

'''simple docstring'''





from __future__ import annotations

from collections.abc import Sequence
from typing import Literal





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= list(lowercase__	)
	snake_case_      		= list(lowercase__	)
	snake_case_      		= 0
	for i in range(len(lowercase__	)	):
		if lista[i] != lista[i]:
			count += 1
			snake_case_      		= '_'
	if count > 1:
		return False
	else:
		return "".join(lowercase__	)





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	while True:
		snake_case_      		= ['$'] * len(lowercase__	)
		snake_case_      		= []
		for i in range(len(lowercase__	)	):
			for j in range(i + 1			, len(lowercase__	)	):
				snake_case_      		= compare_string(binary[i]			, binary[j]	)
				if k is False:
					snake_case_      		= '*'
					snake_case_      		= '*'
					temp.append('X'	)
		for i in range(len(lowercase__	)	):
			if checka[i] == "$":
				pi.append(binary[i]	)
		if len(lowercase__	) == 0:
			return pi
		snake_case_      		= list(set(lowercase__	)	)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	for minterm in minterms:
		snake_case_      		= ''
		for _ in range(lowercase__	):
			snake_case_      		= str(minterm % 2	) + string
			minterm //= 2
		temp.append(lowercase__	)
	return temp





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= list(lowercase__	)
	snake_case_      		= list(lowercase__	)
	snake_case_      		= 0
	for i in range(len(lowercase__	)	):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= [0] * len(lowercase__	)
	for i in range(len(chart[0]	)	):
		snake_case_      		= 0
		snake_case_      		= -1
		for j in range(len(lowercase__	)	):
			if chart[j][i] == 1:
				count += 1
				snake_case_      		= j
		if count == 1:
			snake_case_      		= 1
	for i in range(len(lowercase__	)	):
		if select[i] == 1:
			for j in range(len(chart[0]	)	):
				if chart[i][j] == 1:
					for k in range(len(lowercase__	)	):
						snake_case_      		= 0
			temp.append(prime_implicants[i]	)
	while True:
		snake_case_      		= 0
		snake_case_      		= -1
		snake_case_      		= 0
		for i in range(len(lowercase__	)	):
			snake_case_      		= chart[i].count(1	)
			if count_n > max_n:
				snake_case_      		= count_n
				snake_case_      		= i

		if max_n == 0:
			return temp

		temp.append(prime_implicants[rem]	)

		for i in range(len(chart[0]	)	):
			if chart[rem][i] == 1:
				for j in range(len(lowercase__	)	):
					snake_case_      		= 0





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= [[0 for x in range(len(lowercase__	)	)] for x in range(len(lowercase__	)	)]
	for i in range(len(lowercase__	)	):
		snake_case_      		= prime_implicants[i].count('_'	)
		for j in range(len(lowercase__	)	):
			if is_for_table(prime_implicants[i]			, binary[j]			, lowercase__	):
				snake_case_      		= 1

	return chart





def a():





	'''simple docstring'''
	snake_case_      		= int(input('Enter the no. of variables\n'	)	)
	snake_case_      		= [
	    float(lowercase__	)
	    for x in input(
	        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'	).split()
	]
	snake_case_      		= decimal_to_binary(lowercase__			, lowercase__	)

	snake_case_      		= check(lowercase__	)
	print('Prime Implicants are:'	)
	print(lowercase__	)
	snake_case_      		= prime_implicant_chart(lowercase__			, lowercase__	)

	snake_case_      		= selection(lowercase__			, lowercase__	)
	print('Essential Prime Implicants are:'	)
	print(lowercase__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A											=     logging.get_logger(__name__)  # pylint: disable=invalid-name


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			@register_to_config
			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				super().__init__()

				snake_case_      		= learnable

				if self.learnable:
					assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
					assert length is not None, "learnable=True requires `length` to be set"

					snake_case_      		= torch.zeros(__UpperCamelCase ,				__UpperCamelCase							)
				else:
					snake_case_      		= None

				snake_case_      		= torch.nn.Parameter(__UpperCamelCase							)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

				self.register_modules(
				    vqvae=__UpperCamelCase ,				transformer=__UpperCamelCase ,				text_encoder=__UpperCamelCase ,				tokenizer=__UpperCamelCase ,				scheduler=__UpperCamelCase ,				learned_classifier_free_sampling_embeddings=__UpperCamelCase ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else 1

				# get prompt text embeddings
				snake_case_      		= self.tokenizer(
				    __UpperCamelCase ,				padding='max_length' ,				max_length=self.tokenizer.model_max_length ,				return_tensors='pt' ,				)
				snake_case_      		= text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					snake_case_      		= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
					logger.warning(
					    'The following part of your input was truncated because CLIP can only handle sequences up to'
					    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
					snake_case_      		= text_input_ids[:, : self.tokenizer.model_max_length]
				snake_case_      		= self.text_encoder(text_input_ids.to(self.device							)							)[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				snake_case_      		= prompt_embeds / prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

