



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       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""vqvae"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							AutoencoderKL     ,  UpperCamelCase :							UNetaDConditionModel     ,  UpperCamelCase :							Mel     ,  UpperCamelCase :							Union[DDIMScheduler, DDPMScheduler]     ,  )->Tuple:
      super().__init__()
      self.register_modules(unet=UpperCamelCase     ,  scheduler=UpperCamelCase     ,  mel=UpperCamelCase     ,  vqvae=UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]				)->int:
      return 5_0 if isinstance(self.scheduler     ,  UpperCamelCase				) else 1_0_0_0





     @torch.no_grad()
     def __call__(       self :							Dict     ,  UpperCamelCase :							int = 1     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							np.ndarray = None     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							Any=True     ,  )->Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	steps or self.get_default_steps()
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size				) == int:
       __SCREAMING_SNAKE_CASE       :    Dict       =	(self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
       __SCREAMING_SNAKE_CASE       :    Any       =	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     ,  )
      __SCREAMING_SNAKE_CASE       :    Any       =	noise
      __SCREAMING_SNAKE_CASE       :    Any       =	None

      if audio_file is not None or raw_audio is not None:
       self.mel.load_audio(UpperCamelCase     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    str       =	self.mel.audio_slice_to_image(UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	np.frombuffer(input_image.tobytes()     ,  dtype="uint8"				).reshape(
           (input_image.height, input_image.width)				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(input_image / 2_5_5) * 2 - 1
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.tensor(input_image[np.newaxis, :, :]     ,  dtype=torch.float				).to(self.device				)

       if self.vqvae is not None:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.vqvae.encode(torch.unsqueeze(UpperCamelCase     ,  0				)				).latent_dist.sample(
            generator=UpperCamelCase				)[0]
        __SCREAMING_SNAKE_CASE       :    int       =	self.vqvae.config.scaling_factor * input_images

       if start_step > 0:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  self.scheduler.timesteps[start_step - 1]				)

       __SCREAMING_SNAKE_CASE       :    int       =	(
           self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
       )
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(mask_start_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    int       =	int(mask_end_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    Any       =	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				):
        __SCREAMING_SNAKE_CASE       :    str       =	self.unet(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)["sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    int       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]

       if isinstance(self.scheduler     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  eta=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]

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

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

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(images / 2 + 0.5).clamp(0     ,  1				)
      __SCREAMING_SNAKE_CASE       :    str       =	images.cpu().permute(0     ,  2     ,  3     ,  1				).numpy()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	(images * 2_5_5).round().astype("uint8"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	list(
          (Image.fromarray(_[:, :, 0]				) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(UpperCamelCase     ,  mode="RGB"				).convert("L"				) for _ in images)				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	[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 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Image.Image]     ,  UpperCamelCase :							int = 5_0				)->np.ndarray:
      assert isinstance(self.scheduler     ,  UpperCamelCase				)
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	np.array(
          [np.frombuffer(image.tobytes()     ,  dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	(sample / 2_5_5) * 2 - 1
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.Tensor(UpperCamelCase				).to(self.device				)

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

      return sample






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							float				)->torch.Tensor:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	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				)











import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase						    =  logging.getLogger(__name__)

@dataclass(frozen=UpperCamelCase )
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			42
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None






@dataclass(frozen=UpperCamelCase )
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None
     lowerCAmelCase					=			None


if is_torch_available():
      import torch
      from torch.utils.data import Dataset

      class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
           lowerCAmelCase					=			42





           def __init__(       self :							str     ,  UpperCamelCase :							str     ,  UpperCamelCase :							PreTrainedTokenizer     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Union[str, Any]=False     ,  UpperCamelCase :							bool = False     ,  )->List[str]:
            __SCREAMING_SNAKE_CASE       :    int       =	hans_processors[task]()

            __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	os.path.join(
                UpperCamelCase     ,  "cached_{}_{}_{}_{}".format(
                    "dev" if evaluate else "train"     ,  tokenizer.__class__.__name__     ,  str(UpperCamelCase				)     ,  UpperCamelCase     ,  )     ,  )
            __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	label_list[2], label_list[1]
            __SCREAMING_SNAKE_CASE       :    Any       =	label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            __SCREAMING_SNAKE_CASE       :    str       =	cached_features_file + ".lock"
            with FileLock(UpperCamelCase				):
             if os.path.exists(UpperCamelCase				) and not overwrite_cache:
              logger.info(F"""Loading features from cached file {cached_features_file}"""				)
              __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.load(UpperCamelCase				)
             else:
              logger.info(F"""Creating features from dataset file at {data_dir}"""				)

