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

from ..utils.generic import ModelOutput





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Tuple    =		dataset
       lowerCAmelCase_					:       Optional[int]    =		process
       lowerCAmelCase_					:       Dict    =		params







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







  def __getitem__(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Union[str, Any]    =		self.dataset[i]
       lowerCAmelCase_					:       Optional[Any]    =		self.process(UpperCamelCase_    ,						**self.params						)
       return processed





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase=None						):
       lowerCAmelCase_					:       Optional[Any]    =		loader
       lowerCAmelCase_					:       Tuple    =		infer
       lowerCAmelCase_					:       int    =		params
       if loader_batch_size == 1:
            # Let's spare some time by deactivating altogether
            lowerCAmelCase_					:       Optional[int]    =		None
       lowerCAmelCase_					:       List[str]    =		loader_batch_size

       # Internal bookkeeping
       lowerCAmelCase_					:       Optional[Any]    =		None
       lowerCAmelCase_					:       Any    =		None







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







  def __iter__(	self						):
       lowerCAmelCase_					:       Tuple    =		iter(self.loader						)
       return self







  def 	UpperCAmelCase__		(	self						):
       if isinstance(self._loader_batch_data    ,						torch.Tensor						):
            # Batch data is simple tensor, just fetch the slice
            lowerCAmelCase_					:       Union[str, Any]    =		self._loader_batch_data[self._loader_batch_index]
       else:
            # Batch data is assumed to be BaseModelOutput (or dict)
            lowerCAmelCase_					:       Any    =		{}
            for k, element in self._loader_batch_data.items():
                 if isinstance(UpperCamelCase_    ,						UpperCamelCase_						):
                      # Convert ModelOutput to tuple first
                      lowerCAmelCase_					:       int    =		element.to_tuple()
                      if isinstance(element[0]    ,						torch.Tensor						):
                           lowerCAmelCase_					:       str    =		tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                      elif isinstance(element[0]    ,						np.ndarray						):
                           lowerCAmelCase_					:       Optional[int]    =		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						):
                           lowerCAmelCase_					:       Optional[int]    =		tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
                      elif isinstance(element[0]    ,						np.ndarray						):
                           lowerCAmelCase_					:       List[str]    =		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
                      lowerCAmelCase_					:       int    =		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

                      lowerCAmelCase_					:       str    =		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
                      lowerCAmelCase_					:       List[str]    =		np.expand_dims(element[self._loader_batch_index]    ,						0						)
                 else:
                      # This is typically a list, so no need to `unsqueeze`.
                      lowerCAmelCase_					:       List[str]    =		element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
            lowerCAmelCase_					:       Optional[Any]    =		self._loader_batch_data.__class__(UpperCamelCase_						)
       self._loader_batch_index += 1
       return result







  def 	UpperCAmelCase__		(	self						):
       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
       lowerCAmelCase_					:       Optional[int]    =		next(self.iterator						)
       lowerCAmelCase_					:       int    =		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						):
                 lowerCAmelCase_					:       Optional[int]    =		processed
            else:
                 lowerCAmelCase_					:       Dict    =		list(processed.keys()						)[0]
                 lowerCAmelCase_					:       Tuple    =		processed[key]
            if isinstance(UpperCamelCase_    ,						UpperCamelCase_						):
                 lowerCAmelCase_					:       Any    =		len(UpperCamelCase_						)
            else:
                 lowerCAmelCase_					:       Tuple    =		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.
                 lowerCAmelCase_					:       Optional[int]    =		observed_batch_size
            # Setting internal index to unwrap the batch
            lowerCAmelCase_					:       Tuple    =		processed
            lowerCAmelCase_					:       Union[str, Any]    =		0
            return self.loader_batch_item()
       else:
            # We're not unrolling batches
            return processed





