCamelCase		:   Dict           =       image[0, -3:, -3:, -1]
											__lowerCamelCase		:   int           =       image_from_tuple[0, -3:, -3:, -1]

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

											__lowerCamelCase		:   List[Any]           =       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 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       torch.device("cpu"						)





											class _snake_case :
																		"""simple docstring"""





																		__a				       =  1

											__lowerCamelCase		:   str           =       self.get_dummy_components()

											__lowerCamelCase		:   Dict           =       self.pipeline_class(**__A						)
											__lowerCamelCase		:   Optional[Any]           =       pipe.to(__A						)

											__lowerCamelCase		:   int           =       components["prior"]
											__lowerCamelCase		:   Optional[Any]           =       components["decoder"]
											__lowerCamelCase		:   Tuple           =       components["super_res_first"]
											__lowerCamelCase		:   List[Any]           =       components["tokenizer"]
											__lowerCamelCase		:   Union[str, Any]           =       components["text_encoder"]

											__lowerCamelCase		:   int           =       torch.Generator(device=__A						).manual_seed(0						)
											__lowerCamelCase		:   List[str]           =       prior.dtype
											__lowerCamelCase		:   Dict           =       1

											__lowerCamelCase		:   int           =       (batch_size, prior.config.embedding_dim)
											__lowerCamelCase		:   Dict           =       pipe.prepare_latents(
											    __A      ,					dtype=__A      ,					device=__A      ,					generator=__A      ,					latents=__A      ,					scheduler=DummyScheduler()						)
											__lowerCamelCase		:   Optional[Any]           =       (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
											__lowerCamelCase		:   int           =       pipe.prepare_latents(
											    __A      ,					dtype=__A      ,					device=__A      ,					generator=__A      ,					latents=__A      ,					scheduler=DummyScheduler()						)

											__lowerCamelCase		:   int           =       (
											    batch_size,
											    super_res_first.config.in_channels // 2,
											    super_res_first.config.sample_size,
											    super_res_first.config.sample_size,
											)
											__lowerCamelCase		:   Dict           =       pipe.prepare_latents(
											    __A      ,					dtype=__A      ,					device=__A      ,					generator=__A      ,					latents=__A      ,					scheduler=DummyScheduler()						)

											pipe.set_progress_bar_config(disable=__A						)

											__lowerCamelCase		:   Optional[Any]           =       "this is a prompt example"

											__lowerCamelCase		:   Dict           =       torch.Generator(device=__A						).manual_seed(0						)
											__lowerCamelCase		:   str           =       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"      ,					)
											__lowerCamelCase		:   Any           =       output.images

											__lowerCamelCase		:   Any           =       tokenizer(
											    __A      ,					padding="max_length"      ,					max_length=tokenizer.model_max_length      ,					return_tensors="pt"      ,					)
											__lowerCamelCase		:   str           =       text_encoder(text_inputs.input_ids						)
											__lowerCamelCase		:   Any           =       text_inputs.attention_mask

											__lowerCamelCase		:   List[str]           =       torch.Generator(device=__A						).manual_seed(0						)
											__lowerCamelCase		:   Union[str, Any]           =       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 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       torch_device == "cpu"

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







							@skip_mps
							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       torch_device == "cpu"
											__lowerCamelCase		:   Optional[Any]           =       True
											__lowerCamelCase		:   Optional[int]           =       [
											    "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 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       [
											    "prior_num_inference_steps",
											    "decoder_num_inference_steps",
											    "super_res_num_inference_steps",
											]

											if torch_device == "mps":
															# TODO: MPS errors with larger batch sizes
															__lowerCamelCase		:   Optional[int]           =       [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 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											return super().test_dict_tuple_outputs_equivalent()







							@skip_mps
							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return super().test_save_load_local()







							@skip_mps
							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											return super().test_save_load_optional_components()





@nightly
class _snake_case (			unittest.TestCase ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self						)  ->Union[str, Any]:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/unclip/karlo_v1_alpha_horse_cpu.npy"						)

											__lowerCamelCase		:   Union[str, Any]           =       UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"						)
											pipeline.set_progress_bar_config(disable=__A						)

											__lowerCamelCase		:   Optional[int]           =       torch.manual_seed(0						)
											__lowerCamelCase		:   str           =       pipeline(
											    "horse"      ,					num_images_per_prompt=1      ,					generator=__A      ,					output_type="np"      ,					)

											__lowerCamelCase		:   Union[str, Any]           =       output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image						).max() < 1E-1





@slow
@require_torch_gpu
class _snake_case (			unittest.TestCase ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/unclip/karlo_v1_alpha_horse_fp16.npy"						)

											__lowerCamelCase		:   Tuple           =       UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,					torch_dtype=torch.floataa						)
											__lowerCamelCase		:   List[Any]           =       pipeline.to(__A						)
											pipeline.set_progress_bar_config(disable=__A						)

											__lowerCamelCase		:   str           =       torch.Generator(device="cpu"						).manual_seed(0						)
											__lowerCamelCase		:   Dict           =       pipeline(
											    "horse"      ,					generator=__A      ,					output_type="np"      ,					)

											__lowerCamelCase		:   List[str]           =       output.images[0]

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

											assert_mean_pixel_difference(__A      ,					__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->str:



											"""simple docstring"""







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

											__lowerCamelCase		:   Union[str, Any]           =       UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,					torch_dtype=torch.floataa						)
											__lowerCamelCase		:   Dict           =       pipe.to(__A						)
											pipe.set_progress_bar_config(disable=__A						)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCamelCase		:   Optional[Any]           =       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"      ,					)

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









'''simple docstring'''




from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       word
											__lowerCamelCase		:   Optional[Any]           =       count







							def __eq__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count == other.count







							def __lt__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count < other.count





def 				a_							(    lowercase				,    lowercase     )      ->list[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Heap[WordCount]           =       Heap()
				__lowerCamelCase		:   int           =       Counter(lowercase     )
				heap.build_max_heap(
				    [WordCount(lowercase				,    lowercase     ) for word, count in count_by_word.items()]     )
				return [heap.extract_max().word for _ in range(min(lowercase				,    len(lowercase     )     )     )]


if __name__ == "__main__":
				import doctest

				doctest.testmod()





