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						(						__UpperCAmelCase ):
   lowerCamelCase_    					=["""vqvae"""]


   def __init__(   self				:	List[str]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any   ,		)      ->					List[Any]:
         super().__init__()
         self.register_modules(unet=UpperCAmelCase_   ,		scheduler=UpperCAmelCase_   ,		mel=UpperCAmelCase_   ,		vqvae=UpperCAmelCase_)


   def __UpperCAmelCase       (   self				:	str)      ->					List[Any]:
         return 50 if isinstance(self.scheduler   ,		UpperCAmelCase_) else 1000


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

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

               if self.vqvae is not None:
                     lowercase_     =      self.vqvae.encode(torch.unsqueeze(UpperCAmelCase_   ,		0)).latent_dist.sample(
                         generator=UpperCAmelCase_)[0]
                     lowercase_     =      self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                     lowercase_     =      self.scheduler.add_noise(UpperCAmelCase_   ,		UpperCAmelCase_   ,		self.scheduler.timesteps[start_step - 1])

               lowercase_     =      (
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               lowercase_     =      int(mask_start_secs * pixels_per_second)
               lowercase_     =      int(mask_end_secs * pixels_per_second)
               lowercase_     =      self.scheduler.add_noise(UpperCAmelCase_   ,		UpperCAmelCase_   ,		torch.tensor(self.scheduler.timesteps[start_step:]))

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

               if isinstance(self.scheduler   ,		UpperCAmelCase_):
                     lowercase_     =      self.scheduler.step(
                         model_output=UpperCAmelCase_   ,		timestep=UpperCAmelCase_   ,		sample=UpperCAmelCase_   ,		eta=UpperCAmelCase_   ,		generator=UpperCAmelCase_   ,		)["prev_sample"]
               else:
                     lowercase_     =      self.scheduler.step(
                         model_output=UpperCAmelCase_   ,		timestep=UpperCAmelCase_   ,		sample=UpperCAmelCase_   ,		generator=UpperCAmelCase_   ,		)["prev_sample"]

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

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

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

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

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


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

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

         return sample


   @staticmethod
   def __UpperCAmelCase       (   __lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[Any])      ->					List[str]:
         lowercase_     =      acos(torch.dot(torch.flatten(UpperCAmelCase_)   ,		torch.flatten(UpperCAmelCase_)) / torch.norm(UpperCAmelCase_) / torch.norm(UpperCAmelCase_))
         return sin((1 - alpha) * theta) * xa / sin(UpperCAmelCase_) + sin(alpha * theta) * xa / sin(UpperCAmelCase_)







'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       import PIL.Image

       from .features import FeatureType


lowerCAmelCase_ : Optional[List[str]]						  =      None
lowerCAmelCase_ : str						  =      "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase_ : Any						  =      [
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=True
   lowerCamelCase_    					=None
   # Automatically constructed
   lowerCamelCase_    					="PIL.Image.Image"
   lowerCamelCase_    					=pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
   lowerCamelCase_    					=field(default='Image' ,				init=__lowerCamelCase ,				repr=__lowerCamelCase )


   def __call__(   self				:	List[Any])      ->					List[Any]:
         return self.pa_type


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"])      ->					dict:
         if config.PIL_AVAILABLE:
               import PIL.Image
         else:
               raise ImportError("To support encoding images, please install 'Pillow'.")

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               lowercase_     =      np.array(__lowerCAmelCase)

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               return {"path": value, "bytes": None}
         elif isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               return {"path": None, "bytes": value}
         elif isinstance(__lowerCAmelCase   ,		np.ndarray):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(__lowerCAmelCase)
         elif isinstance(__lowerCAmelCase   ,		PIL.Image.Image):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(__lowerCAmelCase)
         elif value.get("path") is not None and os.path.isfile(value["path"]):
               # we set "bytes": None to not duplicate the data if they're already available locally
               return {"bytes": None, "path": value.get("path")}
         elif value.get("bytes") is not None or value.get("path") is not None:
               # store the image bytes, and path is used to infer the image format using the file extension
               return {"bytes": value.get("bytes"), "path": value.get("path")}
         else:
               raise ValueError(
                   F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	dict   ,		__lowerCAmelCase				:	Dict=None)      ->					"PIL.Image.Image":
         if not self.decode:
               raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead.")

         if config.PIL_AVAILABLE:
               import PIL.Image
         else:
               raise ImportError("To support decoding images, please install 'Pillow'.")

