


'''simple docstring'''


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							AltDiffusionPipeline
  a_  					=							TEXT_TO_IMAGE_PARAMS
  a_  					=							TEXT_TO_IMAGE_BATCH_PARAMS
  a_  					=							TEXT_TO_IMAGE_IMAGE_PARAMS
  a_  					=							TEXT_TO_IMAGE_IMAGE_PARAMS






  def        _a							(							self :       int ):

     '''simple docstring'''




     torch.manual_seed(0 )
     A_						:  int          =				UNetaDConditionModel(
         block_out_channels=(32, 64)	,layers_per_block=2	,sample_size=32	,in_channels=4	,out_channels=4	,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,cross_attention_dim=32	,)
     A_						:  Dict          =				DDIMScheduler(
         beta_start=0.00085	,beta_end=0.012	,beta_schedule="""scaled_linear"""	,clip_sample=_a	,set_alpha_to_one=_a	,)
     torch.manual_seed(0 )
     A_						:  Dict          =				AutoencoderKL(
         block_out_channels=[32, 64]	,in_channels=3	,out_channels=3	,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,latent_channels=4	,)

     # TODO: address the non-deterministic text encoder (fails for save-load tests)
     # torch.manual_seed(0)
     # text_encoder_config = RobertaSeriesConfig(
     #     hidden_size=32,
     #     project_dim=32,
     #     intermediate_size=37,
     #     layer_norm_eps=1e-05,
     #     num_attention_heads=4,
     #     num_hidden_layers=5,
     #     vocab_size=5002,
     # )
     # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

     torch.manual_seed(0 )
     A_						:  str          =				CLIPTextConfig(
         bos_token_id=0	,eos_token_id=2	,hidden_size=32	,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=5002	,)
     A_						:  int          =				CLIPTextModel(_a )

     A_						:  Tuple          =				XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
     A_						:  Union[str, Any]          =				77

     A_						:  Tuple          =				{
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components






  def        _a							(							self :       List[str]	,_a :       str	,_a :       List[str]=0 ):

     '''simple docstring'''




     if str(_a ).startswith("""mps""" ):
        A_						:  Dict          =				torch.manual_seed(_a )
     else:
        A_						:  List[str]          =				torch.Generator(device=_a ).manual_seed(_a )
     A_						:  List[str]          =				{
         """prompt""": """A painting of a squirrel eating a burger""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs






  def        _a							(							self :       Union[str, Any] ):

     '''simple docstring'''




     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )






  def        _a							(							self :       Any ):

     '''simple docstring'''




     super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






  def        _a							(							self :       List[Any] ):

     '''simple docstring'''




     A_						:  List[str]          =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator

     A_						:  Tuple          =				self.get_dummy_components()
     torch.manual_seed(0 )
     A_						:  Tuple          =				RobertaSeriesConfig(
         hidden_size=32	,project_dim=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,vocab_size=5002	,)
     # TODO: remove after fixing the non-deterministic text encoder
     A_						:  Union[str, Any]          =				RobertaSeriesModelWithTransformation(_a )
     A_						:  Optional[int]          =				text_encoder

     A_						:  str          =				AltDiffusionPipeline(**_a )
     A_						:  Any          =				alt_pipe.to(_a )
     alt_pipe.set_progress_bar_config(disable=_a )

     A_						:  Any          =				self.get_dummy_inputs(_a )
     A_						:  int          =				"""A photo of an astronaut"""
     A_						:  int          =				alt_pipe(**_a )
     A_						:  Tuple          =				output.images
     A_						:  List[str]          =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     A_						:  List[Any]          =				np.array(
         [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2






  def        _a							(							self :       Any ):

     '''simple docstring'''




     A_						:  List[str]          =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator

     A_						:  Any          =				self.get_dummy_components()
     A_						:  List[Any]          =				PNDMScheduler(skip_prk_steps=_a )
     torch.manual_seed(0 )
     A_						:  str          =				RobertaSeriesConfig(
         hidden_size=32	,project_dim=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,vocab_size=5002	,)
     # TODO: remove after fixing the non-deterministic text encoder
     A_						:  List[str]          =				RobertaSeriesModelWithTransformation(_a )
     A_						:  List[Any]          =				text_encoder
     A_						:  Any          =				AltDiffusionPipeline(**_a )
     A_						:  Optional[Any]          =				alt_pipe.to(_a )
     alt_pipe.set_progress_bar_config(disable=_a )

     A_						:  Any          =				self.get_dummy_inputs(_a )
     A_						:  Tuple          =				alt_pipe(**_a )
     A_						:  int          =				output.images
     A_						:  Optional[Any]          =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     A_						:  Any          =				np.array(
         [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2







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


  '''simple docstring'''







  def        _a							(							self :       Optional[Any] ):

     '''simple docstring'''




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






  def        _a							(							self :       Union[str, Any] ):

     '''simple docstring'''




     A_						:  Optional[int]          =				AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""	,safety_checker=_a )
     A_						:  Any          =				alt_pipe.to(_a )
     alt_pipe.set_progress_bar_config(disable=_a )

     A_						:  List[str]          =				"""A painting of a squirrel eating a burger"""
     A_						:  List[str]          =				torch.manual_seed(0 )
     A_						:  Dict          =				alt_pipe([prompt]	,generator=_a	,guidance_scale=6.0	,num_inference_steps=20	,output_type="""np""" )

     A_						:  Optional[Any]          =				output.images

     A_						:  Any          =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     A_						:  List[str]          =				np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2






  def        _a							(							self :       Union[str, Any] ):

     '''simple docstring'''




     A_						:  Union[str, Any]          =				DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""	,subfolder="""scheduler""" )

     A_						:  str          =				AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""	,scheduler=_a	,safety_checker=_a )
     A_						:  Dict          =				alt_pipe.to(_a )
     alt_pipe.set_progress_bar_config(disable=_a )

     A_						:  List[Any]          =				"""A painting of a squirrel eating a burger"""
     A_						:  Dict          =				torch.manual_seed(0 )

     A_						:  int          =				alt_pipe([prompt]	,generator=_a	,num_inference_steps=2	,output_type="""numpy""" )
     A_						:  Any          =				output.images

     A_						:  Dict          =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     A_						:  Dict          =				np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
