






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       snake_case__ (				__lowercase						,     __lowercase						,     __lowercase						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					str								=			StableUnCLIPPipeline
       lowerCamelCase				:					str								=			TEXT_TO_IMAGE_PARAMS
       lowerCamelCase				:					int								=			TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCamelCase				:					Dict								=			TEXT_TO_IMAGE_IMAGE_PARAMS
       lowerCamelCase				:					List[Any]								=			TEXT_TO_IMAGE_IMAGE_PARAMS

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



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Tuple 							=    32
            __snake_case      :List[Any] 							=    embedder_hidden_size

            # prior components

            torch.manual_seed(0     )
            __snake_case      :Dict 							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

            torch.manual_seed(0     )
            __snake_case      :int 							=    CLIPTextModelWithProjection(
                CLIPTextConfig(
                    bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=_lowerCAmelCase      ,    projection_dim=_lowerCAmelCase      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=10_00      ,    )     )

            torch.manual_seed(0     )
            __snake_case      :str 							=    PriorTransformer(
                num_attention_heads=2      ,    attention_head_dim=12      ,    embedding_dim=_lowerCAmelCase      ,    num_layers=1      ,    )

            torch.manual_seed(0     )
            __snake_case      :Optional[int] 							=    DDPMScheduler(
                variance_type="""fixed_small_log"""      ,    prediction_type="""sample"""      ,    num_train_timesteps=10_00      ,    clip_sample=_lowerCAmelCase      ,    clip_sample_range=5.0      ,    beta_schedule="""squaredcos_cap_v2"""      ,    )

            # regular denoising components

            torch.manual_seed(0     )
            __snake_case      :List[Any] 							=    StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase     )
            __snake_case      :Tuple 							=    DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""     )

            torch.manual_seed(0     )
            __snake_case      :str 							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

            torch.manual_seed(0     )
            __snake_case      :Union[str, Any] 							=    CLIPTextModel(
                CLIPTextConfig(
                    bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=_lowerCAmelCase      ,    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=10_00      ,    )     )

            torch.manual_seed(0     )
            __snake_case      :str 							=    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=_lowerCAmelCase      ,    layers_per_block=1      ,    upcast_attention=_lowerCAmelCase      ,    use_linear_projection=_lowerCAmelCase      ,    )

            torch.manual_seed(0     )
            __snake_case      :Optional[int] 							=    DDIMScheduler(
                beta_schedule="""scaled_linear"""      ,    beta_start=0.0_00_85      ,    beta_end=0.0_12      ,    prediction_type="""v_prediction"""      ,    set_alpha_to_one=_lowerCAmelCase      ,    steps_offset=1      ,    )

            torch.manual_seed(0     )
            __snake_case      :int 							=    AutoencoderKL()

            __snake_case      :Dict 							=    {
                # 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        __lowercase    (				self      ,    a__      ,    a__=0     )			->   Optional[int]:






            '''simple docstring'''





            if str(_lowerCAmelCase     ).startswith("""mps"""     ):
                 __snake_case      :List[str] 							=    torch.manual_seed(_lowerCAmelCase     )
            else:
                 __snake_case      :List[str] 							=    torch.Generator(device=_lowerCAmelCase     ).manual_seed(_lowerCAmelCase     )
            __snake_case      :List[str] 							=    {
                '''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        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :str 							=    torch_device == '''cpu'''

            self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :List[str] 							=    torch_device in ['''cpu''', '''mps''']

            self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase     )




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

       '''simple docstring'''



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






            '''simple docstring'''





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



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""     )

            __snake_case      :Optional[int] 							=    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()

            __snake_case      :Tuple 							=    torch.Generator(device="""cpu"""     ).manual_seed(0     )
            __snake_case      :List[str] 							=    pipe("""anime turle"""      ,    generator=_lowerCAmelCase      ,    output_type="""np"""     )

            __snake_case      :Optional[int] 							=    output.images[0]

            assert image.shape == (7_68, 7_68, 3)

            assert_mean_pixel_difference(_lowerCAmelCase      ,    _lowerCAmelCase     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

            __snake_case      :int 							=    StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      ,    torch_dtype=torch.floataa     )
            __snake_case      :Union[str, Any] 							=    pipe.to(_lowerCAmelCase     )
            pipe.set_progress_bar_config(disable=_lowerCAmelCase     )
            pipe.enable_attention_slicing()
            pipe.enable_sequential_cpu_offload()

            __snake_case      :Dict 							=    pipe(
                """anime turtle"""      ,    prior_num_inference_steps=2      ,    num_inference_steps=2      ,    output_type="""np"""      ,    )

            __snake_case      :Union[str, Any] 							=    torch.cuda.max_memory_allocated()
            # make sure that less than 7 GB is allocated
            assert mem_bytes < 7 * 10**9









from __future__ import annotations






def 		lowerCAmelCase  (	UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[list[str]]       ,			UpperCamelCase__   :   int       ,			)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   len(UpperCamelCase__					)

						# If row is equal to the size of the board it means there are a queen in each row in
						# the current board (possible_board)
						if row == n:
												# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
												# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
												boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board]					)
												return

						# We iterate each column in the row to find all possible results in each row
						for col in range(UpperCamelCase__					):
												# We apply that we learned previously. First we check that in the current board
												# (possible_board) there are not other same value because if there is it means
												# that there are a collision in vertical. Then we apply the two formulas we
												# learned before:
												#
												# 45º: y - x = b or 45: row - col = b
												# 135º: y + x = b or row + col = b.
												#
												# And we verify if the results of this two formulas not exist in their variables
												# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
												#
												# If any or these are True it means there is a collision so we continue to the
												# next value in the for loop.
												if (
												    col in possible_board
												    or row - col in diagonal_right_collisions
												    or row + col in diagonal_left_collisions
												):
																		continue

												# If it is False we call dfs function again and we update the inputs
												depth_first_search(
												    [*possible_board, col]       ,			[*diagonal_right_collisions, row - col]       ,			[*diagonal_left_collisions, row + col]       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)






def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				list[list[str]]           =   []
						depth_first_search([]       ,			[]       ,			[]       ,			UpperCamelCase__       ,			UpperCamelCase__					)

						# Print all the boards
						for board in boards:
												for column in board:
																		print(UpperCamelCase__					)
												print(''''''					)

						print(len(UpperCamelCase__					)       ,			'''solutions were found.'''					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							n_queens_solution(4)



