

"""simple docstring"""





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : torch.FloatTensor
 __UpperCAmelCase     : Optional[torch.FloatTensor]       		=     None







def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	str=0.999,      UpperCamelCase_					:	str="cosine",      )     ->						Optional[Any]:
   '''simple docstring'''


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCamelCase_					:	Tuple):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCamelCase_					:	List[Any]):
         return math.exp(t * -12.0)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""")

   __lowercase								=						[]
   for i in range(UpperCamelCase_):
      __lowercase								=						i / num_diffusion_timesteps
      __lowercase								=						(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(UpperCamelCase_) / alpha_bar_fn(UpperCamelCase_),      UpperCamelCase_))
   return torch.tensor(UpperCamelCase_,      dtype=torch.floataa)







class  _lowerCAmelCase  (     lowercase					,lowercase       ):







 """simple docstring"""







 @register_to_config
 def __init__(      self							:      Tuple, UpperCAmelCase__							:      int = 1_0_0_0, UpperCAmelCase__							:      str = "fixed_small_log", UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[float] = 1.0, UpperCAmelCase__							:      str = "epsilon", UpperCAmelCase__							:      str = "squaredcos_cap_v2", ):
    if beta_schedule != "squaredcos_cap_v2":
       raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"     )

    __lowercase								=						betas_for_alpha_bar(UpperCAmelCase__     )

    __lowercase								=						1.0 - self.betas
    __lowercase								=						torch.cumprod(self.alphas, dim=0     )
    __lowercase								=						torch.tensor(1.0     )

    # standard deviation of the initial noise distribution
    __lowercase								=						1.0

    # setable values
    __lowercase								=						None
    __lowercase								=						torch.from_numpy(np.arange(0, UpperCAmelCase__     )[::-1].copy()     )

    __lowercase								=						variance_type


 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      Optional[int] = None     ):
    return sample


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      int, UpperCAmelCase__							:      Union[str, torch.device] = None     ):
    __lowercase								=						num_inference_steps
    __lowercase								=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
    __lowercase								=						(np.arange(0, UpperCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
    __lowercase								=						torch.from_numpy(UpperCAmelCase__     ).to(UpperCAmelCase__     )


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      Any=None, UpperCAmelCase__							:      str=None, UpperCAmelCase__							:      Dict=None     ):
    if prev_timestep is None:
       __lowercase								=						t - 1

    __lowercase								=						self.alphas_cumprod[t]
    __lowercase								=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    __lowercase								=						1 - alpha_prod_t
    __lowercase								=						1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
       __lowercase								=						self.betas[t]
    else:
       __lowercase								=						1 - alpha_prod_t / alpha_prod_t_prev

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
    # and sample from it to get previous sample
    # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
    __lowercase								=						beta_prod_t_prev / beta_prod_t * beta

    if variance_type is None:
       __lowercase								=						self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small_log":
       __lowercase								=						torch.log(torch.clamp(UpperCAmelCase__, min=1E-20     )     )
       __lowercase								=						torch.exp(0.5 * variance     )
    elif variance_type == "learned_range":
       # NOTE difference with DDPM scheduler
       __lowercase								=						variance.log()
       __lowercase								=						beta.log()

       __lowercase								=						(predicted_variance + 1) / 2
       __lowercase								=						frac * max_log + (1 - frac) * min_log

    return variance


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      int, UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      Optional[int] = None, UpperCAmelCase__							:      str=None, UpperCAmelCase__							:      bool = True, ):
    __lowercase								=						timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
       __lowercase		,__lowercase								=						torch.split(UpperCAmelCase__, sample.shape[1], dim=1     )
    else:
       __lowercase								=						None

    # 1. compute alphas, betas
    if prev_timestep is None:
       __lowercase								=						t - 1

    __lowercase								=						self.alphas_cumprod[t]
    __lowercase								=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    __lowercase								=						1 - alpha_prod_t
    __lowercase								=						1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
       __lowercase								=						self.betas[t]
       __lowercase								=						self.alphas[t]
    else:
       __lowercase								=						1 - alpha_prod_t / alpha_prod_t_prev
       __lowercase								=						1 - beta