              __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(
                  processor.get_dev_examples(UpperCamelCase				) if evaluate else processor.get_train_examples(UpperCamelCase				)
              )

              logger.info("Training examples: %s"     ,  len(UpperCamelCase				)				)
              __SCREAMING_SNAKE_CASE       :    Any       =	hans_convert_examples_to_features(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)
              logger.info("Saving features into cached file %s"     ,  UpperCamelCase				)
              torch.save(self.features     ,  UpperCamelCase				)





           def __len__(       self :							Tuple				)->Any:
            return len(self.features				)





           def __getitem__(       self :							Union[str, Any]     ,  UpperCamelCase :							int				)->InputFeatures:
            return self.features[i]






           def 	__snake_case    (       self :							int				)->Tuple:
            return self.label_list


if is_tf_available():
      import tensorflow as tf

      class       _SCREAMING_SNAKE_CASE	:
           lowerCAmelCase					=			42





           def __init__(       self :							Dict     ,  UpperCamelCase :							str     ,  UpperCamelCase :							PreTrainedTokenizer     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Optional[int] = 1_2_8     ,  UpperCamelCase :							Union[str, Any]=False     ,  UpperCamelCase :							bool = False     ,  )->str:
            __SCREAMING_SNAKE_CASE       :    str       =	hans_processors[task]()
            __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	label_list[2], label_list[1]
            __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	label_list

            __SCREAMING_SNAKE_CASE       :    str       =	processor.get_dev_examples(UpperCamelCase				) if evaluate else processor.get_train_examples(UpperCamelCase				)
            __SCREAMING_SNAKE_CASE       :    str       =	hans_convert_examples_to_features(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

            def gen():
             for ex_index, ex in tqdm.tqdm(enumerate(self.features				)     ,  desc="convert examples to features"				):
              if ex_index % 1_0_0_0_0 == 0:
               logger.info("Writing example %d of %d" % (ex_index, len(UpperCamelCase				))				)

              yield (
                  {
                      "example_id": 0,
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label,
              )

            __SCREAMING_SNAKE_CASE       :    List[str]       =	tf.data.Dataset.from_generator(
                UpperCamelCase     ,  (
                    {
                        "example_id": tf.intaa,
                        "input_ids": tf.intaa,
                        "attention_mask": tf.intaa,
                        "token_type_ids": tf.intaa,
                    },
                    tf.intaa,
                )     ,  (
                    {
                        "example_id": tf.TensorShape([]				),
                        "input_ids": tf.TensorShape([None, None]				),
                        "attention_mask": tf.TensorShape([None, None]				),
                        "token_type_ids": tf.TensorShape([None, None]				),
                    },
                    tf.TensorShape([]				),
                )     ,  )





           def 	__snake_case    (       self :							List[Any]				)->str:
            return self.dataset





           def __len__(       self :							Tuple				)->List[str]:
            return len(self.features				)





           def __getitem__(       self :							Optional[Any]     ,  UpperCamelCase :							Tuple				)->InputFeatures:
            return self.features[i]






           def 	__snake_case    (       self :							List[Any]				)->Optional[int]:
            return self.label_list

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Union[str, Any]				)->Tuple:
      return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase     ,  "heuristics_train_set.txt"				)				)     ,  "train"				)





     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Optional[int]				)->Any:
      return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase     ,  "heuristics_evaluation_set.txt"				)				)     ,  "dev"				)





     def 	__snake_case    (       self :							Optional[int]				)->Tuple:
      return ["contradiction", "entailment", "neutral"]






     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Any				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
      for i, line in enumerate(UpperCamelCase				):
       if i == 0:
        continue
       __SCREAMING_SNAKE_CASE       :    str       =	"%s-%s" % (set_type, line[0])
       __SCREAMING_SNAKE_CASE       :    List[str]       =	line[5]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	line[6]
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	line[7][2:] if line[7].startswith("ex"				) else line[7]
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	line[0]
       examples.append(InputExample(guid=UpperCamelCase     ,  text_a=UpperCamelCase     ,  text_b=UpperCamelCase     ,  label=UpperCamelCase     ,  pairID=UpperCamelCase				)				)
      return examples






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[InputExample]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     PreTrainedTokenizer		,	):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	{label: i for i, label in enumerate(__lowerCamelCase     )}

 __SCREAMING_SNAKE_CASE       :    Any       =	[]
 for ex_index, example in tqdm.tqdm(enumerate(__lowerCamelCase     )		,	desc="convert examples to features"     ):
  if ex_index % 10000 == 0:
   logger.info("Writing example %d" % (ex_index)     )

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenizer(
      example.text_a		,	example.text_b		,	add_special_tokens=__lowerCamelCase		,	max_length=__lowerCamelCase		,	padding="max_length"		,	truncation=__lowerCamelCase		,	return_overflowing_tokens=__lowerCamelCase		,	)

  __SCREAMING_SNAKE_CASE       :    List[str]       =	label_map[example.label] if example.label in label_map else 0

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(example.pairID     )

  features.append(InputFeatures(**__lowerCamelCase		,	label=__lowerCamelCase		,	pairID=__lowerCamelCase     )     )

 for i, example in enumerate(examples[:5]     ):
  logger.info("*** Example ***"     )
  logger.info(F"""guid: {example}"""     )
  logger.info(F"""features: {features[i]}"""     )

 return features


_lowerCamelCase						    =  {
    """hans""": 3,
}






_lowerCamelCase						    =  {
    """hans""": HansProcessor,
}






