import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__     :   str		=   logging.get_logger(__name__)


lowerCAmelCase__     :   str		=   '''https://openaipublic.azureedge.net/jukebox/models/'''
lowerCAmelCase__     :   int		=   {
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}
def  UpperCamelCase__						(				A__    )   ->					Any:



				if key.endswith('.model.1.bias'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	str												=      key.replace('.model.1.bias'			, '.conv1d_1.bias'    )
				elif key.endswith('.model.1.weight'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	Optional[Any]												=      key.replace('.model.1.weight'			, '.conv1d_1.weight'    )
				elif key.endswith('.model.3.bias'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	Dict												=      key.replace('.model.3.bias'			, '.conv1d_2.bias'    )
				elif key.endswith('.model.3.weight'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	Tuple												=      key.replace('.model.3.weight'			, '.conv1d_2.weight'    )

				if "conditioner_blocks.0." in key:
								snake_case__					:	Optional[Any]												=      key.replace('conditioner_blocks.0'			, 'conditioner_blocks'    )

				if "prime_prior" in key:
								snake_case__					:	Dict												=      key.replace('prime_prior'			, 'encoder'    )

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								snake_case__					:	Optional[Any]												=      key.replace('.emb.'			, '.'    )

				if key.endswith('k'    ):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace('.k'			, '.codebook'    )
				if "y_emb." in key:
								return key.replace('y_emb.'			, 'metadata_embedding.'    )

				if "x_emb.emb." in key:
								snake_case__					:	Optional[Any]												=      key.replace('0.x_emb.emb'			, 'embed_tokens'    )

				if "prime_state_ln" in key:
								return key.replace('prime_state_ln'			, 'encoder.final_layer_norm'    )
				if ".ln" in key:
								return key.replace('.ln'			, '.layer_norm'    )
				if "_ln" in key:
								return key.replace('_ln'			, '_layer_norm'    )

				if "prime_state_proj" in key:
								return key.replace('prime_state_proj'			, 'encoder.proj_in'    )
				if "prime_x_out" in key:
								return key.replace('prime_x_out'			, 'encoder.lm_head'    )
				if "prior.x_out" in key:
								return key.replace('x_out'			, 'fc_proj_out'    )
				if "x_emb" in key:
								return key.replace('x_emb'			, 'embed_tokens'    )

				return key
def  UpperCamelCase__						(				A__			, A__			, A__			, A__    )   ->					Tuple:
				snake_case__					:	Optional[Any]												=      {}
				import re

				snake_case__					:	Tuple												=      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'    )
				snake_case__					:	Tuple												=      re.compile(
				    r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
				snake_case__					:	int												=      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'    )

				snake_case__					:	Optional[int]												=      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'    )
				snake_case__					:	Optional[Any]												=      re.compile(
				    r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
				snake_case__					:	Any												=      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'    )

				snake_case__					:	str												=      re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'    )
				snake_case__					:	Optional[Any]												=      re.compile(
				    r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
				snake_case__					:	str												=      re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'    )

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(A__    ):
												snake_case__					:	Dict												=      re_encoder_block_conv_in.match(A__    )
												snake_case__					:	int												=      regex_match.groups()
												snake_case__					:	Tuple												=      int(groups[2]    ) * 2 + int(groups[3]    )
												snake_case__					:	int												=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
												snake_case__					:	List[str]												=      re_encoder_block_conv_in.sub(A__			, A__    )

								elif re_encoder_block_resnet.fullmatch(A__    ):
												snake_case__					:	List[Any]												=      re_encoder_block_resnet.match(A__    )
												snake_case__					:	Any												=      regex_match.groups()
												snake_case__					:	int												=      int(groups[2]    ) * 2 + int(groups[3]    )
												snake_case__					:	Any												=      {'1': 1, '3': 2}[groups[-2]]
												snake_case__					:	Optional[int]												=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
												snake_case__					:	Dict												=      F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												snake_case__					:	str												=      prefix + resnet_block
												snake_case__					:	str												=      re_encoder_block_resnet.sub(A__			, A__    )

								elif re_encoder_block_proj_out.fullmatch(A__    ):
												snake_case__					:	Union[str, Any]												=      re_encoder_block_proj_out.match(A__    )
												snake_case__					:	Any												=      regex_match.groups()
												snake_case__					:	Dict												=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
												snake_case__					:	Optional[int]												=      re_encoder_block_proj_out.sub(A__			, A__    )

