



'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      UnCLIPImageVariationPipeline
 __magic_name__					: Union[str, Any]  =      IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
 __magic_name__					: Dict  =      IMAGE_VARIATION_BATCH_PARAMS

 __magic_name__					: Optional[int]  =      [
     "generator",
     "return_dict",
     "decoder_num_inference_steps",
     "super_res_num_inference_steps",
 ]
 __magic_name__					: Union[str, Any]  =      False




 @property
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      return 3_2




 @property
 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      return 3_2




 @property
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      return self.time_input_dim




 @property
 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      return self.time_input_dim * 4




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      return 1_0_0




 @property
 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      a_       		=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
      return tokenizer




 @property
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      torch.manual_seed(0)
      a_       		=CLIPTextConfig(
          bos_token_id=0							,  eos_token_id=2							,  hidden_size=self.text_embedder_hidden_size							,  projection_dim=self.text_embedder_hidden_size							,  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							,  )
      return CLIPTextModelWithProjection(lowerCAmelCase_)




 @property
 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      torch.manual_seed(0)
      a_       		=CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size							,  projection_dim=self.text_embedder_hidden_size							,  num_hidden_layers=5							,  num_attention_heads=4							,  image_size=3_2							,  intermediate_size=3_7							,  patch_size=1							,  )
      return CLIPVisionModelWithProjection(lowerCAmelCase_)




 @property
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      torch.manual_seed(0)

      a_       		={
          "clip_embeddings_dim": self.text_embedder_hidden_size,
          "time_embed_dim": self.time_embed_dim,
          "cross_attention_dim": self.cross_attention_dim,
      }

      a_       		=UnCLIPTextProjModel(**lowerCAmelCase_)
      return model




 @property
 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      torch.manual_seed(0)

      a_       		={
          "sample_size": 3_2,
          # RGB in channels
          "in_channels": 3,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 6,
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": "identity",
      }

      a_       		=UNetaDConditionModel(**lowerCAmelCase_)
      return model




 @property
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      return {
          "sample_size": 6_4,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      torch.manual_seed(0)

      a_       		=UNetaDModel(**self.dummy_super_res_kwargs)
      return model




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      torch.manual_seed(1)

      a_       		=UNetaDModel(**self.dummy_super_res_kwargs)
      return model




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		=self.dummy_decoder
      a_       		=self.dummy_text_proj
      a_       		=self.dummy_text_encoder
      a_       		=self.dummy_tokenizer
      a_       		=self.dummy_super_res_first
      a_       		=self.dummy_super_res_last

      a_       		=UnCLIPScheduler(
          variance_type="learned_range"							,  prediction_type="epsilon"							,  num_train_timesteps=1_0_0_0							,  )

      a_       		=UnCLIPScheduler(
          variance_type="fixed_small_log"							,  prediction_type="epsilon"							,  num_train_timesteps=1_0_0_0							,  )

      a_       		=CLIPImageProcessor(crop_size=3_2							,  size=3_2)

      a_       		=self.dummy_image_encoder

      return {
          "decoder": decoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_proj": text_proj,
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=0							,  lowerCAmelCase_=True) ->      List[Any]:



      """simple docstring"""







      a_       		=floats_tensor((1, 3, 3_2, 3_2)							,  rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
      if str(lowerCAmelCase_).startswith("mps"):
           a_       		=torch.manual_seed(lowerCAmelCase_)
      else:
           a_       		=torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)

      if pil_image:
           a_       		=input_image * 0.5 + 0.5
           a_       		=input_image.clamp(0							,  1)
           a_       		=input_image.cpu().permute(0							,  2							,  3							,  1).float().numpy()
           a_       		=DiffusionPipeline.numpy_to_pil(lowerCAmelCase_)[0]

      return {
          "image": input_image,
          "generator": generator,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "np",
      }




 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      a_       		="cpu"

      a_       		=self.get_dummy_components()

      a_       		=self.pipeline_class(**lowerCAmelCase_)
      a_       		=pipe.to(lowerCAmelCase_)

      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)

      a_       		=pipe(**lowerCAmelCase_)
      a_       		=output.images

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)

      a_       		=pipe(
          **lowerCAmelCase_							,  return_dict=lowerCAmelCase_							,  )[0]

      a_       		=image[0, -3:, -3:, -1]
      a_       		=image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      a_       		=np.array(
          [
              0.9_9_9_7,
              0.0_0_0_2,
              0.9_9_9_7,
              0.9_9_9_7,
              0.9_9_6_9,
              0.0_0_2_3,
              0.9_9_9_7,
              0.9_9_6_9,
              0.9_9_7_0,
          ])

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




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		="cpu"

      a_       		=self.get_dummy_components()

      a_       		=self.pipeline_class(**lowerCAmelCase_)
      a_       		=pipe.to(lowerCAmelCase_)

      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)

      a_       		=pipe(**lowerCAmelCase_)
      a_       		=output.images

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)

      a_       		=pipe(
          **lowerCAmelCase_							,  return_dict=lowerCAmelCase_							,  )[0]

      a_       		=image[0, -3:, -3:, -1]
      a_       		=image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      a_       		=np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])

