





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


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




class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__    ):


							@register_to_config
							def __init__(							self    :Any							,				__A    :bool							,				__A    :Optional[int] = None							,				__A    :Optional[int] = None		)   ->   str:



									"""simple docstring"""



									super().__init__()

									SCREAMING_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"

											SCREAMING_SNAKE_CASE__			=      torch.zeros(__A							,				__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      None

									SCREAMING_SNAKE_CASE__			=      torch.nn.Parameter(__A		)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


							def __init__(							self    :List[Any]							,				__A    :VQModel							,				__A    :CLIPTextModel							,				__A    :CLIPTokenizer							,				__A    :TransformeraDModel							,				__A    :VQDiffusionScheduler							,				__A    :LearnedClassifierFreeSamplingEmbeddings							,				)   ->   Tuple:



									"""simple docstring"""



									super().__init__()

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


							def      _snake_case  (							self    :int							,				__A    :int							,				__A    :Dict							,				__A    :Tuple		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      len(__A		) if isinstance(__A							,				__A		) else 1

									# get prompt text embeddings
									SCREAMING_SNAKE_CASE__			=      self.tokenizer(
									    __A							,				padding="""max_length"""							,				max_length=self.tokenizer.model_max_length							,				return_tensors="""pt"""							,				)
									SCREAMING_SNAKE_CASE__			=      text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											SCREAMING_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}'''		)
											SCREAMING_SNAKE_CASE__			=      text_input_ids[:, : self.tokenizer.model_max_length]
									SCREAMING_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
									SCREAMING_SNAKE_CASE__			=      prompt_embeds / prompt_embeds.norm(dim=-1							,				keepdim=__A		)

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

									if do_classifier_free_guidance:
											if self.learned_classifier_free_sampling_embeddings.learnable:
													SCREAMING_SNAKE_CASE__			=      self.learned_classifier_free_sampling_embeddings.embeddings
													SCREAMING_SNAKE_CASE__			=      negative_prompt_embeds.unsqueeze(0		).repeat(__A							,				1							,				1		)
											else:
													SCREAMING_SNAKE_CASE__			=      [""""""] * batch_size

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

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

									return prompt_embeds


							@torch.no_grad()
							def __call__(							self    :Tuple							,				__A    :Union[str, List[str]]							,				__A    :int = 100							,				__A    :float = 5.0							,				__A    :float = 1.0							,				__A    :int = 1							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[torch.FloatTensor] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				__A    :Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,				__A    :int = 1							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      batch_size * num_images_per_prompt

									SCREAMING_SNAKE_CASE__			=      guidance_scale > 1.0

									SCREAMING_SNAKE_CASE__			=      self._encode_prompt(__A							,				__A							,				__A		)

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

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

									SCREAMING_SNAKE_CASE__			=      (batch_size, self.transformer.num_latent_pixels)
									if latents is None:
											SCREAMING_SNAKE_CASE__			=      self.transformer.num_vector_embeds - 1
											SCREAMING_SNAKE_CASE__			=      torch.full(__A							,				__A		).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).'''		)
											SCREAMING_SNAKE_CASE__			=      latents.to(self.device		)

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

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

									SCREAMING_SNAKE_CASE__			=      latents

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

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

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

											SCREAMING_SNAKE_CASE__			=      self.truncate(__A							,				__A		)

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

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

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

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

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

									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)


							def      _snake_case  (							self    :Any							,				__A    :torch.FloatTensor							,				__A    :float		)   ->   torch.FloatTensor:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      torch.sort(__A							,				1							,				descending=__A		)
									SCREAMING_SNAKE_CASE__			=      torch.exp(__A		)
									SCREAMING_SNAKE_CASE__			=      sorted_p_x_0.cumsum(dim=1		) < truncation_rate

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

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

									SCREAMING_SNAKE_CASE__			=      log_p_x_0.clone()

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

									return rv






from math import pow, sqrt






def 				SCREAMING_SNAKE_CASE__							(       *UpperCamelCase__:							float    ):
		SCREAMING_SNAKE_CASE__			=      len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
		return result






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError("""Input Error: Molar mass values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(molar_mass / pow(effusion_rate_a / effusion_rate_a					,   2    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(pow(effusion_rate_a / effusion_rate_a					,   2    ) / molar_mass					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)