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(A__    ):
												snake_case__					:	str												=      re_decoder_block_conv_out.match(A__    )
												snake_case__					:	Union[str, Any]												=      regex_match.groups()
												snake_case__					:	Optional[int]												=      int(groups[2]    ) * 2 + int(groups[3]    ) - 2
												snake_case__					:	Tuple												=      F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
												snake_case__					:	int												=      re_decoder_block_conv_out.sub(A__			, A__    )

								elif re_decoder_block_resnet.fullmatch(A__    ):
												snake_case__					:	Dict												=      re_decoder_block_resnet.match(A__    )
												snake_case__					:	Optional[Any]												=      regex_match.groups()
												snake_case__					:	List[str]												=      int(groups[2]    ) * 2 + int(groups[3]    ) - 2
												snake_case__					:	Optional[Any]												=      {'1': 1, '3': 2}[groups[-2]]
												snake_case__					:	Dict												=      F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
												snake_case__					:	Optional[int]												=      F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												snake_case__					:	Tuple												=      prefix + resnet_block
												snake_case__					:	List[str]												=      re_decoder_block_resnet.sub(A__			, A__    )

								elif re_decoder_block_proj_in.fullmatch(A__    ):
												snake_case__					:	List[str]												=      re_decoder_block_proj_in.match(A__    )
												snake_case__					:	str												=      regex_match.groups()
												snake_case__					:	Tuple												=      F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
												snake_case__					:	Optional[int]												=      re_decoder_block_proj_in.sub(A__			, A__    )

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(A__    ):
												snake_case__					:	List[Any]												=      re_prior_cond_conv_out.match(A__    )
												snake_case__					:	Dict												=      regex_match.groups()
												snake_case__					:	Optional[Any]												=      int(groups[1]    ) * 2 + int(groups[2]    ) - 2
												snake_case__					:	List[Any]												=      F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
												snake_case__					:	int												=      re_prior_cond_conv_out.sub(A__			, A__    )

								elif re_prior_cond_resnet.fullmatch(A__    ):
												snake_case__					:	Optional[int]												=      re_prior_cond_resnet.match(A__    )
												snake_case__					:	Union[str, Any]												=      regex_match.groups()
												snake_case__					:	Union[str, Any]												=      int(groups[1]    ) * 2 + int(groups[2]    ) - 2
												snake_case__					:	int												=      {'1': 1, '3': 2}[groups[-2]]
												snake_case__					:	Optional[int]												=      F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
												snake_case__					:	List[str]												=      F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
												snake_case__					:	int												=      prefix + resnet_block
												snake_case__					:	List[Any]												=      re_prior_cond_resnet.sub(A__			, A__    )

								elif re_prior_cond_proj_in.fullmatch(A__    ):
												snake_case__					:	Optional[int]												=      re_prior_cond_proj_in.match(A__    )
												snake_case__					:	Optional[int]												=      regex_match.groups()
												snake_case__					:	Any												=      F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
												snake_case__					:	Optional[int]												=      re_prior_cond_proj_in.sub(A__			, A__    )

								# keep original key
								else:
												snake_case__					:	Any												=      original_key

								snake_case__					:	int												=      replace_key(A__    )

								if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
												print(F"""failed converting {original_key} to {key}, does not match"""    )

								# handle missmatched shape
								elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
												snake_case__					:	List[str]												=      model_state_dict[F"""{key_prefix}.{key}"""]
												print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""    )
												snake_case__					:	Union[str, Any]												=      original_key

								snake_case__					:	Tuple												=      original_key
								snake_case__					:	Dict												=      value

				return new_dict
@torch.no_grad()
def  UpperCamelCase__						(				A__=None			, A__=None    )   ->					Any:



				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split('/'    )[-1]}"""    ):
												snake_case__					:	Optional[Any]												=      requests.get(F"""{PREFIX}{file}"""			, allow_redirects=A__    )
												os.makedirs(F"""{pytorch_dump_folder_path}/"""			, exist_ok=A__    )
												open(F"""{pytorch_dump_folder_path}/{file.split('/'    )[-1]}"""			, 'wb'    ).write(r.content    )

				snake_case__					:	Union[str, Any]												=      MODEL_MAPPING[model_name.split('/'    )[-1]]

				snake_case__					:	int												=      JukeboxConfig.from_pretrained(A__    )
				snake_case__					:	Optional[int]												=      JukeboxModel(A__    )

				snake_case__					:	Dict												=      []
				snake_case__					:	Dict												=      {}
				for i, dict_name in enumerate(A__    ):
								snake_case__					:	Dict												=      torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split('/'    )[-1]}"""    )['model']