         if token_per_repo_id is None:
               lowercase_     =      {}

         lowercase_ ,							lowercase_     =      value["path"], value["bytes"]
         if bytes_ is None:
               if path is None:
                     raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.')
               else:
                     if is_local_path(__lowerCAmelCase):
                           lowercase_     =      PIL.Image.open(__lowerCAmelCase)
                     else:
                           lowercase_     =      path.split("::")[-1]
                           try:
                                 lowercase_     =      string_to_dict(__lowerCAmelCase   ,		config.HUB_DATASETS_URL)["repo_id"]
                                 lowercase_     =      token_per_repo_id.get(__lowerCAmelCase)
                           except ValueError:
                                 lowercase_     =      None
                           with xopen(__lowerCAmelCase   ,		"rb"   ,		use_auth_token=__lowerCAmelCase) as f:
                                 lowercase_     =      BytesIO(f.read())
                           lowercase_     =      PIL.Image.open(bytes_)
         else:
               lowercase_     =      PIL.Image.open(BytesIO(bytes_))
         image.load()  # to avoid "Too many open files" errors
         return image


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value("binary"),
                 "path": Value("string"),
             }
         )


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	Union[pa.StringArray, pa.StructArray, pa.ListArray])      ->					pa.StructArray:
         if pa.types.is_string(storage.type):
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.binary())
               lowercase_     =      pa.StructArray.from_arrays([bytes_array, storage]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_binary(storage.type):
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays([storage, path_array]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_struct(storage.type):
               if storage.type.get_field_index("bytes") >= 0:
                     lowercase_     =      storage.field("bytes")
               else:
                     lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.binary())
               if storage.type.get_field_index("path") >= 0:
                     lowercase_     =      storage.field("path")
               else:
                     lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_list(storage.type):
               lowercase_     =      pa.array(
                   [encode_np_array(np.array(__lowerCAmelCase))["bytes"] if arr is not None else None for arr in storage.to_pylist()]   ,		type=pa.binary()   ,		)
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays(
                   [bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	pa.StructArray)      ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(__lowerCAmelCase				:	int):
               with xopen(__lowerCAmelCase   ,		"rb") as f:
                     lowercase_     =      f.read()
               return bytes_

         lowercase_     =      pa.array(
             [
                 (path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]   ,		type=pa.binary()   ,		)
         lowercase_     =      pa.array(
             [os.path.basename(__lowerCAmelCase) if path is not None else None for path in storage.field("path").to_pylist()]   ,		type=pa.string()   ,		)
         lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)




def __a (	)	->							List[str]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            lowercase_     =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
      return _IMAGE_COMPRESSION_FORMATS


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							bytes:
      '''simple docstring'''


      lowercase_     =      BytesIO()
      if image.format in list_image_compression_formats():
            lowercase_     =      image.format
      else:
            lowercase_     =      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
      image.save(__lowerCamelCase ,		format=__lowerCamelCase				)
      return buffer.getvalue()


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							dict:
      '''simple docstring'''


      if hasattr(__lowerCamelCase ,		"filename"				) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(__lowerCamelCase				)}


def __a (	__lowerCamelCase			:						np.ndarray				)	->							dict:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      lowercase_     =      array.dtype
      lowercase_     =      dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
      lowercase_     =      dtype.kind
      lowercase_     =      dtype.itemsize

      lowercase_     =      None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            lowercase_     =      np.dtype("|u1"				)
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'				)
            if dtype is not dest_dtype:
                  warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''				)
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            lowercase_     =      dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  lowercase_     =      dtype_byteorder + dtype_kind + str(__lowerCamelCase				)
                  lowercase_     =      np.dtype(__lowerCamelCase				)
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''				)
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'				)
      lowercase_     =      PIL.Image.fromarray(array.astype(__lowerCamelCase				)				)
      return {"path": None, "bytes": image_to_bytes(__lowerCamelCase				)}






def __a (	__lowerCamelCase			:						Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				)	->							List[dict]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      if objs:
            lowercase_ ,							lowercase_     =      first_non_null_value(__lowerCamelCase				)
            if isinstance(__lowerCamelCase ,		__lowerCamelCase				):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(__lowerCamelCase ,		np.ndarray				):
                  lowercase_     =      no_op_if_value_is_null(__lowerCamelCase				)
                  return [obj_to_image_dict_func(__lowerCamelCase				) for obj in objs]
            elif isinstance(__lowerCamelCase ,		PIL.Image.Image				):
                  lowercase_     =      no_op_if_value_is_null(__lowerCamelCase				)
                  return [obj_to_image_dict_func(__lowerCamelCase				) for obj in objs]
            else:
                  return objs
      else:
            return objs






