


'''simple docstring'''


import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCamelCase__					=   logging.get_logger(__name__)






class 		lowerCAmelCase__	(	enum.Enum		):
			lowerCAmelCase       :		List[str]				      =   0
			lowerCAmelCase       :		List[str]				      =   1






@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 		lowerCAmelCase__	(	SCREAMING_SNAKE_CASE_		):
			lowerCAmelCase       :		List[Any]				      =   "generated"




			def __init__(						self     :   str		,     *lowerCamelCase__     :   Tuple		,     **lowerCamelCase__     :   Optional[Any]       )      ->Dict:



					'''simple docstring'''







					super().__init__(*_a		,     **_a       )

					self.check_model_type(
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					    if self.framework == "tf"
					    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   Tuple=None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Any=None		,     lowerCamelCase__     :   str=None		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   int=None		,     **lowerCamelCase__     :   List[Any]		,     )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					{}
					if truncation is not None:
							_UpperCAmelCase						:				List[Any]	    =					truncation

					_UpperCAmelCase						:				Optional[Any]	    =					generate_kwargs

					_UpperCAmelCase						:				str	    =					{}
					if return_tensors is not None and return_type is None:
							_UpperCAmelCase						:				Union[str, Any]	    =					ReturnType.TENSORS if return_tensors else ReturnType.TEXT
					if return_type is not None:
							_UpperCAmelCase						:				Optional[Any]	    =					return_type

					if clean_up_tokenization_spaces is not None:
							_UpperCAmelCase						:				int	    =					clean_up_tokenization_spaces

					if stop_sequence is not None:
							_UpperCAmelCase						:				Union[str, Any]	    =					self.tokenizer.encode(_a		,     add_special_tokens=_a       )
							if len(_a       ) > 1:
									warnings.warn(
									    "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
									    " the stop sequence will be used as the stop sequence string in the interim."       )
							_UpperCAmelCase						:				Dict	    =					stop_sequence_ids[0]

					return preprocess_params, forward_params, postprocess_params




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int       )      ->Union[str, Any]:



					'''simple docstring'''







					return True




			def 							lowerCAmelCase__			(						self     :   int		,     *lowerCamelCase__     :   int		,     lowerCamelCase__     :   Tuple       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					self.model.config.prefix if self.model.config.prefix is not None else ''
					if isinstance(args[0]		,     _a       ):
							if self.tokenizer.pad_token_id is None:
									raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"       )
							_UpperCAmelCase						:				int	    =					([prefix + arg for arg in args[0]],)
							_UpperCAmelCase						:				Tuple	    =					True

					elif isinstance(args[0]		,     _a       ):
							_UpperCAmelCase						:				List[Any]	    =					(prefix + args[0],)
							_UpperCAmelCase						:				Union[str, Any]	    =					False
					else:
							raise ValueError(
							    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""       )
					_UpperCAmelCase						:				Tuple	    =					self.tokenizer(*_a		,     padding=_a		,     truncation=_a		,     return_tensors=self.framework       )
					# This is produced by tokenizers but is an invalid generate kwargs
					if "token_type_ids" in inputs:
							del inputs["token_type_ids"]
					return inputs




			def __call__(						self     :   str		,     *lowerCamelCase__     :   int		,     **lowerCamelCase__     :   List[str]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					super().__call__(*_a		,     **_a       )
					if (
					    isinstance(args[0]		,     _a       )
					    and all(isinstance(_a		,     _a       ) for el in args[0]       )
					    and all(len(_a       ) == 1 for res in result       )
					):
							return [res[0] for res in result]
					return result




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   int=TruncationStrategy.DO_NOT_TRUNCATE		,     **lowerCamelCase__     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					self._parse_and_tokenize(_a		,     truncation=_a		,     **_a       )
					return inputs




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Dict		,     **lowerCamelCase__     :   Union[str, Any]       )      ->Optional[Any]:



					'''simple docstring'''







					if self.framework == "pt":
							_UpperCAmelCase						:				Any	    =					model_inputs['input_ids'].shape
					elif self.framework == "tf":
							_UpperCAmelCase						:				str	    =					tf.shape(model_inputs["input_ids"]       ).numpy()