    # 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 self.config.prediction_type == "epsilon":
       __lowercase								=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
       __lowercase								=						model_output
    else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
           " for the UnCLIPScheduler."     )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       __lowercase								=						torch.clamp(
           UpperCAmelCase__, -self.config.clip_sample_range, self.config.clip_sample_range     )

    # 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								=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
    __lowercase								=						alpha ** 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								=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    __lowercase								=						0
    if t > 0:
       __lowercase								=						randn_tensor(
           model_output.shape, dtype=model_output.dtype, generator=UpperCAmelCase__, device=model_output.device     )

       __lowercase								=						self._get_variance(
           UpperCAmelCase__, predicted_variance=UpperCAmelCase__, prev_timestep=UpperCAmelCase__, )

       if self.variance_type == "fixed_small_log":
          __lowercase								=						variance
       elif self.variance_type == "learned_range":
          __lowercase								=						(0.5 * variance).exp()
       else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              " for the UnCLIPScheduler."     )

       __lowercase								=						variance * variance_noise

    __lowercase								=						pred_prev_sample + variance

    if not return_dict:
       return (pred_prev_sample,)

    return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase__, pred_original_sample=UpperCAmelCase__     )



 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      torch.FloatTensor, UpperCAmelCase__							:      torch.IntTensor, ):
    # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
    __lowercase								=						self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype     )
    __lowercase								=						timesteps.to(original_samples.device     )

    __lowercase								=						alphas_cumprod[timesteps] ** 0.5
    __lowercase								=						sqrt_alpha_prod.flatten()
    while len(sqrt_alpha_prod.shape     ) < len(original_samples.shape     ):
       __lowercase								=						sqrt_alpha_prod.unsqueeze(-1     )

    __lowercase								=						(1 - alphas_cumprod[timesteps]) ** 0.5
    __lowercase								=						sqrt_one_minus_alpha_prod.flatten()
    while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
       __lowercase								=						sqrt_one_minus_alpha_prod.unsqueeze(-1     )

    __lowercase								=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples







"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[str], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Dict     ):
    __lowercase								=						dataset
    __lowercase								=						process
    __lowercase								=						params


 def __len__(      self							:      str     ):
    return len(self.dataset     )



 def __getitem__(      self							:      List[Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						self.dataset[i]
    __lowercase								=						self.process(UpperCAmelCase__, **self.params     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[Any]=None     ):
    __lowercase								=						loader
    __lowercase								=						infer
    __lowercase								=						params
    if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       __lowercase								=						None
    __lowercase								=						loader_batch_size

    # Internal bookkeeping
    __lowercase								=						None
    __lowercase								=						None


 def __len__(      self							:      str     ):
    return len(self.loader     )


 def __iter__(      self							:      List[str]     ):
    __lowercase								=						iter(self.loader     )
    return self


 def  _lowercase			(      self							:      Union[str, Any]     ):
    if isinstance(self._loader_batch_data, torch.Tensor     ):
       # Batch data is simple tensor, just fetch the slice
       __lowercase								=						self._loader_batch_data[self._loader_batch_index]
    else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       __lowercase								=						{}
       for k, element in self._loader_batch_data.items():
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Convert ModelOutput to tuple first
             __lowercase								=						element.to_tuple()
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						tuple(np.expand_dims(el[self._loader_batch_index], 0     ) for el in element     )
             continue
          if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Those are stored as lists of tensors so need specific unbatching.
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						tuple(np.expand_dims(el[self._loader_batch_index], 0     ) for el in element     )
             continue
          if element is None:
             # This can happen for optional data that get passed around
             __lowercase								=						None
          elif isinstance(element[self._loader_batch_index], torch.Tensor     ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers

             __lowercase								=						element[self._loader_batch_index].unsqueeze(0     )
          elif isinstance(element[self._loader_batch_index], np.ndarray     ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers
             __lowercase								=						np.expand_dims(element[self._loader_batch_index], 0     )
          else:
             # This is typically a list, so no need to `unsqueeze`.
             __lowercase								=						element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
       __lowercase								=						self._loader_batch_data.__class__(UpperCAmelCase__     )
    self._loader_batch_index += 1
    return result



 def  _lowercase			(      self							:      Tuple     ):
    if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       # We are currently unrolling a batch so we just need to return
       # the current item within a batch
       return self.loader_batch_item()

