




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_						=False


class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				pass




@nightly
@require_torch_gpu
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def       __snake_case		( self						):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



				def       __snake_case		( self						):
											lowerCAmelCase   =		VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
											# remove text_unet
											pipe.remove_unused_weights()
											pipe.to(lowercase_						)
											pipe.set_progress_bar_config(disable=lowercase_						)

											lowerCAmelCase   =		"""A painting of a squirrel eating a burger """
											lowerCAmelCase   =		torch.manual_seed(0						)
											lowerCAmelCase   =		pipe(
											    prompt=lowercase_       , generator=lowercase_       , guidance_scale=7.5       , num_inference_steps=2       , output_type='''numpy'''						).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(lowercase_						)
																		lowerCAmelCase   =		VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase_						)
											pipe.to(lowercase_						)
											pipe.set_progress_bar_config(disable=lowercase_						)

											lowerCAmelCase   =		generator.manual_seed(0						)
											lowerCAmelCase   =		pipe(
											    prompt=lowercase_       , generator=lowercase_       , guidance_scale=7.5       , num_inference_steps=2       , output_type='''numpy'''						).images

											assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"






				def       __snake_case		( self						):
											lowerCAmelCase   =		VersatileDiffusionTextToImagePipeline.from_pretrained(
											    '''shi-labs/versatile-diffusion'''       , torch_dtype=torch.floataa						)
											pipe.to(lowercase_						)
											pipe.set_progress_bar_config(disable=lowercase_						)

											lowerCAmelCase   =		"""A painting of a squirrel eating a burger """
											lowerCAmelCase   =		torch.manual_seed(0						)
											lowerCAmelCase   =		pipe(
											    prompt=lowercase_       , generator=lowercase_       , guidance_scale=7.5       , num_inference_steps=50       , output_type='''numpy'''						).images

											lowerCAmelCase   =		image[0, 2_53:2_56, 2_53:2_56, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											lowerCAmelCase   =		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]						)

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









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