					_UpperCAmelCase						:				Tuple	    =					generate_kwargs.get("min_length"		,     self.model.config.min_length       )
					_UpperCAmelCase						:				List[str]	    =					generate_kwargs.get("max_length"		,     self.model.config.max_length       )
					self.check_inputs(_a		,     generate_kwargs["min_length"]		,     generate_kwargs["max_length"]       )
					_UpperCAmelCase						:				int	    =					self.model.generate(**_a		,     **_a       )
					_UpperCAmelCase						:				Optional[Any]	    =					output_ids.shape[0]
					if self.framework == "pt":
							_UpperCAmelCase						:				List[str]	    =					output_ids.reshape(_a		,     out_b // in_b		,     *output_ids.shape[1:]       )
					elif self.framework == "tf":
							_UpperCAmelCase						:				Union[str, Any]	    =					tf.reshape(_a		,     (in_b, out_b // in_b, *output_ids.shape[1:])       )
					return {"output_ids": output_ids}






			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[Any]		,     lowerCamelCase__     :   str=ReturnType.TEXT		,     lowerCamelCase__     :   Optional[int]=False       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					[]
					for output_ids in model_outputs["output_ids"][0]:
							if return_type == ReturnType.TENSORS:
									_UpperCAmelCase						:				List[str]	    =					{F"""{self.return_name}_token_ids""": output_ids}
							elif return_type == ReturnType.TEXT:
									_UpperCAmelCase						:				List[str]	    =					{
									    F"""{self.return_name}_text""": self.tokenizer.decode(
									        _a		,     skip_special_tokens=_a		,     clean_up_tokenization_spaces=_a		,     )
									}
							records.append(_a       )
					return records






@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 		lowerCAmelCase__	(	SCREAMING_SNAKE_CASE_		):
			lowerCAmelCase       :		Dict				      =   "summary"




			def __call__(						self     :   str		,     *lowerCamelCase__     :   List[Any]		,     **lowerCamelCase__     :   Any       )      ->Dict:



					'''simple docstring'''







					return super().__call__(*_a		,     **_a       )






			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int       )      ->bool:



					'''simple docstring'''







					if max_length < min_length:
							logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""       )

					if input_length < max_length:
							logger.warning(
							    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
							    "a summarization task, where outputs shorter than the input are typically wanted, you might "
							    F"""consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})"""       )






@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 		lowerCAmelCase__	(	SCREAMING_SNAKE_CASE_		):
			lowerCAmelCase       :		Optional[Any]				      =   "translation"




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   int       )      ->int:



					'''simple docstring'''







					if input_length > 0.9 * max_length:
							logger.warning(
							    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
							    "increasing your max_length manually, e.g. translator(\'...\', max_length=400)"       )
					return True




			def 							lowerCAmelCase__			(						self     :   List[str]		,     *lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   Any=TruncationStrategy.DO_NOT_TRUNCATE		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   Tuple=None       )      ->Any:



					'''simple docstring'''







					if getattr(self.tokenizer		,     "_build_translation_inputs"		,     _a       ):
							return self.tokenizer._build_translation_inputs(
							    *_a		,     return_tensors=self.framework		,     truncation=_a		,     src_lang=_a		,     tgt_lang=_a       )
					else:
							return super()._parse_and_tokenize(*_a		,     truncation=_a       )




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Tuple=None		,     lowerCamelCase__     :   int=None		,     **lowerCamelCase__     :   Tuple       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					super()._sanitize_parameters(**_a       )
					if src_lang is not None:
							_UpperCAmelCase						:				str	    =					src_lang
					if tgt_lang is not None:
							_UpperCAmelCase						:				int	    =					tgt_lang
					if src_lang is None and tgt_lang is None:
							# Backward compatibility, direct arguments use is preferred.
							_UpperCAmelCase						:				Dict	    =					kwargs.get("task"		,     self.task       )
							_UpperCAmelCase						:				Tuple	    =					task.split("_"       )
							if task and len(_a       ) == 4:
									# translation, XX, to YY
									_UpperCAmelCase						:				Optional[int]	    =					items[1]
									_UpperCAmelCase						:				Optional[Any]	    =					items[3]
					return preprocess_params, forward_params, postprocess_params






			def __call__(						self     :   Dict		,     *lowerCamelCase__     :   Tuple		,     **lowerCamelCase__     :   Optional[Any]       )      ->Optional[int]:



					'''simple docstring'''







					return super().__call__(*_a		,     **_a       )








'''simple docstring'''





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCamelCase											=						8

