

'''simple docstring'''






import random
import unittest

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

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,unittest.TestCase      ):
    _A			    =							KandinskyVaaPriorEmbaEmbPipeline
    _A			    =							["prompt", "image"]
    _A			    =							["prompt", "image"]
    _A			    =							[
        "num_images_per_prompt",
        "strength",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    _A			    =							False




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 32




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 32




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.time_input_dim




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.time_input_dim * 4




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 100




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          torch.manual_seed(0			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       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=37				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1000				,     )
          return CLIPTextModelWithProjection(lowercase__			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Any     =       {
              "num_attention_heads": 2,
              "attention_head_dim": 12,
              "embedding_dim": self.text_embedder_hidden_size,
              "num_layers": 1,
          }

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       PriorTransformer(**lowercase__			)
          # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
          SCREAMING_SNAKE_CASE_      :			Dict     =       nn.Parameter(torch.ones(model.clip_std.shape			)			)
          return model




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          torch.manual_seed(0			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       CLIPVisionModelWithProjection(lowercase__			)
          return model




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       CLIPImageProcessor(
              crop_size=224				,     do_center_crop=lowercase__				,     do_normalize=lowercase__				,     do_resize=lowercase__				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

          return image_processor




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Dict     =       self.dummy_prior
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.dummy_image_encoder
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_text_encoder
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.dummy_tokenizer
          SCREAMING_SNAKE_CASE_      :			int     =       self.dummy_image_processor

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       UnCLIPScheduler(
              variance_type="fixed_small_log"				,     prediction_type="sample"				,     num_train_timesteps=1000				,     clip_sample=lowercase__				,     clip_sample_range=10.0				,     )

          SCREAMING_SNAKE_CASE_      :			Tuple     =       {
              "prior": prior,
              "image_encoder": image_encoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "scheduler": scheduler,
              "image_processor": image_processor,
          }

          return components




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__=0			):






          """simple docstring"""






          if str(lowercase__			).startswith("mps"			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.manual_seed(lowercase__			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.Generator(device=lowercase__			).manual_seed(lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       floats_tensor((1, 3, 64, 64)				,     rng=random.Random(lowercase__			)			).to(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			str     =       image.cpu().permute(0				,     2				,     3				,     1			)[0]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       Image.fromarray(np.uinta(lowercase__			)			).convert("RGB"			).resize((256, 256)			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       {
              "prompt": "horse",
              "image": init_image,
              "strength": 0.5,
              "generator": generator,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       "cpu"

          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.get_dummy_components()

          SCREAMING_SNAKE_CASE_      :			str     =       self.pipeline_class(**lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       pipe.to(lowercase__			)

          pipe.set_progress_bar_config(disable=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       pipe(**self.get_dummy_inputs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			str     =       output.image_embeds

          SCREAMING_SNAKE_CASE_      :			Dict     =       pipe(
              **self.get_dummy_inputs(lowercase__			)				,     return_dict=lowercase__				,     )[0]

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       image[0, -10:]
          SCREAMING_SNAKE_CASE_      :			Any     =       image_from_tuple[0, -10:]

          assert image.shape == (1, 32)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       np.array(
              [
                  0.1071284,
                  1.3330271,
                  0.61260223,
                  -0.6691065,
                  -0.3846852,
                  -1.0303661,
                  0.22716111,
                  0.03348901,
                  0.30040675,
                  -0.24805029,
              ]			)

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




    @skip_mps
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       torch_device == "cpu"
          SCREAMING_SNAKE_CASE_      :			str     =       True
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       False

          self._test_inference_batch_single_identical(
              test_max_difference=lowercase__				,     relax_max_difference=lowercase__				,     test_mean_pixel_difference=lowercase__				,     )







    @skip_mps
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch_device == "cpu"
          SCREAMING_SNAKE_CASE_      :			List[str]     =       False

          self._test_attention_slicing_forward_pass(
              test_max_difference=lowercase__				,     test_mean_pixel_difference=lowercase__				,     )








'''simple docstring'''







from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					list:
			seed(1	)
			return [gauss(lowercase					,	lowercase	) for _ in range(lowercase	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					list:
			return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # An empty list to store all squared differences
			# iterate over number of elements in items
			for i in range(len(lowercase	)	):
						# for loop iterates over number of elements in inner layer of items
						for j in range(len(items[i]	)	):
									# appending squared differences to 'squared_diff' list
									squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# for loop iterates over number of elements in list
			for i in range(len(lowercase	)	):
						# for loop iterates over number of inner items of each element
						for j in range(len(x_items[i]	)	):
									__lowerCAmelCase											=				[]  # to store all discriminant values of each item as a list
									# for loop iterates over number of classes we have in our dataset
									for k in range(len(lowercase	)	):
												# appending values of discriminants for each class to 'temp' list
												temp.append(
												    x_items[i][j] * (means[k] / variance)
												    - (means[k] ** 2 / (2 * variance))
												    + log(probabilities[k]	)	)
									# appending discriminant values of each item to 'results' list
									results.append(lowercase	)

			return [result.index(max(lowercase	)	) for result in results]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				sum(1 for i, j in zip(lowercase					,	lowercase	) if i == j	)
			# percentage of accuracy equals to number of correct predictions divided by number
			# of all data and multiplied by 100
			return (correct / len(lowercase	)) * 100


