import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			StableDiffusionXLImgaImgPipeline
  _SCREAMING_SNAKE_CASE    				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  _SCREAMING_SNAKE_CASE    				=			PipelineTesterMixin.required_optional_params - {"""latents"""}
  _SCREAMING_SNAKE_CASE    				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE    				=			IMAGE_TO_IMAGE_IMAGE_PARAMS
  _SCREAMING_SNAKE_CASE    				=			IMAGE_TO_IMAGE_IMAGE_PARAMS

  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  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")     ,							attention_head_dim=(2, 4)     ,							use_linear_projection=__SCREAMING_SNAKE_CASE     ,							addition_embed_type="text_time"     ,							addition_time_embed_dim=8     ,							transformer_layers_per_block=(1, 2)     ,							projection_class_embeddings_input_dim=80     ,							cross_attention_dim=64     ,							)
   __a       =  EulerDiscreteScheduler(
       beta_start=0.0_00_85     ,							beta_end=0.0_12     ,							steps_offset=1     ,							beta_schedule="scaled_linear"     ,							timestep_spacing="leading"     ,							)
   torch.manual_seed(0       )
   __a       =  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     ,							sample_size=1_28     ,							)
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							hidden_act="gelu"     ,							projection_dim=32     ,							)
   __a       =  CLIPTextModel(__SCREAMING_SNAKE_CASE       )
   __a       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     ,							local_files_only=__SCREAMING_SNAKE_CASE       )

   __a       =  CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE       )
   __a       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     ,							local_files_only=__SCREAMING_SNAKE_CASE       )

   __a       =  {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "text_encoder_2": text_encoder_a,
       "tokenizer_2": tokenizer_a,
       # "safety_checker": None,
       # "feature_extractor": None,
   }
   return components

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Any=0       ):
   __a       =  floats_tensor((1, 3, 32, 32)     ,							rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  image / 2 + 0.5
   if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
    __a       =  torch.manual_seed(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
   __a       =  {
       "prompt": "A painting of a squirrel eating a burger",
       "image": image,
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 5.0,
       "output_type": "numpy",
       "strength": 0.75,
   }
   return inputs

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
   __a       =  self.get_dummy_components()
   __a       =  StableDiffusionXLImgaImgPipeline(**__SCREAMING_SNAKE_CASE       )
   __a       =  sd_pipe.to(__SCREAMING_SNAKE_CASE       )
   sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
   __a       =  sd_pipe(**__SCREAMING_SNAKE_CASE       ).images
   __a       =  image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)

   __a       =  np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]       )

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

  def 		_UpperCAmelCase   (		self :      int       ):
   super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )

  def 		_UpperCAmelCase   (		self :      int       ):
   super().test_inference_batch_single_identical(expected_max_diff=3E-3       )

  def 		_UpperCAmelCase   (		self :      int       ):
   pass

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.get_dummy_components()
   __a       =  StableDiffusionXLImgaImgPipeline(**__SCREAMING_SNAKE_CASE       )
   __a       =  sd_pipe.to(__SCREAMING_SNAKE_CASE       )
   __a       =  sd_pipe.to(__SCREAMING_SNAKE_CASE       )
   sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   # forward without prompt embeds
   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
   __a       =  3 * ["this is a negative prompt"]
   __a       =  negative_prompt
   __a       =  3 * [inputs["prompt"]]

   __a       =  sd_pipe(**__SCREAMING_SNAKE_CASE       )
   __a       =  output.images[0, -3:, -3:, -1]

   # forward with prompt embeds
   __a       =  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )
   __a       =  3 * ["this is a negative prompt"]
   __a       =  3 * [inputs.pop("prompt"       )]

   (
       (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     
   )       =  sd_pipe.encode_prompt(__SCREAMING_SNAKE_CASE     ,							negative_prompt=__SCREAMING_SNAKE_CASE       )

   __a       =  sd_pipe(
       **__SCREAMING_SNAKE_CASE     ,							prompt_embeds=__SCREAMING_SNAKE_CASE     ,							negative_prompt_embeds=__SCREAMING_SNAKE_CASE     ,							pooled_prompt_embeds=__SCREAMING_SNAKE_CASE     ,							negative_pooled_prompt_embeds=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  output.images[0, -3:, -3:, -1]

   # make sure that it's equal
   assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4
@slow
@require_torch_gpu
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]="cpu"     ,							__SCREAMING_SNAKE_CASE :      str=torch.floataa     ,							__SCREAMING_SNAKE_CASE :      Tuple=0       ):
   __a       =  torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
   __a       =  np.random.RandomState(__SCREAMING_SNAKE_CASE       ).standard_normal((1, 4, 64, 64)       )
   __a       =  torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE     ,							dtype=__SCREAMING_SNAKE_CASE       )
   __a       =  {
       "prompt": "a photograph of an astronaut riding a horse",
       "latents": latents,
       "generator": generator,
       "num_inference_steps": 3,
       "guidance_scale": 7.5,
       "output_type": "numpy",
   }
   return inputs

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"       )
   pipe.to(__SCREAMING_SNAKE_CASE       )
   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  self.get_inputs(__SCREAMING_SNAKE_CASE       )
   __a       =  pipe(**__SCREAMING_SNAKE_CASE       ).images
   __a       =  image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 5_12, 5_12, 3)
   __a       =  np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]       )
   assert np.abs(image_slice - expected_slice       ).max() < 7E-3



from __future__ import annotations

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[]



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  if board[row][i] == 1:
   return False
 for i in range(len(_A					)					):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				-1							,				-1					)					):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				len(_A					)					)					):
  if board[i][j] == 1:
   return False
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if row >= len(_A					):
  solution.append(_A					)
  printboard(_A					)
  print()
  return True
 for i in range(len(_A					)					):
  if is_safe(_A							,				_A							,				_A					):
   __a       =  1
   solve(_A							,				row + 1					)
   __a       =  0
 return False



def   __A		(			_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  for j in range(len(_A					)					):
   if board[i][j] == 1:
    print("Q"							,				end=" "					)
   else:
    print("."							,				end=" "					)
  print()


# n=int(input("The no. of queens"))
SCREAMING_SNAKE_CASE :	List[str]							     =		8
SCREAMING_SNAKE_CASE :	str							     =		[[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))