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase=None						):
       super().__init__(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_						)







  def __iter__(	self						):
       lowerCAmelCase_					:       Any    =		iter(self.loader						)
       lowerCAmelCase_					:       List[Any]    =		None
       return self







  def 	UpperCAmelCase__		(	self						):
       if self.subiterator is None:
            lowerCAmelCase_					:       Any    =		self.infer(next(self.iterator						)    ,						**self.params						)
       try:
            # Try to return next item
            lowerCAmelCase_					:       Tuple    =		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
            lowerCAmelCase_					:       List[str]    =		self.infer(next(self.iterator						)    ,						**self.params						)
            lowerCAmelCase_					:       List[Any]    =		next(self.subiterator						)
       return processed





class      lowercase__    (							__A  ):







  def __iter__(	self						):
       lowerCAmelCase_					:       Tuple    =		iter(self.loader						)
       return self







  def 	UpperCAmelCase__		(	self						):
       # 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.
       lowerCAmelCase_					:       Union[str, Any]    =		False
       lowerCAmelCase_					:       Any    =		[]
       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:
                 lowerCAmelCase_					:       int    =		self.loader_batch_item()
                 lowerCAmelCase_					:       Optional[int]    =		item.pop("""is_last"""						)
                 accumulator.append(UpperCamelCase_						)
                 if is_last:
                      return accumulator

       while not is_last:
            lowerCAmelCase_					:       List[Any]    =		self.infer(next(self.iterator						)    ,						**self.params						)
            if self.loader_batch_size is not None:
                 if isinstance(UpperCamelCase_    ,						torch.Tensor						):
                      lowerCAmelCase_					:       Dict    =		processed
                 else:
                      lowerCAmelCase_					:       Tuple    =		list(processed.keys()						)[0]
                      lowerCAmelCase_					:       Optional[Any]    =		processed[key]
                 if isinstance(UpperCamelCase_    ,						UpperCamelCase_						):
                      lowerCAmelCase_					:       Union[str, Any]    =		len(UpperCamelCase_						)
                 else:
                      lowerCAmelCase_					:       Dict    =		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.
                      lowerCAmelCase_					:       List[Any]    =		observed_batch_size
                 lowerCAmelCase_					:       Union[str, Any]    =		processed
                 lowerCAmelCase_					:       int    =		0
                 while self._loader_batch_index < self.loader_batch_size:
                      lowerCAmelCase_					:       int    =		self.loader_batch_item()
                      lowerCAmelCase_					:       str    =		item.pop("""is_last"""						)
                      accumulator.append(UpperCamelCase_						)
                      if is_last:
                           return accumulator
            else:
                 lowerCAmelCase_					:       List[Any]    =		processed
                 lowerCAmelCase_					:       Optional[int]    =		item.pop("""is_last"""						)
                 accumulator.append(UpperCamelCase_						)
       return accumulator





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Union[str, Any]    =		dataset
       lowerCAmelCase_					:       List[str]    =		key







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







  def __getitem__(	self    ,						_lowercase						):
       return self.dataset[i][self.key]





class      lowercase__    (							__A  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Union[str, Any]    =		dataset
       lowerCAmelCase_					:       Union[str, Any]    =		keya
       lowerCAmelCase_					:       Optional[Any]    =		keya







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







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







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""vqvae"""]







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						):
       super().__init__()
       self.register_modules(unet=_lowercase    ,						scheduler=_lowercase    ,						mel=_lowercase    ,						vqvae=_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       return 50 if isinstance(self.scheduler    ,						_lowercase						) else 1_000







  @torch.no_grad()
  def __call__(	self    ,						_lowercase = 1    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase=True    ,						):
       lowerCAmelCase_					:       Tuple    =		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       Union[str, Any]    =		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size						) == int:
            lowerCAmelCase_					:       Optional[Any]    =		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
            lowerCAmelCase_					:       Tuple    =		randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                )    ,						generator=_lowercase    ,						device=self.device    ,						)
       lowerCAmelCase_					:       List[str]    =		noise
       lowerCAmelCase_					:       List[str]    =		None

       if audio_file is not None or raw_audio is not None:
            self.mel.load_audio(_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       Tuple    =		self.mel.audio_slice_to_image(_lowercase						)
            lowerCAmelCase_					:       List[Any]    =		np.frombuffer(input_image.tobytes()    ,						dtype="""uint8"""						).reshape(
                (input_image.height, input_image.width)						)
            lowerCAmelCase_					:       Optional[Any]    =		(input_image / 255) * 2 - 1
            lowerCAmelCase_					:       Optional[Any]    =		torch.tensor(input_image[np.newaxis, :, :]    ,						dtype=torch.float						).to(self.device						)

