

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


snake_case_			  = 8

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    str=BITS			):



     '''simple docstring'''





     lowercase__    :   str      =       x.device

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

     lowercase__    :   Tuple      =       2 ** torch.arange(bits - 1	,     -1	,     -1	,     device=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Dict      =       rearrange(SCREAMING_SNAKE_CASE_	,     'd -> d 1 1'			)
     lowercase__    :   Tuple      =       rearrange(SCREAMING_SNAKE_CASE_	,     'b c h w -> b c 1 h w'			)

     lowercase__    :   Tuple      =       ((x & mask) != 0).float()
     lowercase__    :   Optional[int]      =       rearrange(SCREAMING_SNAKE_CASE_	,     'b c d h w -> b (c d) h w'			)
     lowercase__    :   Optional[int]      =       bits * 2 - 1
     return bits

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    int=BITS			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       x.device

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

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

def 	snake_case__			(    self		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    torch.FloatTensor	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    torch.FloatTensor	,     SCREAMING_SNAKE_CASE_		:    float = 0.0	,     SCREAMING_SNAKE_CASE_		:    bool = True	,     SCREAMING_SNAKE_CASE_		:    Dict=None	,     SCREAMING_SNAKE_CASE_		:    bool = True	,     ):



     '''simple docstring'''





     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)
     lowercase__    :   Optional[Any]      =       timestep - self.config.num_train_timesteps // self.num_inference_steps

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

     lowercase__    :   List[str]      =       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
     lowercase__    :   Tuple      =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     lowercase__    :   Dict      =       self.bit_scale
     if self.config.clip_sample:
          lowercase__    :   List[str]      =       torch.clamp(SCREAMING_SNAKE_CASE_	,     -scale	,     SCREAMING_SNAKE_CASE_			)

     # 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)
     lowercase__    :   List[str]      =       self._get_variance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Union[str, Any]      =       eta * variance ** 0.5

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

          lowercase__    :   Tuple      =       prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_	,     pred_original_sample=SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    self		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    torch.FloatTensor	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    torch.FloatTensor	,     SCREAMING_SNAKE_CASE_		:    Tuple="epsilon"	,     SCREAMING_SNAKE_CASE_		:    List[Any]=None	,     SCREAMING_SNAKE_CASE_		:    bool = True	,     ):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       timestep

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

     # 1. compute alphas, betas
     lowercase__    :   int      =       self.alphas_cumprod[t]
     lowercase__    :   int      =       self.alphas_cumprod[t - 1] if t > 0 else self.one
     lowercase__    :   Optional[int]      =       1 - alpha_prod_t
     lowercase__    :   Dict      =       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":
          lowercase__    :   Tuple      =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          lowercase__    :   Dict      =       model_output
     else:
          raise ValueError(f"""Unsupported prediction_type {prediction_type}."""			)

     # 3. Clip "predicted x_0"
     lowercase__    :   Optional[int]      =       self.bit_scale
     if self.config.clip_sample:
          lowercase__    :   Dict      =       torch.clamp(SCREAMING_SNAKE_CASE_	,     -scale	,     SCREAMING_SNAKE_CASE_			)

     # 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
     lowercase__    :   Dict      =       (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     lowercase__    :   Optional[Any]      =       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
     lowercase__    :   str      =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     lowercase__    :   Union[str, Any]      =       0
     if t > 0:
          lowercase__    :   List[Any]      =       torch.randn(
              model_output.size()	,     dtype=model_output.dtype	,     layout=model_output.layout	,     generator=SCREAMING_SNAKE_CASE_			).to(model_output.device			)
          lowercase__    :   Any      =       (self._get_variance(SCREAMING_SNAKE_CASE_	,     predicted_variance=SCREAMING_SNAKE_CASE_			) ** 0.5) * noise

     lowercase__    :   Union[str, Any]      =       pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_	,     pred_original_sample=SCREAMING_SNAKE_CASE_			)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   a						,   a						,   a = 1.0						,   ):
         super().__init__()
         lowercase__    :   int      =       bit_scale
         lowercase__    :   Any      =       (
             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						,   a = 256						,   a = 256						,   a = 50						,   a = None						,   a = 1						,   a = "pil"						,   a = True						,   **a						,   ):
         lowercase__    :   Optional[int]      =       torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)						,   generator=a						,   )
         lowercase__    :   Any      =       decimal_to_bits(a) * self.bit_scale
         lowercase__    :   List[str]      =       latents.to(self.device)

         self.scheduler.set_timesteps(a)

         for t in self.progress_bar(self.scheduler.timesteps):
              # predict the noise residual
              lowercase__    :   str      =       self.unet(a						,   a).sample