    # We're out of items within a batch
    __lowercase								=						next(self.iterator     )
    __lowercase								=						self.infer(UpperCAmelCase__, **self.params     )
    # We now have a batch of "inferred things".
    if self.loader_batch_size is not None:
       # Try to infer the size of the batch
       if isinstance(UpperCAmelCase__, torch.Tensor     ):
          __lowercase								=						processed
       else:
          __lowercase								=						list(processed.keys()     )[0]
          __lowercase								=						processed[key]
       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase								=						len(UpperCAmelCase__     )
       else:
          __lowercase								=						first_tensor.shape[0]
       if 0 < observed_batch_size < self.loader_batch_size:
          # could be last batch so we can't unroll as many
          # elements.
          __lowercase								=						observed_batch_size
       # Setting internal index to unwrap the batch
       __lowercase								=						processed
       __lowercase								=						0
       return self.loader_batch_item()
    else:
       # We're not unrolling batches
       return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      int, UpperCAmelCase__							:      str=None     ):
    super().__init__(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )


 def __iter__(      self							:      str     ):
    __lowercase								=						iter(self.loader     )
    __lowercase								=						None
    return self



 def  _lowercase			(      self							:      int     ):
    if self.subiterator is None:
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
    try:
       # Try to return next item
       __lowercase								=						next(self.subiterator     )
    except StopIteration:
       # When a preprocess iterator ends, we can start lookig at the next item
       # ChunkIterator will keep feeding until ALL elements of iterator
       # all have created their subiterator and have been iterating against.
       #
       # Another way to look at it, is we're basically flattening lists of lists
       # into a single list, but with generators
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
       __lowercase								=						next(self.subiterator     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __iter__(      self							:      int     ):
    __lowercase								=						iter(self.loader     )
    return self



 def  _lowercase			(      self							:      List[str]     ):
    # Extremely similar to PipelineIterator in its unpacking mechanism
    # BUT, we have an extra required item which is the presence of `is_last`
    # That is because everything is flattened by `PipelineChunkIterator` we
    # need to keep track of how to regroup here in the original `process`
    # boundaries so that `process` and `postprocess` see the same data.

    # This iterator accumulates items (possibly while unbatching) until it
    # its a `is_last` and then just passes it on to the caller.
    __lowercase								=						False
    __lowercase								=						[]
    if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       while self._loader_batch_index < self.loader_batch_size:
          __lowercase								=						self.loader_batch_item()
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
          if is_last:
             return accumulator

    while not is_last:
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
       if self.loader_batch_size is not None:
          if isinstance(UpperCAmelCase__, torch.Tensor     ):
             __lowercase								=						processed
          else:
             __lowercase								=						list(processed.keys()     )[0]
             __lowercase								=						processed[key]
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             __lowercase								=						len(UpperCAmelCase__     )
          else:
             __lowercase								=						first_tensor.shape[0]
          if 0 < observed_batch_size < self.loader_batch_size:
             # could be last batch so we can't unroll as many
             # elements.
             __lowercase								=						observed_batch_size
          __lowercase								=						processed
          __lowercase								=						0
          while self._loader_batch_index < self.loader_batch_size:
             __lowercase								=						self.loader_batch_item()
             __lowercase								=						item.pop("is_last"     )
             accumulator.append(UpperCAmelCase__     )
             if is_last:
                return accumulator
       else:
          __lowercase								=						processed
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
    return accumulator







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[Any], UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						key


 def __len__(      self							:      Optional[Any]     ):
    return len(self.dataset     )



 def __getitem__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any     ):
    return self.dataset[i][self.key]







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      str, UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						keya
    __lowercase								=						keya


 def __len__(      self							:      Optional[int]     ):
    return len(self.dataset     )



 def __getitem__(      self							:      Dict, UpperCAmelCase__							:      Tuple     ):
    return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




