





'''simple docstring'''

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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Any					     =     image.size
      _snake_case		:	List[str]					     =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      _snake_case		:	List[str]					     =     image.resize((w, h)			,      resample=PIL_INTERPOLATION['''lanczos''']	)
      _snake_case		:	Dict					     =     np.array(lowerCAmelCase_	).astype(np.floataa	) / 255.0
      _snake_case		:	Optional[Any]					     =     image[None].transpose(0			,      3			,      1			,      2	)
      _snake_case		:	Tuple					     =     torch.from_numpy(lowerCAmelCase_	)
      return 2.0 * image - 1.0





class lowerCamelCase						(a__					):







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						int:






        """simple docstring"""





        super().__init__()
        self.register_modules(vqvae=UpperCAmelCase__     ,     unet=UpperCAmelCase__     ,     scheduler=UpperCAmelCase__		)







  @torch.no_grad()
  def __call__(		self     ,     lowercase__ = None     ,     lowercase__ = 1     ,     lowercase__ = 100     ,     lowercase__ = 0.0     ,     lowercase__ = None     ,     lowercase__ = "pil"     ,     lowercase__ = True     ,     )		->						List[str]:






        """simple docstring"""





        if isinstance(UpperCAmelCase__     ,     PIL.Image.Image		):
              _snake_case		:	Optional[int]					     =     1
        elif isinstance(UpperCAmelCase__     ,     torch.Tensor		):
              _snake_case		:	Any					     =     image.shape[0]
        else:
              raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCAmelCase__		)}'''		)

        if isinstance(UpperCAmelCase__     ,     PIL.Image.Image		):
              _snake_case		:	Optional[Any]					     =     preprocess(UpperCAmelCase__		)

        _snake_case		:	Union[str, Any]					     =     image.shape[-2:]

        # in_channels should be 6: 3 for latents, 3 for low resolution image
        _snake_case		:	List[Any]					     =     (batch_size, self.unet.config.in_channels // 2, height, width)
        _snake_case		:	str					     =     next(self.unet.parameters()		).dtype

        _snake_case		:	Dict					     =     randn_tensor(UpperCAmelCase__     ,     generator=UpperCAmelCase__     ,     device=self.device     ,     dtype=UpperCAmelCase__		)

        _snake_case		:	Any					     =     image.to(device=self.device     ,     dtype=UpperCAmelCase__		)

        # set timesteps and move to the correct device
        self.scheduler.set_timesteps(UpperCAmelCase__     ,     device=self.device		)
        _snake_case		:	Optional[Any]					     =     self.scheduler.timesteps

        # scale the initial noise by the standard deviation required by the scheduler
        _snake_case		:	Union[str, Any]					     =     latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        _snake_case		:	Any					     =     '''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
        _snake_case		:	Optional[Any]					     =     {}
        if accepts_eta:
              _snake_case		:	Dict					     =     eta

        for t in self.progress_bar(UpperCAmelCase__		):
              # concat latents and low resolution image in the channel dimension.
              _snake_case		:	Optional[int]					     =     torch.cat([latents, image]     ,     dim=1		)
              _snake_case		:	str					     =     self.scheduler.scale_model_input(UpperCAmelCase__     ,     UpperCAmelCase__		)
              # predict the noise residual
              _snake_case		:	int					     =     self.unet(UpperCAmelCase__     ,     UpperCAmelCase__		).sample
              # compute the previous noisy sample x_t -> x_t-1
              _snake_case		:	Any					     =     self.scheduler.step(UpperCAmelCase__     ,     UpperCAmelCase__     ,     UpperCAmelCase__     ,     **UpperCAmelCase__		).prev_sample

        # decode the image latents with the VQVAE
        _snake_case		:	Optional[int]					     =     self.vqvae.decode(UpperCAmelCase__		).sample
        _snake_case		:	int					     =     torch.clamp(UpperCAmelCase__     ,     -1.0     ,     1.0		)
        _snake_case		:	Dict					     =     image / 2 + 0.5
        _snake_case		:	int					     =     image.cpu().permute(0     ,     2     ,     3     ,     1		).numpy()

        if output_type == "pil":
              _snake_case		:	Any					     =     self.numpy_to_pil(UpperCAmelCase__		)

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase__		)









'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