              # compute the previous noisy sample x_t -> x_t-1
              lowercase__    :   Optional[int]      =       self.scheduler.step(a						,   a						,   a).prev_sample

         lowercase__    :   int      =       bits_to_decimal(a)

         if output_type == "pil":
              lowercase__    :   List[str]      =       self.numpy_to_pil(a)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=a)




import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''simple docstring'''





     return 1.0 / (1.0 + np.exp(-_outputs			))

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__    :   int      =       np.max(_outputs	,     axis=-1	,     keepdims=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   int      =       np.exp(_outputs - maxes			)
     return shifted_exp / shifted_exp.sum(axis=-1	,     keepdims=SCREAMING_SNAKE_CASE_			)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Optional[int]														=     """sigmoid"""
    __lowerCamelCase						:       List[Any]														=     """softmax"""
    __lowerCamelCase						:       Optional[Any]														=     """none"""



@add_end_docstrings(
    __snake_case		,       r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """		,       )
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Dict														=     False
    __lowerCamelCase						:       List[str]														=     ClassificationFunction.NONE





    def __init__(      self						,   **a):
         super().__init__(**a)

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == 'tf'
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)





    def       snake_case_				(      self						,   a=None						,   a=None						,   a=""						,   **a):
         # Using "" as default argument because we're going to use `top_k=None` in user code to declare
         # "No top_k"
         lowercase__    :   List[str]      =       tokenizer_kwargs

         lowercase__    :   Dict      =       {}
         if hasattr(self.model.config						,   'return_all_scores') and return_all_scores is None:
              lowercase__    :   Union[str, Any]      =       self.model.config.return_all_scores

         if isinstance(a						,   a) or top_k is None:
              lowercase__    :   Tuple      =       top_k
              lowercase__    :   Any      =       False
         elif return_all_scores is not None:
              warnings.warn(
                  '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                  ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'						,   a						,   )
              if return_all_scores:
                   lowercase__    :   Union[str, Any]      =       None
              else:
                   lowercase__    :   Union[str, Any]      =       1

         if isinstance(a						,   a):
              lowercase__    :   Tuple      =       ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
              lowercase__    :   List[str]      =       function_to_apply
         return preprocess_params, {}, postprocess_params





    def __call__(      self						,   *a						,   **a):
         lowercase__    :   int      =       super().__call__(*a						,   **a)
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         lowercase__    :   Any      =       'top_k' not in kwargs
         if isinstance(args[0]						,   a) and _legacy:
              # This pipeline is odd, and return a list when single item is run
              return [result]
         else:
              return result





    def       snake_case_				(      self						,   a						,   **a):
         lowercase__    :   List[str]      =       self.framework
         if isinstance(a						,   a):
              return self.tokenizer(**a						,   return_tensors=a						,   **a)
         elif isinstance(a						,   a) and len(a) == 1 and isinstance(inputs[0]						,   a) and len(inputs[0]) == 2:
              # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
              return self.tokenizer(
                  text=inputs[0][0]						,   text_pair=inputs[0][1]						,   return_tensors=a						,   **a)
         elif isinstance(a						,   a):
              # This is likely an invalid usage of the pipeline attempting to pass text pairs.
              raise ValueError(
                  'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                  ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.')
         return self.tokenizer(a						,   return_tensors=a						,   **a)





    def       snake_case_				(      self						,   a):
         return self.model(**a)





    def       snake_case_				(      self						,   a						,   a=None						,   a=1						,   a=True):
         # `_legacy` is used to determine if we're running the naked pipeline and in backward
         # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
         # the more natural result containing the list.
         # Default value before `set_parameters`
         if function_to_apply is None:
              if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                   lowercase__    :   Union[str, Any]      =       ClassificationFunction.SIGMOID
              elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   lowercase__    :   Union[str, Any]      =       ClassificationFunction.SOFTMAX
              elif hasattr(self.model.config						,   'function_to_apply') and function_to_apply is None:
                   lowercase__    :   List[str]      =       self.model.config.function_to_apply
              else:
                   lowercase__    :   Union[str, Any]      =       ClassificationFunction.NONE

         lowercase__    :   str      =       model_outputs['logits'][0]
         lowercase__    :   Dict      =       outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
              lowercase__    :   int      =       sigmoid(a)
         elif function_to_apply == ClassificationFunction.SOFTMAX:
              lowercase__    :   List[Any]      =       softmax(a)
         elif function_to_apply == ClassificationFunction.NONE:
              lowercase__    :   Any      =       outputs
         else:
              raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""")

         if top_k == 1 and _legacy:
              return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         lowercase__    :   Any      =       [
             {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a)
         ]
         if not _legacy:
              dict_scores.sort(key=lambda a: x["score"]						,   reverse=a)
              if top_k is not None:
                   lowercase__    :   Optional[Any]      =       dict_scores[:top_k]
         return dict_scores

