






'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					DiTPipeline
		_UpperCamelCase				  =					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		_UpperCamelCase				  =					PipelineTesterMixin.required_optional_params - {
		    'latents',
		    'num_images_per_prompt',
		    'callback',
		    'callback_steps',
		}
		_UpperCamelCase				  =					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								torch.manual_seed(0       )
								lowerCamelCase__         =  TransformeraDModel(
								    sample_size=16   ,num_layers=2   ,patch_size=4   ,attention_head_dim=8   ,num_attention_heads=2   ,in_channels=4   ,out_channels=8   ,attention_bias=_lowerCAmelCase   ,activation_fn="""gelu-approximate"""   ,num_embeds_ada_norm=10_00   ,norm_type="""ada_norm_zero"""   ,norm_elementwise_affine=_lowerCAmelCase   ,)
								lowerCamelCase__         =  AutoencoderKL()
								lowerCamelCase__         =  DDIMScheduler()
								lowerCamelCase__         =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
								return components






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=0       ):
								if str(_lowerCAmelCase       ).startswith("""mps"""       ):
														lowerCamelCase__         =  torch.manual_seed(_lowerCAmelCase       )
								else:
														lowerCamelCase__         =  torch.Generator(device=_lowerCAmelCase       ).manual_seed(_lowerCAmelCase       )
								lowerCamelCase__         =  {
								    """class_labels""": [1],
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}
								return inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """cpu"""

								lowerCamelCase__         =  self.get_dummy_components()
								lowerCamelCase__         =  self.pipeline_class(**_lowerCAmelCase       )
								pipe.to(_lowerCAmelCase       )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

								lowerCamelCase__         =  self.get_dummy_inputs(_lowerCAmelCase       )
								lowerCamelCase__         =  pipe(**_lowerCAmelCase       ).images
								lowerCamelCase__         =  image[0, -3:, -3:, -1]

								self.assertEqual(image.shape   ,(1, 16, 16, 3)       )
								lowerCamelCase__         =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]       )
								lowerCamelCase__         =  np.abs(image_slice.flatten() - expected_slice       ).max()
								self.assertLessEqual(_lowerCAmelCase   ,1E-3       )






		def 			UpperCamelCase_					(      self       ):
								self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase   ,expected_max_diff=1E-3       )






		@unittest.skipIf(
		    torch_device != """cuda""" or not is_xformers_available()   ,reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,)
		def 			UpperCamelCase_					(      self       ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




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




		'''simple docstring'''





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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  torch.manual_seed(0       )

								lowerCamelCase__         =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""       )
								pipe.to("""cuda"""       )

								lowerCamelCase__         =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
								lowerCamelCase__         =  pipe.get_label_ids(_lowerCAmelCase       )

								lowerCamelCase__         =  pipe(_lowerCAmelCase   ,generator=_lowerCAmelCase   ,num_inference_steps=40   ,output_type="""np"""       ).images

								for word, image in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  load_numpy(
														    F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''       )
														assert np.abs((expected_image - image).max()       ) < 1E-2






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""       )
								lowerCamelCase__         =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
								pipe.to("""cuda"""       )

								lowerCamelCase__         =  ["""vase""", """umbrella"""]
								lowerCamelCase__         =  pipe.get_label_ids(_lowerCAmelCase       )

								lowerCamelCase__         =  torch.manual_seed(0       )
								lowerCamelCase__         =  pipe(_lowerCAmelCase   ,generator=_lowerCAmelCase   ,num_inference_steps=25   ,output_type="""np"""       ).images

								for word, image in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    F'''/dit/{word}_512.npy'''       )

														assert np.abs((expected_image - image).max()       ) < 1E-1









'''simple docstring'''







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

UpperCamelCase    :      str  			=		(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase    :      list[int]  			=		[ord(letter) for letter in string.ascii_lowercase]
UpperCamelCase    :      set[int]  			=		{ord(char) for char in VALID_CHARS}

UpperCamelCase    :      list[str]  			=		["the", "be", "to", "of", "and", "in", "that", "have"]
def 						A__			(				__lowerCAmelCase		:			list[int]   ,    __lowerCAmelCase		:			tuple[int, ...]						):
						lowerCamelCase__         =  ""
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42

						for keychar, cipherchar in zip(cycle(__lowerCAmelCase						)   ,    __lowerCAmelCase						):
												lowerCamelCase__         =  cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__lowerCAmelCase						)

						return decoded
def 						A__			(				__lowerCAmelCase		:			list[int]						):
						lowerCamelCase__         =  []
						for key in product(__lowerCAmelCase   ,    repeat=3						):
												lowerCamelCase__         =  try_key(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if encoded is not None:
																		possibles.append(__lowerCAmelCase						)
						return possibles
def 						A__			(				__lowerCAmelCase		:			list[str]   ,    __lowerCAmelCase		:			str						):
						return [possible for possible in possibles if common_word in possible.lower()]
def 						A__			(				__lowerCAmelCase		:			str = "p059_cipher.txt"						):
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  Path(__lowerCAmelCase						).parent.joinpath(__lowerCAmelCase						).read_text(encoding="""utf-8"""						)

						lowerCamelCase__         =  [int(__lowerCAmelCase						) for number in data.strip().split(""","""						)]

						lowerCamelCase__         =  filter_valid_chars(__lowerCAmelCase						)
						for common_word in COMMON_WORDS:
												lowerCamelCase__         =  filter_common_word(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if len(__lowerCAmelCase						) == 1:
																		break

						lowerCamelCase__         =  possibles[0]
						return sum(ord(__lowerCAmelCase						) for char in decoded_text						)


if __name__ == "__main__":
							print(F'{solution() = }')