				# duplicate text embeddings for each generation per prompt
				snake_case_      		= prompt_embeds.repeat_interleave(__UpperCamelCase ,				dim=0							)

				if do_classifier_free_guidance:
					if self.learned_classifier_free_sampling_embeddings.learnable:
						snake_case_      		= self.learned_classifier_free_sampling_embeddings.embeddings
						snake_case_      		= negative_prompt_embeds.unsqueeze(0							).repeat(__UpperCamelCase ,				1 ,				1							)
					else:
						snake_case_      		= [''] * batch_size

						snake_case_      		= text_input_ids.shape[-1]
						snake_case_      		= self.tokenizer(
						    __UpperCamelCase ,				padding='max_length' ,				max_length=__UpperCamelCase ,				truncation=__UpperCamelCase ,				return_tensors='pt' ,				)
						snake_case_      		= self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
						# See comment for normalizing text embeddings
						snake_case_      		= negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					snake_case_      		= negative_prompt_embeds.shape[1]
					snake_case_      		= negative_prompt_embeds.repeat(1 ,				__UpperCamelCase ,				1							)
					snake_case_      		= negative_prompt_embeds.view(batch_size * num_images_per_prompt ,				__UpperCamelCase ,				-1							)

					# For classifier free guidance, we need to do two forward passes.
					# Here we concatenate the unconditional and text embeddings into a single batch
					# to avoid doing two forward passes
					snake_case_      		= torch.cat([negative_prompt_embeds, prompt_embeds]							)

				return prompt_embeds


			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 5.0 ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= 1
				elif isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= len(__UpperCamelCase							)
				else:
					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase							)}"""							)

				snake_case_      		= batch_size * num_images_per_prompt

				snake_case_      		= guidance_scale > 1.0

				snake_case_      		= self._encode_prompt(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(__UpperCamelCase ,				__UpperCamelCase							) or callback_steps <= 0)
				):
					raise ValueError(
					    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
					    f""" {type(__UpperCamelCase							)}."""							)

				# get the initial completely masked latents unless the user supplied it

				snake_case_      		= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					snake_case_      		= self.transformer.num_vector_embeds - 1
					snake_case_      		= torch.full(__UpperCamelCase ,				__UpperCamelCase							).to(self.device							)
				else:
					if latents.shape != latents_shape:
						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
					if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
						raise ValueError(
						    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
						    f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""							)
					snake_case_      		= latents.to(self.device							)

				# set timesteps
				self.scheduler.set_timesteps(__UpperCamelCase ,				device=self.device							)

				snake_case_      		= self.scheduler.timesteps.to(self.device							)

				snake_case_      		= latents

				for i, t in enumerate(self.progress_bar(__UpperCamelCase							)							):
					# expand the sample if we are doing classifier free guidance
					snake_case_      		= torch.cat([sample] * 2							) if do_classifier_free_guidance else sample

					# predict the un-noised image
					# model_output == `log_p_x_0`
					snake_case_      		= self.transformer(__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				timestep=__UpperCamelCase							).sample

					if do_classifier_free_guidance:
						snake_case_		,       snake_case_      		= model_output.chunk(2							)
						snake_case_      		= model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
						model_output -= torch.logsumexp(__UpperCamelCase ,				dim=1 ,				keepdim=__UpperCamelCase							)

					snake_case_      		= self.truncate(__UpperCamelCase ,				__UpperCamelCase							)

					# remove `log(0)`'s (`-inf`s)
					snake_case_      		= model_output.clamp(-70							)

					# compute the previous noisy sample x_t -> x_t-1
					snake_case_      		= self.scheduler.step(__UpperCamelCase ,				timestep=__UpperCamelCase ,				sample=__UpperCamelCase ,				generator=__UpperCamelCase							).prev_sample

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

				snake_case_      		= self.vqvae.config.vq_embed_dim
				snake_case_      		= (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				snake_case_      		= self.vqvae.quantize.get_codebook_entry(__UpperCamelCase ,				shape=__UpperCamelCase							)
				snake_case_      		= self.vqvae.decode(__UpperCamelCase ,				force_not_quantize=__UpperCamelCase							).sample

				snake_case_      		= (image / 2 + 0.5).clamp(0 ,				1							)
				snake_case_      		= image.cpu().permute(0 ,				2 ,				3 ,				1							).numpy()

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= torch.sort(__UpperCamelCase ,				1 ,				descending=__UpperCamelCase							)
				snake_case_      		= torch.exp(__UpperCamelCase							)
				snake_case_      		= sorted_p_x_0.cumsum(dim=1							) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				snake_case_      		= torch.full_like(keep_mask[:, 0:1, :] ,				__UpperCamelCase							)
				snake_case_      		= torch.cat((all_true, keep_mask) ,				dim=1							)
				snake_case_      		= keep_mask[:, :-1, :]

				snake_case_      		= keep_mask.gather(1 ,				indices.argsort(1							)							)

				snake_case_      		= log_p_x_0.clone()

				snake_case_      		= -torch.inf  # -inf = log(0)

				return rv
