



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class     __a			(  unittest.TestCase					):



	@property
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						torch.manual_seed(0)
						__lowerCAmelCase						 =UNetaDModel(
						    block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=3					,						out_channels=3					,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,						)
						return model



	@property
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[Any]:
						torch.manual_seed(0)
						__lowerCAmelCase						 =VQModel(
						    block_out_channels=[32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,						latent_channels=3					,						)
						return model



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				List[str]:
						torch.manual_seed(0)
						__lowerCAmelCase						 =CLIPTextConfig(
						    bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=10_00					,						)
						return CLIPTextModel(snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						__lowerCAmelCase						 =self.dummy_uncond_unet
						__lowerCAmelCase						 =DDIMScheduler()
						__lowerCAmelCase						 =self.dummy_vq_model

						__lowerCAmelCase						 =LDMPipeline(unet=snake_case_					,						vqvae=snake_case_					,						scheduler=snake_case_)
						ldm.to(snake_case_)
						ldm.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =torch.manual_seed(0)
						__lowerCAmelCase						 =ldm(generator=snake_case_					,						num_inference_steps=2					,						output_type="""numpy""").images

						__lowerCAmelCase						 =torch.manual_seed(0)
						__lowerCAmelCase						 =ldm(generator=snake_case_					,						num_inference_steps=2					,						output_type="""numpy"""					,						return_dict=snake_case_)[0]

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

						assert image.shape == (1, 64, 64, 3)
						__lowerCAmelCase						 =np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2])
						__lowerCAmelCase						 =1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance






@slow
@require_torch
class     __a			(  unittest.TestCase					):



	def 		UpperCamelCase				(    self				:     str)->				Tuple:
						__lowerCAmelCase						 =LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256""")
						ldm.to(snake_case_)
						ldm.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =torch.manual_seed(0)
						__lowerCAmelCase						 =ldm(generator=snake_case_					,						num_inference_steps=5					,						output_type="""numpy""").images

						__lowerCAmelCase						 =image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_56, 2_56, 3)
						__lowerCAmelCase						 =np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7])
						__lowerCAmelCase						 =1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    None:
					__lowerCAmelCase ,						__lowerCAmelCase						 =analyze_text(__lowerCamelCase						)
					__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(__lowerCamelCase						)  # 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(__lowerCamelCase						) / all_sum
																				my_sec_sum += prob * math.loga(__lowerCamelCase						)

    # 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        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    tuple[dict, dict]:
					__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(__lowerCamelCase						) - 1						):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings






def        __lowerCAmelCase		(	)	->    str:
					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()




