





'''simple docstring'''

from __future__ import annotations





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					list[str] | None = None						,       _lowerCamelCase					:					dict[str, float] | None = None						,       _lowerCamelCase					:					bool = False						,       ):
    A__				=			cipher_alphabet or [chr(_lowerCamelCase       ) for i in range(97						,       1_23       )]

    # 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)
        A__				=			{
            "a": 0.0_8_4_9_7,
            "b": 0.0_1_4_9_2,
            "c": 0.0_2_2_0_2,
            "d": 0.0_4_2_5_3,
            "e": 0.1_1_1_6_2,
            "f": 0.0_2_2_2_8,
            "g": 0.0_2_0_1_5,
            "h": 0.0_6_0_9_4,
            "i": 0.0_7_5_4_6,
            "j": 0.0_0_1_5_3,
            "k": 0.0_1_2_9_2,
            "l": 0.0_4_0_2_5,
            "m": 0.0_2_4_0_6,
            "n": 0.0_6_7_4_9,
            "o": 0.0_7_5_0_7,
            "p": 0.0_1_9_2_9,
            "q": 0.0_0_0_9_5,
            "r": 0.0_7_5_8_7,
            "s": 0.0_6_3_2_7,
            "t": 0.0_9_3_5_6,
            "u": 0.0_2_7_5_8,
            "v": 0.0_0_9_7_8,
            "w": 0.0_2_5_6_0,
            "x": 0.0_0_1_5_0,
            "y": 0.0_1_9_9_4,
            "z": 0.0_0_0_7_7,
        }
    else:
        # Custom frequencies dictionary
        A__				=			frequencies_dict

    if not case_sensitive:
        A__				=			ciphertext.lower()

    # Chi squared statistic values
    A__				=			{}

    # cycle through all of the shifts
    for shift in range(len(_lowerCamelCase       )       ):
        A__				=			""

        # decrypt the message with the shift
        for letter in ciphertext:
            try:
                # Try to index the letter in the alphabet
                A__				=			(alphabet_letters.index(letter.lower()       ) - shift) % len(
                    _lowerCamelCase       )
                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

        A__				=			0.0

        # Loop through each letter in the decoded message with the shift
        for letter in decrypted_with_shift:
            if case_sensitive:
                A__				=			letter.lower()
                if letter in frequencies:
                    # Get the amount of times the letter occurs in the message
                    A__				=			decrypted_with_shift.lower().count(_lowerCamelCase       )

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

                    # Complete the chi squared statistic formula
                    A__				=			((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
                    A__				=			decrypted_with_shift.count(_lowerCamelCase       )

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

                    # Complete the chi squared statistic formula
                    A__				=			((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
        A__				=			(
            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(_lowerCamelCase					:					int       ) -> tuple[float, str]:
        return chi_squared_statistic_values[key]

    A__				=			min(
        _lowerCamelCase						,       key=_lowerCamelCase						,       )

    # Get all the data from the most likely cipher (key, decoded message)
    (
        (
        A__
    ),	(
        A__
    ),	
    )				=			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,
    )










'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class    UpperCAmelCase	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				KandinskyVaaPipeline
 __lowercase   		=				[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __lowercase   		=				["""image_embeds""", """negative_image_embeds"""]
 __lowercase   		=				[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Dict:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Union[str, Any]:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     torch.manual_seed(0							)

     A__				=			{
         "in_channels": 4,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "image",
         "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,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     A__				=			UNetaDConditionModel(**lowercase_							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }
 @property
 def      UpperCAmelCase_							(				self      :Dict							)->							Union[str, Any]:
     torch.manual_seed(0							)
     A__				=			VQModel(**self.dummy_movq_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :Dict							)->							Optional[Any]:
     A__				=			self.dummy_unet
     A__				=			self.dummy_movq

     A__				=			DDIMScheduler(
         num_train_timesteps=10_00						,       beta_schedule="linear"						,       beta_start=0.0_0_0_8_5						,       beta_end=0.0_1_2						,       clip_sample=lowercase_						,       set_alpha_to_one=lowercase_						,       steps_offset=1						,       prediction_type="epsilon"						,       thresholding=lowercase_						,       )

     A__				=			{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :int						,       lowercase_      :Union[str, Any]=0							)->							Dict:
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(seed + 1							)							).to(
         lowercase_							)
     if str(lowercase_							).startswith("mps"							):
         A__				=			torch.manual_seed(lowercase_							)
     else:
         A__				=			torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
     A__				=			{
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

     A__				=			self.pipeline_class(**lowercase_							)
     A__				=			pipe.to(lowercase_							)

     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			pipe(**self.get_dummy_inputs(lowercase_							)							)
     A__				=			output.images

     A__				=			pipe(
         **self.get_dummy_inputs(lowercase_							)						,       return_dict=lowercase_						,       )[0]

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

     assert image.shape == (1, 64, 64, 3)

     A__				=			np.array(
         [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]							)

     assert (
         np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     A__				=			load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"							)

     A__				=			KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"						,       torch_dtype=torch.floataa							)
     pipe_prior.to(lowercase_							)

     A__				=			KandinskyVaaPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder"						,       torch_dtype=torch.floataa							)
     A__				=			pipeline.to(lowercase_							)
     pipeline.set_progress_bar_config(disable=lowercase_							)

     A__				=			"red cat, 4k photo"

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__,	A__				=			pipe_prior(
         lowercase_						,       generator=lowercase_						,       num_inference_steps=5						,       negative_prompt=""						,       ).to_tuple()

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__				=			pipeline(
         image_embeds=lowercase_						,       negative_image_embeds=lowercase_						,       generator=lowercase_						,       num_inference_steps=1_00						,       output_type="np"						,       )

     A__				=			output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowercase_						,       lowercase_							)