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Any:
  __lowerCamelCase       :       List[str]           = x.device

  __lowerCamelCase       :       int           = (x * 255).int().clamp(0       ,255       )

  __lowerCamelCase       :       Any           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Optional[Any]           = rearrange(_lowerCAmelCase       ,'b c h w -> b c 1 h w'       )

  __lowerCamelCase       :       Optional[Any]           = ((x & mask) != 0).float()
  __lowerCamelCase       :       Tuple           = rearrange(_lowerCAmelCase       ,'b c d h w -> b (c d) h w'       )
  __lowerCamelCase       :       Union[str, Any]           = bits * 2 - 1
  return bits

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Optional[Any]:
  __lowerCamelCase       :       int           = x.device

  __lowerCamelCase       :       int           = (x > 0).int()
  __lowerCamelCase       :       List[str]           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       ,dtype=torch.intaa       )

  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'b (c d) h w -> b c d h w'       ,d=8       )
  __lowerCamelCase       :       List[str]           = reduce(x * mask       ,'b c d h w -> b c h w'       ,'sum'       )
  return (dec / 255).clamp(0.0       ,1.0       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 0.0       ,_lowerCAmelCase = True       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDIMSchedulerOutput, Tuple]:
  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'       )

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  __lowerCamelCase       :       Optional[Any]           = timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  __lowerCamelCase       :       str           = self.alphas_cumprod[timestep]
  __lowerCamelCase       :       Optional[Any]           = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  __lowerCamelCase       :       Union[str, Any]           = 1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Optional[int]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __lowerCamelCase       :       Dict           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Optional[Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  __lowerCamelCase       :       int           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    __lowerCamelCase       :       List[str]           = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Union[str, Any]           = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Any           = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    __lowerCamelCase       :       Dict           = model_output.device if torch.is_tensor(_lowerCAmelCase       ) else 'cpu'
    __lowerCamelCase       :       Dict           = torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=_lowerCAmelCase       ).to(_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       ) ** 0.5 * eta * noise

    __lowerCamelCase       :       int           = prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase="epsilon"       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDPMSchedulerOutput, Tuple]:
  __lowerCamelCase       :       Dict           = timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = torch.split(_lowerCAmelCase       ,sample.shape[1]       ,dim=1       )
  else:
    __lowerCamelCase       :       Optional[Any]           = None

  # 1. compute alphas, betas
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t]
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t - 1] if t > 0 else self.one
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    __lowerCamelCase       :       List[Any]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    __lowerCamelCase       :       Any           = model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

  # 3. Clip "predicted x_0"
  __lowerCamelCase       :       Optional[Any]           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Union[str, Any]           = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __lowerCamelCase       :       Dict           = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Dict           = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  __lowerCamelCase       :       Optional[Any]           = 0
  if t > 0:
    __lowerCamelCase       :       Any           = torch.randn(
        model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=_lowerCAmelCase       ).to(model_output.device       )
    __lowerCamelCase       :       Optional[Any]           = (self._get_variance(_lowerCAmelCase       ,predicted_variance=_lowerCAmelCase       ) ** 0.5) * noise

  __lowerCamelCase       :       str           = pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	UNetaDConditionModel			,	_a      :	Union[DDIMScheduler, DDPMScheduler]			,	_a      :	Optional[float] = 1.0			,	)					->					Union[str, Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = bit_scale
    __lowerCamelCase       :       List[str]           = (
        ddim_bit_scheduler_step if isinstance(_a			,	_a   ) else ddpm_bit_scheduler_step
    )

    self.register_modules(unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	Tuple			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 50			,	_a      :	Optional[torch.Generator] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	**_a      :	Dict			,	)					->					Union[Tuple, ImagePipelineOutput]:
    __lowerCamelCase       :       str           = torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)			,	generator=_a			,	)
    __lowerCamelCase       :       int           = decimal_to_bits(_a   ) * self.bit_scale
    __lowerCamelCase       :       Dict           = latents.to(self.device   )

    self.scheduler.set_timesteps(_a   )

    for t in self.progress_bar(self.scheduler.timesteps   ):
      # predict the noise residual
      __lowerCamelCase       :       int           = self.unet(_a			,	_a   ).sample

      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a   ).prev_sample

    __lowerCamelCase       :       Dict           = bits_to_decimal(_a   )

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

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