_a					: Optional[int]      =    TypeVar("""num""")





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase = lambda lowercase	: True					,	lowercase = None					,	)					->					num:
			while True:
						try:
									__lowerCAmelCase											=				input_type(input(lowercase	).strip() or default	)
									if condition(lowercase	):
												return user_input
									else:
												print(f'{user_input}: {err_msg}'	)
												continue
						except ValueError:
									print(
									    f'{user_input}: Incorrect input type, expected {input_type.__name__!r}'	)





def    _lowerCAmelCase						(						)					->					List[str]:
			while True:
						print(""" Linear Discriminant Analysis """.center(50					,	"""*"""	)	)
						print("""*""" * 50					,	"""\n"""	)
						print("""First of all we should specify the number of classes that"""	)
						print("""we want to generate as training dataset"""	)
						# Trying to get number of classes
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg="""Enter the number of classes (Data Groupings): """					,	err_msg="""Number of classes should be positive!"""					,	)

						print("""-""" * 100	)

						# Trying to get the value of standard deviation
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x >= 0					,	input_msg=(
						        """Enter the value of standard deviation"""
						        """(Default value is 1.0 for all classes): """
						    )					,	err_msg="""Standard deviation should not be negative!"""					,	default="""1.0"""					,	)

						print("""-""" * 100	)

						# Trying to get number of instances in classes and theirs means to generate
						# dataset
						__lowerCAmelCase											=				[]  # An empty list to store instance counts of classes in dataset
						for i in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg=(f'Enter The number of instances for class_{i+1}: ')					,	err_msg="""Number of instances should be positive!"""					,	)
									counts.append(lowercase	)
						print("""-""" * 100	)

						# An empty list to store values of user-entered means of classes
						__lowerCAmelCase											=				[]
						for a in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	input_msg=(f'Enter the value of mean for class_{a+1}: ')					,	err_msg="""This is an invalid value."""					,	)
									user_means.append(lowercase	)
						print("""-""" * 100	)

						print("""Standard deviation: """					,	lowercase	)
						# print out the number of instances in classes in separated line
						for i, count in enumerate(lowercase					,	1	):
									print(f'Number of instances in class_{i} is: {count}'	)
						print("""-""" * 100	)

						# print out mean values of classes separated line
						for i, user_mean in enumerate(lowercase					,	1	):
									print(f'Mean of class_{i} is: {user_mean}'	)
						print("""-""" * 100	)

						# Generating training dataset drawn from gaussian distribution
						__lowerCAmelCase											=				[
						    gaussian_distribution(user_means[j]					,	lowercase					,	counts[j]	)
						    for j in range(lowercase	)
						]
						print("""Generated Normal Distribution: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Generating Ys to detecting corresponding classes
						__lowerCAmelCase											=				y_generator(lowercase					,	lowercase	)
						print("""Generated Corresponding Ys: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[calculate_mean(counts[k]					,	x[k]	) for k in range(lowercase	)]
						# for loop iterates over number of elements in 'actual_means' list and print
						# out them in separated line
						for i, actual_mean in enumerate(lowercase					,	1	):
									print(f'Actual(Real) mean of class_{i} is: {actual_mean}'	)
						print("""-""" * 100	)

						# Calculating the value of probabilities for each class
						__lowerCAmelCase											=				[
						    calculate_probabilities(counts[i]					,	sum(lowercase	)	) for i in range(lowercase	)
						]

						# for loop iterates over number of elements in 'probabilities' list and print
						# out them in separated line
						for i, probability in enumerate(lowercase					,	1	):
									print(f'Probability of class_{i} is: {probability}'	)
						print("""-""" * 100	)

						# Calculating the values of variance for each class
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						__lowerCAmelCase											=				predict_y_values(lowercase					,	lowercase					,	lowercase					,	lowercase	)
						print("""-""" * 100	)

						# Calculating Accuracy of the model
						print(f'Accuracy: {accuracy(lowercase					,	lowercase	)}'	)
						print("""-""" * 100	)
						print(""" DONE """.center(100					,	"""+"""	)	)

						if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
									print("""\n""" + """GoodBye!""".center(100					,	"""-"""	) + """\n"""	)
									break
						system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
	main()

