'''simple docstring'''

from __future__ import annotations

from collections import Counter
from random import random


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      {}





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      {}





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     if nodea not in self.connections:
        self.add_node(_A )
     if nodea not in self.connections:
        self.add_node(_A )
     lowerCAmelCase_			=      probability





  def 						UpperCAmelCase_	(	self ):
     return list(self.connections )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      0
     lowerCAmelCase_			=      random()

     for dest in self.connections[node]:
        current_probability += self.connections[node][dest]
        if current_probability > random_value:
           return dest
     return ""






def snake_case_      (					__snake_case  :							str	,   __snake_case  :							List[str]	,   __snake_case  :							Dict)			->					Union[str, Any]:
   lowerCAmelCase_			=      MarkovChainGraphUndirectedUnweighted()

   for nodea, nodea, probability in transitions:
      graph.add_transition_probability(__snake_case	,   __snake_case	,   __snake_case)

   lowerCAmelCase_			=      Counter(graph.get_nodes())
   lowerCAmelCase_			=      start

   for _ in range(__snake_case):
      lowerCAmelCase_			=      graph.transition(__snake_case)
      visited[node] += 1

   return visited


if __name__ == "__main__":
 import doctest

 doctest.testmod()






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_     (       __lowercase,  __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     StableUnCLIPPipeline
							UpperCAmelCase				   =     TEXT_TO_IMAGE_PARAMS
							UpperCAmelCase				   =     TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCAmelCase				   =     TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase				   =     TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     32
												_UpperCamelCase			     =     embedder_hidden_size

												# prior components

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

												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextModelWithProjection(
												    CLIPTextConfig(
												        bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=_A ,			projection_dim=_A ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     PriorTransformer(
												    num_attention_heads=2 ,			attention_head_dim=12 ,			embedding_dim=_A ,			num_layers=1 ,			)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     DDPMScheduler(
												    variance_type='''fixed_small_log''' ,			prediction_type='''sample''' ,			num_train_timesteps=1000 ,			clip_sample=_A ,			clip_sample_range=5.0 ,			beta_schedule='''squaredcos_cap_v2''' ,			)

												# regular denoising components

												torch.manual_seed(0		)
												_UpperCamelCase			     =     StableUnCLIPImageNormalizer(embedding_dim=_A		)
												_UpperCamelCase			     =     DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

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

												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextModel(
												    CLIPTextConfig(
												        bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=_A ,			projection_dim=32 ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     UNetaDConditionModel(
												    sample_size=32 ,			in_channels=4 ,			out_channels=4 ,			down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') ,			up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') ,			block_out_channels=(32, 64) ,			attention_head_dim=(2, 4) ,			class_embed_type='''projection''' ,			projection_class_embeddings_input_dim=embedder_projection_dim * 2 ,			cross_attention_dim=_A ,			layers_per_block=1 ,			upcast_attention=_A ,			use_linear_projection=_A ,			)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     DDIMScheduler(
												    beta_schedule='''scaled_linear''' ,			beta_start=0.0_0085 ,			beta_end=0.012 ,			prediction_type='''v_prediction''' ,			set_alpha_to_one=_A ,			steps_offset=1 ,			)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     AutoencoderKL()

												_UpperCamelCase			     =     {
												    # 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      :     Dict ,			_A      :     Tuple ,			_A      :     Dict=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''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      :     Dict		):
												_UpperCamelCase			     =     torch_device == '''cpu'''

												self._test_attention_slicing_forward_pass(test_max_difference=_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     torch_device in ['''cpu''', '''mps''']

												self._test_inference_batch_single_identical(test_max_difference=_A		)




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







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







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''		)

												_UpperCamelCase			     =     StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,			torch_dtype=torch.floataa		)
												pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)
												# 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()

												_UpperCamelCase			     =     torch.Generator(device='''cpu'''		).manual_seed(0		)
												_UpperCamelCase			     =     pipe('''anime turle''' ,			generator=_A ,			output_type='''np'''		)

												_UpperCamelCase			     =     output.images[0]

												assert image.shape == (768, 768, 3)

												assert_mean_pixel_difference(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

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

												_UpperCamelCase			     =     pipe(
												    '''anime turtle''' ,			prior_num_inference_steps=2 ,			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 * 10**9
