"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						__UpperCAmelCase    (__A							,   __A     ):







			'''simple docstring'''





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







										'''simple docstring'''


										super().__init__()

										A__       :     str								=			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"

																	A__       :     Optional[Any]								=			torch.zeros(snake_case_   ,      snake_case_							)
										else:
																	A__       :     Union[str, Any]								=			None

										A__       :     List[str]								=			torch.nn.Parameter(snake_case_							)





class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						VQModel
			_UpperCamelCase  :						CLIPTextModel
			_UpperCamelCase  :						CLIPTokenizer
			_UpperCamelCase  :						TransformeraDModel
			_UpperCamelCase  :						LearnedClassifierFreeSamplingEmbeddings
			_UpperCamelCase  :						VQDiffusionScheduler





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      ):







										'''simple docstring'''


										super().__init__()

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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			len(snake_case_							) if isinstance(snake_case_   ,      snake_case_							) else 1

										# get prompt text embeddings
										A__       :     Dict								=			self.tokenizer(
										    snake_case_   ,      padding="""max_length"""   ,      max_length=self.tokenizer.model_max_length   ,      return_tensors="""pt"""   ,      )
										A__       :     Tuple								=			text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	A__       :     Optional[int]								=			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}'''							)
																	A__       :     Optional[Any]								=			text_input_ids[:, : self.tokenizer.model_max_length]
										A__       :     str								=			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
										A__       :     Dict								=			prompt_embeds / prompt_embeds.norm(dim=-1   ,      keepdim=snake_case_							)

										# duplicate text embeddings for each generation per prompt
										A__       :     List[Any]								=			prompt_embeds.repeat_interleave(snake_case_   ,      dim=0							)

										if do_classifier_free_guidance:
																	if self.learned_classifier_free_sampling_embeddings.learnable:
																								A__       :     Optional[int]								=			self.learned_classifier_free_sampling_embeddings.embeddings
																								A__       :     Any								=			negative_prompt_embeds.unsqueeze(0							).repeat(snake_case_   ,      1   ,      1							)
																	else:
																								A__       :     Dict								=			[""""""] * batch_size

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

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	A__       :     List[str]								=			negative_prompt_embeds.shape[1]
																	A__       :     Dict								=			negative_prompt_embeds.repeat(1   ,      snake_case_   ,      1							)
																	A__       :     List[Any]								=			negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,      snake_case_   ,      -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
																	A__       :     Optional[int]								=			torch.cat([negative_prompt_embeds, prompt_embeds]							)

										return prompt_embeds





			@torch.no_grad()
			def __call__(			self   ,      snake_case_   ,      snake_case_ = 100   ,      snake_case_ = 5.0   ,      snake_case_ = 1.0   ,      snake_case_ = 1   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = "pil"   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = 1   ,      ):







										'''simple docstring'''


										if isinstance(snake_case_   ,      snake_case_							):
																	A__       :     Tuple								=			1
										elif isinstance(snake_case_   ,      snake_case_							):
																	A__       :     Any								=			len(snake_case_							)
										else:
																	raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case_							)}'''							)

										A__       :     int								=			batch_size * num_images_per_prompt

										A__       :     Any								=			guidance_scale > 1.0

										A__       :     Dict								=			self._encode_prompt(snake_case_   ,      snake_case_   ,      snake_case_							)

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

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

										A__       :     Optional[int]								=			(batch_size, self.transformer.num_latent_pixels)
										if latents is None:
																	A__       :     Dict								=			self.transformer.num_vector_embeds - 1
																	A__       :     Optional[Any]								=			torch.full(snake_case_   ,      snake_case_							).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).'''							)
																	A__       :     List[str]								=			latents.to(self.device							)

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

										A__       :     Optional[Any]								=			self.scheduler.timesteps.to(self.device							)

										A__       :     Dict								=			latents

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

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

																	if do_classifier_free_guidance:
																								A__				,							A__       :     Union[str, Any]								=			model_output.chunk(2							)
																								A__       :     Dict								=			model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																								model_output -= torch.logsumexp(snake_case_   ,      dim=1   ,      keepdim=snake_case_							)

																	A__       :     int								=			self.truncate(snake_case_   ,      snake_case_							)

																	# remove `log(0)`'s (`-inf`s)
																	A__       :     Optional[Any]								=			model_output.clamp(-70							)

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

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

										A__       :     Optional[int]								=			self.vqvae.config.vq_embed_dim
										A__       :     Dict								=			(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
										A__       :     Union[str, Any]								=			self.vqvae.quantize.get_codebook_entry(snake_case_   ,      shape=snake_case_							)
										A__       :     List[Any]								=			self.vqvae.decode(snake_case_   ,      force_not_quantize=snake_case_							).sample

										A__       :     Any								=			(image / 2 + 0.5).clamp(0   ,      1							)
										A__       :     Optional[Any]								=			image.cpu().permute(0   ,      2   ,      3   ,      1							).numpy()

										if output_type == "pil":
																	A__       :     List[str]								=			self.numpy_to_pil(snake_case_							)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__				,							A__       :     List[Any]								=			torch.sort(snake_case_   ,      1   ,      descending=snake_case_							)
										A__       :     List[str]								=			torch.exp(snake_case_							)
										A__       :     Any								=			sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

										A__       :     int								=			keep_mask.gather(1   ,      indices.argsort(1							)							)

										A__       :     List[Any]								=			log_p_x_0.clone()

										A__       :     List[Any]								=			-torch.inf  # -inf = log(0)

										return rv






"""simple docstring"""



from collections.abc import Sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase							)							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			0.0
							for coeff in reversed(lowerCAmelCase							):
														A__       :     Optional[int]								=			result * x + coeff
							return result


if __name__ == "__main__":
				_UpperCamelCase								=		(0.0, 0.0, 5.0, 9.3, 7.0)
				_UpperCamelCase								=		10.0
				print(evaluate_poly(poly, x))
				print(horner(poly, x))





