


'''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


A__						:   int				=    8



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							int=BITS				)	->						int:
     __lowerCamelCase  :      List[Any]														=						x.device

     __lowerCamelCase  :      Union[str, Any]														=						(x * 2_55).int().clamp(0 ,       2_55				)

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

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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							str=BITS				)	->						Tuple:
     __lowerCamelCase  :      int														=						x.device

     __lowerCamelCase  :      Tuple														=						(x > 0).int()
     __lowerCamelCase  :      Optional[int]														=						2 ** torch.arange(bits - 1 ,       -1 ,       -1 ,       device=UpperCAmelCase_ ,       dtype=torch.intaa				)

     __lowerCamelCase  :      Tuple														=						rearrange(UpperCAmelCase_ ,       'd -> d 1 1'				)
     __lowerCamelCase  :      List[Any]														=						rearrange(UpperCAmelCase_ ,       'b (c d) h w -> b c d h w' ,       d=8				)
     __lowerCamelCase  :      Dict														=						reduce(x * mask ,       'b c d h w -> b c h w' ,       'sum'				)
     return (dec / 2_55).clamp(0.0 ,       1.0				)



def      UpperCAmelCase__   (      self       :							str ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							bool = True ,       UpperCAmelCase_       :							Any=None ,       UpperCAmelCase_       :							bool = 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  :      Tuple														=						timestep - self.config.num_train_timesteps // self.num_inference_steps

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

     __lowerCamelCase  :      Dict														=						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  :      Union[str, Any]														=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     __lowerCamelCase  :      Any														=						self.bit_scale
     if self.config.clip_sample:
          __lowerCamelCase  :      Tuple														=						torch.clamp(UpperCAmelCase_ ,       -scale ,       UpperCAmelCase_				)

     # 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(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      Any														=						eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          __lowerCamelCase  :      Any														=						(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  :      List[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  :      List[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  :      int														=						model_output.device if torch.is_tensor(UpperCAmelCase_				) else 'cpu'
          __lowerCamelCase  :      List[Any]														=						torch.randn(model_output.shape ,       dtype=model_output.dtype ,       generator=UpperCAmelCase_				).to(UpperCAmelCase_				)
          __lowerCamelCase  :      int														=						self._get_variance(UpperCAmelCase_ ,       UpperCAmelCase_				) ** 0.5 * eta * noise

          __lowerCamelCase  :      Tuple														=						prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_ ,       pred_original_sample=UpperCAmelCase_				)



def      UpperCAmelCase__   (      self       :							Optional[Any] ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							torch.FloatTensor ,       UpperCAmelCase_       :							int="epsilon" ,       UpperCAmelCase_       :							List[str]=None ,       UpperCAmelCase_       :							bool = True ,       )	->						Union[DDPMSchedulerOutput, Tuple]:
     __lowerCamelCase  :      Tuple														=						timestep

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

     # 1. compute alphas, betas
     __lowerCamelCase  :      Tuple														=						self.alphas_cumprod[t]
     __lowerCamelCase  :      Optional[Any]														=						self.alphas_cumprod[t - 1] if t > 0 else self.one
     __lowerCamelCase  :      Optional[Any]														=						1 - alpha_prod_t
     __lowerCamelCase  :      int														=						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  :      Optional[int]														=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          __lowerCamelCase  :      Union[str, Any]														=						model_output
     else:
          raise ValueError(F'Unsupported prediction_type {prediction_type}.'				)

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

     # 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  :      List[str]														=						(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  :      str														=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

     __lowerCamelCase  :      List[Any]														=						pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=UpperCAmelCase_ ,       pred_original_sample=UpperCAmelCase_				)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	)							->	Dict:
         super().__init__()
         __lowerCamelCase  :      Tuple														=						bit_scale
         __lowerCamelCase  :      str														=						(
             ddim_bit_scheduler_step if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) else ddpm_bit_scheduler_step
         )

