






'''simple docstring'''







from __future__ import annotations

from queue import Queue






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	Any					)  ->       None:
           __A		   =      {}






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       None:
           for i in self.vertices:
                 print(lowercase__				,				" : "				,				" -> ".join([str(lowercase__					) for j in self.vertices[i]]					)					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	int				,				lowercase__    :	int					)  ->       None:
           if from_vertex in self.vertices:
                 self.vertices[from_vertex].append(lowercase__					)
           else:
                 __A		   =      [to_vertex]






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	int					)  ->       set[int]:
           __A		   =      set()

           # create a first in first out queue to store all the vertices for BFS
           __A		   =      Queue()

           # mark the source node as visited and enqueue it
           visited.add(lowercase__					)
           queue.put(lowercase__					)

           while not queue.empty():
                 __A		   =      queue.get()

                 # loop through all adjacent vertex and enqueue it if not yet visited
                 for adjacent_vertex in self.vertices[vertex]:
                       if adjacent_vertex not in visited:
                             queue.put(lowercase__					)
                             visited.add(lowercase__					)
           return visited


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

       testmod(verbose=True)

       __A      :       Dict      =	Graph()
       g.add_edge(0, 1)
       g.add_edge(0, 2)
       g.add_edge(1, 2)
       g.add_edge(2, 0)
       g.add_edge(2, 3)
       g.add_edge(3, 3)

       g.print_graph()
       # 0  :  1 -> 2
       # 1  :  2
       # 2  :  0 -> 3
       # 3  :  3

       assert sorted(g.bfs(2)) == [0, 1, 2, 3]










'''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 					a__    (			lowerCamelCase ,						unittest.TestCase						):
     '''simple docstring'''







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






     @property
     def     _UpperCamelCase   (self    :	Dict					)  ->       Union[str, Any]:
           return 32






     @property
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           return 32






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       Dict:
           return self.time_input_dim






     @property
     def     _UpperCamelCase   (self    :	List[Any]					)  ->       int:
           return self.time_input_dim * 4






     @property
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       List[str]:
           return 100






     @property
     def     _UpperCamelCase   (self    :	Tuple					)  ->       Optional[Any]:
           __A		   =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
           return tokenizer






     @property
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           torch.manual_seed(0					)
           __A		   =      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(lowercase__					)






     @property
     def     _UpperCamelCase   (self    :	int					)  ->       Optional[Any]:
           torch.manual_seed(0					)

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

           __A		   =      PriorTransformer(**lowercase__					)
           return model






     @property
     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Union[str, Any]:
           torch.manual_seed(0					)

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

           __A		   =      UnCLIPTextProjModel(**lowercase__					)
           return model






     @property
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Tuple:
           torch.manual_seed(0					)

           __A		   =      {
               "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",
           }

           __A		   =      UNetaDConditionModel(**lowercase__					)
           return model






     @property
     def     _UpperCamelCase   (self    :	Any					)  ->       str:
           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     _UpperCamelCase   (self    :	List[str]					)  ->       List[str]:
           torch.manual_seed(0					)

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






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       Optional[Any]:
           # seeded differently to get different unet than `self.dummy_super_res_first`
           torch.manual_seed(1					)

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






     def     _UpperCamelCase   (self    :	Any					)  ->       Any:
           __A		   =      self.dummy_prior
           __A		   =      self.dummy_decoder
           __A		   =      self.dummy_text_proj
           __A		   =      self.dummy_text_encoder
           __A		   =      self.dummy_tokenizer
           __A		   =      self.dummy_super_res_first
           __A		   =      self.dummy_super_res_last

           __A		   =      UnCLIPScheduler(
               variance_type="fixed_small_log"				,				prediction_type="sample"				,				num_train_timesteps=1000				,				clip_sample_range=5.0				,				)

           __A		   =      UnCLIPScheduler(
               variance_type="learned_range"				,				prediction_type="epsilon"				,				num_train_timesteps=1000				,				)

           __A		   =      UnCLIPScheduler(
               variance_type="fixed_small_log"				,				prediction_type="epsilon"				,				num_train_timesteps=1000				,				)

           __A		   =      {
               "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     _UpperCamelCase   (self    :	int				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Any=0					)  ->       str:
           if str(lowercase__					).startswith("mps"					):
                 __A		   =      torch.manual_seed(lowercase__					)
           else:
                 __A		   =      torch.Generator(device=lowercase__					).manual_seed(lowercase__					)
           __A		   =      {
               "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     _UpperCamelCase   (self    :	str					)  ->       Tuple:
           __A		   =      "cpu"

           __A		   =      self.get_dummy_components()

           __A		   =      self.pipeline_class(**lowercase__					)
           __A		   =      pipe.to(lowercase__					)

           pipe.set_progress_bar_config(disable=lowercase__					)

           __A		   =      pipe(**self.get_dummy_inputs(lowercase__					)					)
           __A		   =      output.images

           __A		   =      pipe(
               **self.get_dummy_inputs(lowercase__					)				,				return_dict=lowercase__				,				)[0]

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

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

           __A		   =      np.array(
               [
                   0.99_97,
                   0.99_88,
                   0.00_28,
                   0.99_97,
                   0.99_84,
                   0.99_65,
                   0.00_29,
                   0.99_86,
                   0.00_25,
               ]					)

           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     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[Any]:
           __A		   =      torch.device("cpu"					)






           class 					a__    :
                '''simple docstring'''







                _snake_case             =  1







           __A		   =      self.get_dummy_components()

           __A		   =      self.pipeline_class(**lowercase__					)
           __A		   =      pipe.to(lowercase__					)

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

           __A		   =      torch.Generator(device=lowercase__					).manual_seed(0					)
           __A		   =      prior.dtype
           __A		   =      1

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

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

           pipe.set_progress_bar_config(disable=lowercase__					)

           __A		   =      "this is a prompt example"

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

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

           __A		   =      torch.Generator(device=lowercase__					).manual_seed(0					)
           __A		   =      pipe(
               generator=lowercase__				,				prior_num_inference_steps=2				,				decoder_num_inference_steps=2				,				super_res_num_inference_steps=2				,				prior_latents=lowercase__				,				decoder_latents=lowercase__				,				super_res_latents=lowercase__				,				text_model_output=lowercase__				,				text_attention_mask=lowercase__				,				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     _UpperCamelCase   (self    :	str					)  ->       str:
           __A		   =      torch_device == "cpu"

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






     @skip_mps
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       str:
           __A		   =      torch_device == "cpu"
           __A		   =      True
           __A		   =      [
               "prior_num_inference_steps",
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

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






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[int]:
           __A		   =      [
               "prior_num_inference_steps",
               "decoder_num_inference_steps",
               "super_res_num_inference_steps",
           ]

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






     @skip_mps
     def     _UpperCamelCase   (self    :	int					)  ->       int:
           return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       int:
           return super().test_save_load_local()






     @skip_mps
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Any:
           return super().test_save_load_optional_components()







@nightly
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







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






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Tuple:
           __A		   =      load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_horse_cpu.npy"					)

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

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

           __A		   =      output.images[0]

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







@slow
@require_torch_gpu
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       List[str]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Dict:
           __A		   =      load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/unclip/karlo_v1_alpha_horse_fp16.npy"					)

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

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

           __A		   =      output.images[0]

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

           assert_mean_pixel_difference(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Any:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

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

           __A		   =      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"				,				)

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


