






"""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        a     (   a_,				a_,				a_,				unittest.TestCase	):
    UpperCAmelCase_ :     List[Any]	     =StableUnCLIPPipeline
    UpperCAmelCase_ :     Any	     =TEXT_TO_IMAGE_PARAMS
    UpperCAmelCase_ :     Any	     =TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCAmelCase_ :     Optional[int]	     =TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCAmelCase_ :     Tuple	     =TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    UpperCAmelCase_ :     List[Any]	     =False







    def 			UpperCamelCase_      (			self       ):
      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-05							,		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-05							,		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_0_0_8_5							,		beta_end=0.0_1_2							,		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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=0       ):
      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 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch_device == 'cpu'

      self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch_device in ['cpu', 'mps']

      self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase       )


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







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







    def 			UpperCamelCase_      (			self       ):
      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 			UpperCamelCase_      (			self       ):
      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"""



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_UpperCamelCase		:  Optional[Any]						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase = True       ):
      lowercase							    =       {}  # dictionary of lists
      lowercase							    =       directed







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):

      if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       [source_vertex]
      else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       []

      return self







    def __repr__(			self       ):
      return pformat(self.adj_list       )