            if self.vqvae is not None:
                 lowerCAmelCase_					:       List[Any]    =		self.vqvae.encode(torch.unsqueeze(_lowercase    ,						0						)						).latent_dist.sample(
                     generator=_lowercase						)[0]
                 lowerCAmelCase_					:       str    =		self.vqvae.config.scaling_factor * input_images

            if start_step > 0:
                 lowerCAmelCase_					:       str    =		self.scheduler.add_noise(_lowercase    ,						_lowercase    ,						self.scheduler.timesteps[start_step - 1]						)

            lowerCAmelCase_					:       Dict    =		(
                self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
            )
            lowerCAmelCase_					:       Union[str, Any]    =		int(mask_start_secs * pixels_per_second						)
            lowerCAmelCase_					:       str    =		int(mask_end_secs * pixels_per_second						)
            lowerCAmelCase_					:       List[Any]    =		self.scheduler.add_noise(_lowercase    ,						_lowercase    ,						torch.tensor(self.scheduler.timesteps[start_step:]						)						)

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
            if isinstance(self.unet    ,						_lowercase						):
                 lowerCAmelCase_					:       List[Any]    =		self.unet(_lowercase    ,						_lowercase    ,						_lowercase						)["""sample"""]
            else:
                 lowerCAmelCase_					:       Any    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]

            if isinstance(self.scheduler    ,						_lowercase						):
                 lowerCAmelCase_					:       str    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						eta=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]
            else:
                 lowerCAmelCase_					:       List[str]    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]

            if mask is not None:
                 if mask_start > 0:
                      lowerCAmelCase_					:       Optional[Any]    =		mask[:, step, :, :mask_start]
                 if mask_end > 0:
                      lowerCAmelCase_					:       Optional[Any]    =		mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
            # 0.18215 was scaling factor used in training to ensure unit variance
            lowerCAmelCase_					:       Dict    =		1 / self.vqvae.config.scaling_factor * images
            lowerCAmelCase_					:       Dict    =		self.vqvae.decode(_lowercase						)["""sample"""]

       lowerCAmelCase_					:       List[Any]    =		(images / 2 + 0.5).clamp(0    ,						1						)
       lowerCAmelCase_					:       List[Any]    =		images.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
       lowerCAmelCase_					:       Optional[Any]    =		(images * 255).round().astype("""uint8"""						)
       lowerCAmelCase_					:       str    =		list(
           (Image.fromarray(_[:, :, 0]						) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(_lowercase    ,						mode="""RGB"""						).convert("""L"""						) for _ in images)						)

       lowerCAmelCase_					:       str    =		[self.mel.image_to_audio(_lowercase						) for _ in images]
       if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(_lowercase						)[:, np.newaxis, :]						)    ,						**ImagePipelineOutput(_lowercase						)						)







  @torch.no_grad()
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = 50						):
       assert isinstance(self.scheduler    ,						_lowercase						)
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		np.array(
           [np.frombuffer(image.tobytes()    ,						dtype="""uint8"""						).reshape((1, image.height, image.width)						) for image in images]						)
       lowerCAmelCase_					:       List[str]    =		(sample / 255) * 2 - 1
       lowerCAmelCase_					:       Optional[Any]    =		torch.Tensor(_lowercase						).to(self.device						)

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,						(0,)						)						):
            lowerCAmelCase_					:       int    =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
            lowerCAmelCase_					:       Optional[int]    =		self.scheduler.alphas_cumprod[t]
            lowerCAmelCase_					:       Any    =		(
                self.scheduler.alphas_cumprod[prev_timestep]
                if prev_timestep >= 0
                else self.scheduler.final_alpha_cumprod
            )
            lowerCAmelCase_					:       Optional[int]    =		1 - alpha_prod_t
            lowerCAmelCase_					:       Union[str, Any]    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]
            lowerCAmelCase_					:       int    =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
            lowerCAmelCase_					:       Dict    =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
            lowerCAmelCase_					:       Optional[Any]    =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample







  @staticmethod
  def 	UpperCAmelCase__		(	_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		acos(torch.dot(torch.flatten(_lowercase						)    ,						torch.flatten(_lowercase						)						) / torch.norm(_lowercase						) / torch.norm(_lowercase						)						)
       return sin((1 - alpha) * theta						) * xa / sin(_lowercase						) + sin(alpha * theta						) * xa / sin(_lowercase						)
