
"""simple docstring"""






from __future__ import annotations






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  list[str] | None = None      ,     lowercase__		:  dict[str, float] | None = None      ,     lowercase__		:  bool = False      ,     )  ->				tuple[int, float, str]:


			'''simple docstring'''





			lowerCAmelCase_    :Optional[Any]			     =  cipher_alphabet or [chr(lowercase__		) for i in range(9_7      ,     1_2_3		)]

			# If the argument is None or the user provided an empty dictionary
			if not frequencies_dict:
						# Frequencies of letters in the english language (how much they show up)
						lowerCAmelCase_    :Optional[Any]			     =  {
						    """a""": 0.08497,
						    """b""": 0.01492,
						    """c""": 0.02202,
						    """d""": 0.04253,
						    """e""": 0.11162,
						    """f""": 0.02228,
						    """g""": 0.02015,
						    """h""": 0.06094,
						    """i""": 0.07546,
						    """j""": 0.00153,
						    """k""": 0.01292,
						    """l""": 0.04025,
						    """m""": 0.02406,
						    """n""": 0.06749,
						    """o""": 0.07507,
						    """p""": 0.01929,
						    """q""": 0.00095,
						    """r""": 0.07587,
						    """s""": 0.06327,
						    """t""": 0.09356,
						    """u""": 0.02758,
						    """v""": 0.00978,
						    """w""": 0.02560,
						    """x""": 0.00150,
						    """y""": 0.01994,
						    """z""": 0.00077,
						}
			else:
						# Custom frequencies dictionary
						lowerCAmelCase_    :List[str]			     =  frequencies_dict

			if not case_sensitive:
						lowerCAmelCase_    :Optional[Any]			     =  ciphertext.lower()

			# Chi squared statistic values
			lowerCAmelCase_    :dict[int, tuple[float, str]]			     =  {}

			# cycle through all of the shifts
			for shift in range(len(lowercase__		)		):
						lowerCAmelCase_    :Tuple			     =  """"""

						# decrypt the message with the shift
						for letter in ciphertext:
									try:
												# Try to index the letter in the alphabet
												lowerCAmelCase_    :Any			     =  (alphabet_letters.index(letter.lower()		) - shift) % len(
												    lowercase__		)
												decrypted_with_shift += (
												    alphabet_letters[new_key].upper()
												    if case_sensitive and letter.isupper()
												    else alphabet_letters[new_key]
												)
									except ValueError:
												# Append the character if it isn't in the alphabet
												decrypted_with_shift += letter

						lowerCAmelCase_    :Optional[Any]			     =  0.0

						# Loop through each letter in the decoded message with the shift
						for letter in decrypted_with_shift:
									if case_sensitive:
												lowerCAmelCase_    :List[str]			     =  letter.lower()
												if letter in frequencies:
															# Get the amount of times the letter occurs in the message
															lowerCAmelCase_    :Tuple			     =  decrypted_with_shift.lower().count(lowercase__		)

															# Get the excepcted amount of times the letter should appear based
															# on letter frequencies
															lowerCAmelCase_    :List[Any]			     =  frequencies[letter] * occurrences

															# Complete the chi squared statistic formula
															lowerCAmelCase_    :Optional[Any]			     =  ((occurrences - expected) ** 2) / expected

															# Add the margin of error to the total chi squared statistic
															chi_squared_statistic += chi_letter_value
									else:
												if letter.lower() in frequencies:
															# Get the amount of times the letter occurs in the message
															lowerCAmelCase_    :List[Any]			     =  decrypted_with_shift.count(lowercase__		)

															# Get the excepcted amount of times the letter should appear based
															# on letter frequencies
															lowerCAmelCase_    :str			     =  frequencies[letter] * occurrences

															# Complete the chi squared statistic formula
															lowerCAmelCase_    :Tuple			     =  ((occurrences - expected) ** 2) / expected

															# Add the margin of error to the total chi squared statistic
															chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
						lowerCAmelCase_    :Dict			     =  (
						    chi_squared_statistic,
						    decrypted_with_shift,
						)

			# Get the most likely cipher by finding the cipher with the smallest chi squared
			# statistic
			def chi_squared_statistic_values_sorting_key(lowercase__		:  int		) -> tuple[float, str]:
						return chi_squared_statistic_values[key]

			lowerCAmelCase_    :int			     =  min(
			    lowercase__      ,     key=lowercase__      ,     )

			# Get all the data from the most likely cipher (key, decoded message)
			(
			    (
			    lowerCAmelCase_
			)     ,       (
			    lowerCAmelCase_
			)     ,       
			)    :Union[str, Any]			     =  chi_squared_statistic_values[most_likely_cipher]

			# Return the data on the most likely shift
			return (
			    most_likely_cipher,
			    most_likely_cipher_chi_squared_value,
			    decoded_most_likely_cipher,
			)












import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size		,      image_size=64		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=37		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 16,
								    """embedding_dim""": self.time_input_dim,
								    """num_embeddings""": 32,
								    """embedding_proj_dim""": self.text_embedder_hidden_size,
								    """time_embed_dim""": self.time_embed_dim,
								    """num_layers""": 1,
								    """clip_embed_dim""": self.time_input_dim * 2,
								    """additional_embeddings""": 0,
								    """time_embed_act_fn""": """gelu""",
								    """norm_in_type""": """layer""",
								    """embedding_proj_norm_type""": """layer""",
								    """encoder_hid_proj_type""": None,
								    """added_emb_type""": None,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """param_shapes""": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    """d_latent""": self.time_input_dim,
								    """d_hidden""": self.renderer_dim,
								    """n_output""": 12,
								    """background""": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							pipe(**self.get_dummy_inputs(__magic_name__				)				)
								a										=							output.images[0]
								a										=							image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

								for key in inputs.keys():
									if key in self.batch_params:
										a										=							batch_size * [inputs[key]]

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""				)
								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""				)
								a										=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      guidance_scale=3.0		,      num_inference_steps=64		,      frame_size=64		,      output_type="""np"""		,      ).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__magic_name__		,      __magic_name__				)

