






'''simple docstring'''

import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						KandinskyVaaPriorPipeline
					A_						    =						["""prompt"""]
					A_						    =						["""prompt""", """negative_prompt"""]
					A_						    =						[
					    """num_images_per_prompt""",
					    """generator""",
					    """num_inference_steps""",
					    """latents""",
					    """negative_prompt""",
					    """guidance_scale""",
					    """output_type""",
					    """return_dict""",
					]
					A_						    =						False




					@property
					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Any:



						'''simple docstring'''





						return 32




					@property
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						return 32




					@property
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[int]:



						'''simple docstring'''





						return self.time_input_dim




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Optional[Any]:



						'''simple docstring'''





						return self.time_input_dim * 4




					@property
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Any:



						'''simple docstring'''





						return 100




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Tuple       =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
						return tokenizer




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Optional[int]       =		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(__A					)




					@property
					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Dict:



						'''simple docstring'''





						torch.manual_seed(0					)

						_lowercase					:       List[str]       =		{
						    "num_attention_heads": 2,
						    "attention_head_dim": 12,
						    "embedding_dim": self.text_embedder_hidden_size,
						    "num_layers": 1,
						}

						_lowercase					:       Any       =		PriorTransformer(**__A					)
						# 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
						_lowercase					:       Optional[Any]       =		nn.Parameter(torch.ones(model.clip_std.shape					)					)
						return model




					@property
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       str:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Optional[int]       =		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     ,  )

						_lowercase					:       Any       =		CLIPVisionModelWithProjection(__A					)
						return model




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		CLIPImageProcessor(
						    crop_size=224     ,  do_center_crop=__A     ,  do_normalize=__A     ,  do_resize=__A     ,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]     ,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]     ,  resample=3     ,  size=224     ,  )

						return image_processor




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		self.dummy_prior
						_lowercase					:       Any       =		self.dummy_image_encoder
						_lowercase					:       Any       =		self.dummy_text_encoder
						_lowercase					:       List[str]       =		self.dummy_tokenizer
						_lowercase					:       List[str]       =		self.dummy_image_processor

						_lowercase					:       Optional[Any]       =		UnCLIPScheduler(
						    variance_type='fixed_small_log'     ,  prediction_type='sample'     ,  num_train_timesteps=1000     ,  clip_sample=__A     ,  clip_sample_range=10.0     ,  )

						_lowercase					:       Optional[Any]       =		{
						    "prior": prior,
						    "image_encoder": image_encoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "scheduler": scheduler,
						    "image_processor": image_processor,
						}

						return components




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Tuple=0					)  ->       List[str]:



						'''simple docstring'''





						if str(__A					).startswith('mps'					):
							_lowercase					:       Any       =		torch.manual_seed(__A					)
						else:
							_lowercase					:       Optional[int]       =		torch.Generator(device=__A					).manual_seed(__A					)
						_lowercase					:       int       =		{
						    "prompt": "horse",
						    "generator": generator,
						    "guidance_scale": 4.0,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}
						return inputs




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		"cpu"

						_lowercase					:       Any       =		self.get_dummy_components()

						_lowercase					:       int       =		self.pipeline_class(**__A					)
						_lowercase					:       str       =		pipe.to(__A					)

						pipe.set_progress_bar_config(disable=__A					)

						_lowercase					:       int       =		pipe(**self.get_dummy_inputs(__A					)					)
						_lowercase					:       Any       =		output.image_embeds

						_lowercase					:       Optional[int]       =		pipe(
						    **self.get_dummy_inputs(__A					)     ,  return_dict=__A     ,  )[0]

						_lowercase					:       Any       =		image[0, -10:]
						_lowercase					:       List[str]       =		image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						_lowercase					:       str       =		np.array(
						    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]					)

						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 						__UpperCAmelCase     (  self				:	str					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		torch_device == "cpu"
						_lowercase					:       Optional[int]       =		True
						_lowercase					:       Dict       =		False

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




					@skip_mps
					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		torch_device == "cpu"
						_lowercase					:       Any       =		False

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





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       Union[str, Any]       =		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:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

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

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

    # 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				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       List[Any]       =		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(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	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()