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







    @torch.no_grad()
    def __call__(				self     ,	SCREAMING_SNAKE_CASE_ = 2_56     ,	SCREAMING_SNAKE_CASE_ = 2_56     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Union[Tuple, ImagePipelineOutput]:
         __lowerCamelCase  :      Optional[Any]														=						torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)     ,	generator=SCREAMING_SNAKE_CASE_     ,	)
         __lowerCamelCase  :      Optional[Any]														=						decimal_to_bits(SCREAMING_SNAKE_CASE_      ) * self.bit_scale
         __lowerCamelCase  :      Optional[int]														=						latents.to(self.device      )

         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_      )

         for t in self.progress_bar(self.scheduler.timesteps      ):
              # predict the noise residual
              __lowerCamelCase  :      List[str]														=						self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ).sample

              # compute the previous noisy sample x_t -> x_t-1
              __lowerCamelCase  :      Tuple														=						self.scheduler.step(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ).prev_sample

         __lowerCamelCase  :      Dict														=						bits_to_decimal(SCREAMING_SNAKE_CASE_      )

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

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )














"""simple docstring"""





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


__lowerCamelCase							=  logging.get_logger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any					)			->							List[Any]:
											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Dict							=       [label.strip() for label in labels.split(""","""					) if label.strip()]
											return labels






					def __call__(			self  :      Optional[Any]     ,		__snake_case  :      str     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[Any]					)			->							int:
											if len(__snake_case					) == 0 or len(__snake_case					) == 0:
																	raise ValueError("""You must include at least one label and at least one sequence."""					)
											if hypothesis_template.format(labels[0]					) == hypothesis_template:
																	raise ValueError(
																	    (
																	        """The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. """
																	        """Make sure the passed template includes formatting syntax such as {{}} where the label should go."""
																	    ).format(__snake_case					)					)

											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Any							=       [sequences]

											__magic_name__:	List[Any]							=       []
											for sequence in sequences:
																	sequence_pairs.extend([[sequence, hypothesis_template.format(__snake_case					)] for label in labels]					)

											return sequence_pairs, sequences



@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Dict     ,		__snake_case  :      Dict=ZeroShotClassificationArgumentHandler()     ,		*__snake_case  :      Dict     ,		**__snake_case  :      Optional[int]					)			->							int:
											__magic_name__:	List[str]							=       args_parser
											super().__init__(*__snake_case     ,		**__snake_case					)
											if self.entailment_id == -1:
																	logger.warning(
																	    """Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to """
																	    """-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."""					)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											for label, ind in self.model.config.labelaid.items():
																	if label.lower().startswith("""entail"""					):
																							return ind
											return -1

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Tuple     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      Tuple=True     ,		__snake_case  :      Tuple=TruncationStrategy.ONLY_FIRST     ,		**__snake_case  :      Union[str, Any]					)			->							int:
											__magic_name__:	Optional[int]							=       self.framework
											if self.tokenizer.pad_token is None:
																	# Override for tokenizers not supporting padding
																	logger.error(
																	    """Tokenizer was not supporting padding necessary for zero-shot, attempting to use """
																	    """ `pad_token=eos_token`"""					)
																	__magic_name__:	Tuple							=       self.tokenizer.eos_token
											try:
																	__magic_name__:	List[Any]							=       self.tokenizer(
																	    __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		)
											except Exception as e:
																	if "too short" in str(__snake_case					):
																							# tokenizers might yell that we want to truncate
																							# to a value that is not even reached by the input.
																							# In that case we don't want to truncate.
																							# It seems there's not a really better way to catch that
																							# exception.

																							__magic_name__:	List[str]							=       self.tokenizer(
																							    __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		truncation=TruncationStrategy.DO_NOT_TRUNCATE     ,		)
																	else:
																							raise e

											return inputs

					def   lowerCamelCase__   (			self  :      Any     ,		**__snake_case  :      Optional[Any]					)			->							int:
											if kwargs.get("""multi_class"""     ,		__snake_case					) is not None:
																	__magic_name__:	List[str]							=       kwargs["""multi_class"""]
																	logger.warning(
																	    """The `multi_class` argument has been deprecated and renamed to `multi_label`. """
																	    """`multi_class` will be removed in a future version of Transformers."""					)
											__magic_name__:	Optional[Any]							=       {}
											if "candidate_labels" in kwargs:
																	__magic_name__:	str							=       self._args_parser._parse_labels(kwargs["""candidate_labels"""]					)
											if "hypothesis_template" in kwargs:
																	__magic_name__:	Optional[Any]							=       kwargs["""hypothesis_template"""]