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




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		="cpu"

      a_       		=self.get_dummy_components()

      a_       		=self.pipeline_class(**lowerCAmelCase_)
      a_       		=pipe.to(lowerCAmelCase_)

      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)
      a_       		=[
          pipeline_inputs["image"],
          pipeline_inputs["image"],
      ]

      a_       		=pipe(**lowerCAmelCase_)
      a_       		=output.images

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)
      a_       		=[
          tuple_pipeline_inputs["image"],
          tuple_pipeline_inputs["image"],
      ]

      a_       		=pipe(
          **lowerCAmelCase_							,  return_dict=lowerCAmelCase_							,  )[0]

      a_       		=image[0, -3:, -3:, -1]
      a_       		=image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (2, 6_4, 6_4, 3)

      a_       		=np.array(
          [
              0.9_9_9_7,
              0.9_9_8_9,
              0.0_0_0_8,
              0.0_0_2_1,
              0.9_9_6_0,
              0.0_0_1_8,
              0.0_0_1_4,
              0.0_0_0_2,
              0.9_9_3_3,
          ])

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




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      a_       		=torch.device("cpu")







      class 	UpperCAmelCase	:


       '''simple docstring'''




       __magic_name__					: Optional[int]  =      1



      a_       		=self.get_dummy_components()

      a_       		=self.pipeline_class(**lowerCAmelCase_)
      a_       		=pipe.to(lowerCAmelCase_)

      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=torch.Generator(device=lowerCAmelCase_).manual_seed(0)
      a_       		=pipe.decoder.dtype
      a_       		=1

      a_       		=(
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      a_       		=pipe.prepare_latents(
          lowerCAmelCase_							,  dtype=lowerCAmelCase_							,  device=lowerCAmelCase_							,  generator=lowerCAmelCase_							,  latents=lowerCAmelCase_							,  scheduler=DummyScheduler())

      a_       		=(
          batch_size,
          pipe.super_res_first.config.in_channels // 2,
          pipe.super_res_first.config.sample_size,
          pipe.super_res_first.config.sample_size,
      )
      a_       		=pipe.prepare_latents(
          lowerCAmelCase_							,  dtype=lowerCAmelCase_							,  device=lowerCAmelCase_							,  generator=lowerCAmelCase_							,  latents=lowerCAmelCase_							,  scheduler=DummyScheduler())

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)

      a_       		=pipe(
          **lowerCAmelCase_							,  decoder_latents=lowerCAmelCase_							,  super_res_latents=lowerCAmelCase_).images

      a_       		=self.get_dummy_inputs(lowerCAmelCase_							,  pil_image=lowerCAmelCase_)
      # Don't pass image, instead pass embedding
      a_       		=pipeline_inputs.pop("image")
      a_       		=pipe.image_encoder(lowerCAmelCase_).image_embeds

      a_       		=pipe(
          **lowerCAmelCase_							,  decoder_latents=lowerCAmelCase_							,  super_res_latents=lowerCAmelCase_							,  image_embeddings=lowerCAmelCase_							,  ).images

      # make sure passing text embeddings manually is identical
      assert np.abs(img_out_a - img_out_a).max() < 1e-4




 @skip_mps
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=torch_device == "cpu"

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      a_       		=1e-2

      self._test_attention_slicing_forward_pass(
          test_max_difference=lowerCAmelCase_							,  expected_max_diff=lowerCAmelCase_)




 @skip_mps
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=torch_device == "cpu"
      a_       		=True
      a_       		=[
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=lowerCAmelCase_							,  relax_max_difference=lowerCAmelCase_							,  additional_params_copy_to_batched_inputs=lowerCAmelCase_							,  )




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      a_       		=[
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      if torch_device == "mps":
           # TODO: MPS errors with larger batch sizes
           a_       		=[2, 3]
           self._test_inference_batch_consistent(
               batch_sizes=lowerCAmelCase_							,  additional_params_copy_to_batched_inputs=lowerCAmelCase_							,  )
      else:
           self._test_inference_batch_consistent(
               additional_params_copy_to_batched_inputs=lowerCAmelCase_)




 @skip_mps
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      return super().test_save_load_local()




 @skip_mps
 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      return super().test_save_load_optional_components()







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


 '''simple docstring'''




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      a_       		=load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png")
      a_       		=load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_cat_variation_fp16.npy")

      a_       		=UnCLIPImageVariationPipeline.from_pretrained(
          "kakaobrain/karlo-v1-alpha-image-variations"							,  torch_dtype=torch.floataa)
      a_       		=pipeline.to(lowerCAmelCase_)
      pipeline.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=torch.Generator(device="cpu").manual_seed(0)
      a_       		=pipeline(
          lowerCAmelCase_							,  generator=lowerCAmelCase_							,  output_type="np"							,  )

      a_       		=output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)

      assert_mean_pixel_difference(lowerCAmelCase_							,  lowerCAmelCase_							,  1_5)







'''simple docstring'''


from __future__ import annotations

lowercase							       =			[]






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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


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

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