								snake_case__					:	Optional[Any]												=      {}
								for k in old_dic.keys():
												if k.endswith('.b'    ):
																snake_case__					:	Union[str, Any]												=      old_dic[k]
												elif k.endswith('.w'    ):
																snake_case__					:	Optional[Any]												=      old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																snake_case__					:	Union[str, Any]												=      old_dic[k]
												else:
																snake_case__					:	Union[str, Any]												=      old_dic[k]

								snake_case__					:	str												=      'vqvae' if i == 0 else F"""priors.{3 - i}"""
								snake_case__					:	Optional[int]												=      fix_jukebox_keys(A__			, model.state_dict()			, A__			, A__    )
								weight_dict.append(A__    )

				snake_case__					:	Optional[int]												=      weight_dict.pop(0    )
				model.vqvae.load_state_dict(A__    )
				for i in range(len(A__    )    ):
								model.priors[i].load_state_dict(weight_dict[2 - i]    )

				Path(A__    ).mkdir(exist_ok=A__    )
				with open(F"""{pytorch_dump_folder_path}/mapping.json"""			, 'w'    ) as txtfile:
								json.dump(A__			, A__    )

				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""    )
				model.save_pretrained(A__    )

				return weight_dict


if __name__ == "__main__":
				lowerCAmelCase__     :   str		=   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''jukebox-5b-lyrics''',
				    type=str,
				    help='''Name of the model you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default='''jukebox-5b-lyrics-converted''',
				    type=str,
				    help='''Path to the output PyTorch model directory.''',
				)
				lowerCAmelCase__     :   Union[str, Any]		=   parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








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     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase):
      @register_to_config
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: bool    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None   ):
             super().__init__()

             __lowerCamelCase   =						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"

                    __lowerCamelCase   =						torch.zeros(UpperCamelCase_    ,       UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						None

             __lowerCamelCase   =						torch.nn.Parameter(UpperCamelCase_   )





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	VQModel
      UpperCAmelCase__      :	CLIPTextModel
      UpperCAmelCase__      :	CLIPTokenizer
      UpperCAmelCase__      :	TransformeraDModel
      UpperCAmelCase__      :	LearnedClassifierFreeSamplingEmbeddings
      UpperCAmelCase__      :	VQDiffusionScheduler
      def __init__(				self: str    ,       UpperCamelCase_: VQModel    ,       UpperCamelCase_: CLIPTextModel    ,       UpperCamelCase_: CLIPTokenizer    ,       UpperCamelCase_: TransformeraDModel    ,       UpperCamelCase_: VQDiffusionScheduler    ,       UpperCamelCase_: LearnedClassifierFreeSamplingEmbeddings    ,       ):
             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: str    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						len(UpperCamelCase_   ) if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ) else 1

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

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

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

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

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

                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                    __lowerCamelCase   =						negative_prompt_embeds.shape[1]
                    __lowerCamelCase   =						negative_prompt_embeds.repeat(1    ,       UpperCamelCase_    ,       1   )
                    __lowerCamelCase   =						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
                    __lowerCamelCase   =						torch.cat([negative_prompt_embeds, prompt_embeds]   )

             return prompt_embeds
      @torch.no_grad()
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[str, List[str]]    ,       UpperCamelCase_: int = 1_00    ,       UpperCamelCase_: float = 5.0    ,       UpperCamelCase_: float = 1.0    ,       UpperCamelCase_: int = 1    ,       UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       UpperCamelCase_: Optional[torch.FloatTensor] = None    ,       UpperCamelCase_: Optional[str] = "pil"    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,       UpperCamelCase_: int = 1    ,       ):
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						1
             elif isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						len(UpperCamelCase_   )
             else:
                    raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_   )}'   )

             __lowerCamelCase   =						batch_size * num_images_per_prompt

             __lowerCamelCase   =						guidance_scale > 1.0

             __lowerCamelCase   =						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

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

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

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

             __lowerCamelCase   =						latents

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

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

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

                    __lowerCamelCase   =						self.truncate(UpperCamelCase_    ,       UpperCamelCase_   )

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

                    # compute the previous noisy sample x_t -> x_t-1
                    __lowerCamelCase   =						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_   )

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

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

             if output_type == "pil":
                    __lowerCamelCase   =						self.numpy_to_pil(UpperCamelCase_   )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: torch.FloatTensor    ,       UpperCamelCase_: float   ):
             __lowerCamelCase,   __lowerCamelCase   =						torch.sort(UpperCamelCase_    ,       1    ,       descending=UpperCamelCase_   )
             __lowerCamelCase   =						torch.exp(UpperCamelCase_   )
             __lowerCamelCase   =						sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

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

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

             __lowerCamelCase   =						log_p_x_0.clone()

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

             return rv