											__magic_name__:	int							=       {}
											if "multi_label" in kwargs:
																	__magic_name__:	int							=       kwargs["""multi_label"""]
											return preprocess_params, {}, postprocess_params

					def __call__(			self  :      Any     ,		__snake_case  :      Union[str, List[str]]     ,		*__snake_case  :      Optional[int]     ,		**__snake_case  :      List[str]     ,		)			->							List[str]:
											if len(__snake_case					) == 0:
																	pass
											elif len(__snake_case					) == 1 and "candidate_labels" not in kwargs:
																	__magic_name__:	str							=       args[0]
											else:
																	raise ValueError(F'Unable to understand extra arguments {args}'					)

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

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      int     ,		__snake_case  :      List[Any]=None     ,		__snake_case  :      Tuple="This example is {}."					)			->							List[Any]:
											__magic_name__,			__magic_name__:	int							=       self._args_parser(__snake_case     ,		__snake_case     ,		__snake_case					)

											for i, (candidate_label, sequence_pair) in enumerate(zip(__snake_case     ,		__snake_case					)					):
																	__magic_name__:	Dict							=       self._parse_and_tokenize([sequence_pair]					)

																	yield {
																	    "candidate_label": candidate_label,
																	    "sequence": sequences[0],
																	    "is_last": i == len(__snake_case					) - 1,
																	    **model_input,
																	}

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      int					)			->							Tuple:
											__magic_name__:	Optional[Any]							=       inputs["""candidate_label"""]
											__magic_name__:	List[str]							=       inputs["""sequence"""]
											__magic_name__:	Any							=       {k: inputs[k] for k in self.tokenizer.model_input_names}
											__magic_name__:	Optional[int]							=       self.model(**__snake_case					)

											__magic_name__:	Optional[Any]							=       {
											    """candidate_label""": candidate_label,
											    """sequence""": sequence,
											    """is_last""": inputs["""is_last"""],
											    **outputs,
											}
											return model_outputs






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      str     ,		__snake_case  :      List[Any]=False					)			->							Any:
											__magic_name__:	List[Any]							=       [outputs["""candidate_label"""] for outputs in model_outputs]
											__magic_name__:	Any							=       [outputs["""sequence"""] for outputs in model_outputs]
											__magic_name__:	Tuple							=       np.concatenate([output["""logits"""].numpy() for output in model_outputs]					)
											__magic_name__:	Any							=       logits.shape[0]
											__magic_name__:	Any							=       len(__snake_case					)
											__magic_name__:	List[str]							=       N // n
											__magic_name__:	int							=       logits.reshape((num_sequences, n, -1)					)

											if multi_label or len(__snake_case					) == 1:
																	# softmax over the entailment vs. contradiction dim for each label independently
																	__magic_name__:	List[Any]							=       self.entailment_id
																	__magic_name__:	List[str]							=       -1 if entailment_id == 0 else 0
																	__magic_name__:	Tuple							=       reshaped_outputs[..., [contradiction_id, entailment_id]]
																	__magic_name__:	int							=       np.exp(__snake_case					) / np.exp(__snake_case					).sum(-1     ,		keepdims=__snake_case					)
																	__magic_name__:	List[str]							=       scores[..., 1]
											else:
																	# softmax the "entailment" logits over all candidate labels
																	__magic_name__:	List[str]							=       reshaped_outputs[..., self.entailment_id]
																	__magic_name__:	Tuple							=       np.exp(__snake_case					) / np.exp(__snake_case					).sum(-1     ,		keepdims=__snake_case					)

											__magic_name__:	Optional[Any]							=       list(reversed(scores[0].argsort()					)					)
											return {
											    "sequence": sequences[0],
											    "labels": [candidate_labels[i] for i in top_inds],
											    "scores": scores[0, top_inds].tolist(),
											}



