


"""simple docstring"""

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class    lowerCamelCase__						(		__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			,   unittest.TestCase    ):
      a :     Tuple											=		StableUnCLIPPipeline
      a :     Tuple											=		TEXT_TO_IMAGE_PARAMS
      a :     Any											=		TEXT_TO_IMAGE_BATCH_PARAMS
      a :     Dict											=		TEXT_TO_IMAGE_IMAGE_PARAMS
      a :     Union[str, Any]											=		TEXT_TO_IMAGE_IMAGE_PARAMS

      # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
      a :     Dict											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	3_2
       __lowercase									=	embedder_hidden_size

       # prior components

       torch.manual_seed(0							)
       __lowercase									=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

       torch.manual_seed(0							)
       __lowercase									=	CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=lowerCAmelCase_      ,       projection_dim=lowerCAmelCase_      ,       intermediate_size=3_7      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_0_0_0      ,       )							)

       torch.manual_seed(0							)
       __lowercase									=	PriorTransformer(
           num_attention_heads=2      ,       attention_head_dim=1_2      ,       embedding_dim=lowerCAmelCase_      ,       num_layers=1      ,       )

       torch.manual_seed(0							)
       __lowercase									=	DDPMScheduler(
           variance_type="""fixed_small_log"""      ,       prediction_type="""sample"""      ,       num_train_timesteps=1_0_0_0      ,       clip_sample=lowerCAmelCase_      ,       clip_sample_range=5.0      ,       beta_schedule="""squaredcos_cap_v2"""      ,       )

       # regular denoising components

       torch.manual_seed(0							)
       __lowercase									=	StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase_							)
       __lowercase									=	DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""							)

       torch.manual_seed(0							)
       __lowercase									=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

       torch.manual_seed(0							)
       __lowercase									=	CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=lowerCAmelCase_      ,       projection_dim=3_2      ,       intermediate_size=3_7      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_0_0_0      ,       )							)

       torch.manual_seed(0							)
       __lowercase									=	UNetaDConditionModel(
           sample_size=3_2      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       block_out_channels=(3_2, 6_4)      ,       attention_head_dim=(2, 4)      ,       class_embed_type="""projection"""      ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,       cross_attention_dim=lowerCAmelCase_      ,       layers_per_block=1      ,       upcast_attention=lowerCAmelCase_      ,       use_linear_projection=lowerCAmelCase_      ,       )

       torch.manual_seed(0							)
       __lowercase									=	DDIMScheduler(
           beta_schedule="""scaled_linear"""      ,       beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       prediction_type="""v_prediction"""      ,       set_alpha_to_one=lowerCAmelCase_      ,       steps_offset=1      ,       )

       torch.manual_seed(0							)
       __lowercase									=	AutoencoderKL()

       __lowercase									=	{
           # prior components
           """prior_tokenizer""": prior_tokenizer,
           """prior_text_encoder""": prior_text_encoder,
           """prior""": prior,
           """prior_scheduler""": prior_scheduler,
           # image noising components
           """image_normalizer""": image_normalizer,
           """image_noising_scheduler""": image_noising_scheduler,
           # regular denoising components
           """tokenizer""": tokenizer,
           """text_encoder""": text_encoder,
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
       }

       return components
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Optional[int]      ,       A_	:	Optional[Any]=0							):






       '''simple docstring'''


       if str(lowerCAmelCase_							).startswith("""mps"""							):
        __lowercase									=	torch.manual_seed(lowerCAmelCase_							)
       else:
        __lowercase									=	torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
       __lowercase									=	{
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """prior_num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	torch_device == """cpu"""

       self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	torch_device in ["""cpu""", """mps"""]

       self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase_							)






@slow
@require_torch_gpu
class    lowerCamelCase__						(		unittest.TestCase    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""							)

       __lowercase									=	StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      ,       torch_dtype=torch.floataa							)
       pipe.to(lowerCAmelCase_							)
       pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       __lowercase									=	torch.Generator(device="""cpu"""							).manual_seed(0							)
       __lowercase									=	pipe("""anime turle"""      ,       generator=lowerCAmelCase_      ,       output_type="""np"""							)

       __lowercase									=	output.images[0]

       assert image.shape == (7_6_8, 7_6_8, 3)

       assert_mean_pixel_difference(lowerCAmelCase_      ,       lowerCAmelCase_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


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

       __lowercase									=	StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      ,       torch_dtype=torch.floataa							)
       __lowercase									=	pipe.to(lowerCAmelCase_							)
       pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       __lowercase									=	pipe(
           """anime turtle"""      ,       prior_num_inference_steps=2      ,       num_inference_steps=2      ,       output_type="""np"""      ,       )

       __lowercase									=	torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 1_0**9






"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[False] * len(UpperCamelCase__ )
 __lowercase									=	[]
 queue.append(UpperCamelCase__ )
 __lowercase									=	True

 while queue:
  __lowercase									=	queue.pop(0 )
  for ind in range(len(graph[u] ) ):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(UpperCamelCase__ )
    __lowercase									=	True
    __lowercase									=	u

 return visited[t]


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 __lowercase									=	[-1] * (len(UpperCamelCase__ ))
 __lowercase									=	0
 while bfs(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	float("""Inf""" )
  __lowercase									=	sink

  while s != source:
   # Find the minimum value in select path
   __lowercase									=	min(UpperCamelCase__				,   graph[parent[s]][s] )
   __lowercase									=	parent[s]

  max_flow += path_flow
  __lowercase									=	sink

  while v != source:
   __lowercase									=	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __lowercase									=	parent[v]
 return max_flow


UpperCAmelCase__  							=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__							,      UpperCAmelCase__  							=0, 5

print(ford_fulkerson(graph, source, sink))
