

"""simple docstring"""






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image


_lowerCAmelCase						=		["""text""", """image""", """audio"""]

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for input_type in input_types:
        if input_type == "text":
            inputs.append('Text input'   )
        elif input_type == "image":
            inputs.append(
                Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'   )   ) / '000000039769.png'   ).resize((512, 512)   )   )
        elif input_type == "audio":
            inputs.append(torch.ones(3000   )   )
        elif isinstance(_lowerCamelCase ,   _lowerCamelCase   ):
            inputs.append(create_inputs(_lowerCamelCase   )   )
        else:
            raise ValueError(f"""Invalid type requested: {input_type}"""   )

    return inputs

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    []

    for output in outputs:
        if isinstance(_lowerCamelCase ,   (str, AgentText)   ):
            output_types.append('text'   )
        elif isinstance(_lowerCamelCase ,   (Image.Image, AgentImage)   ):
            output_types.append('image'   )
        elif isinstance(_lowerCamelCase ,   (torch.Tensor, AgentAudio)   ):
            output_types.append('audio'   )
        else:
            raise ValueError(f"""Invalid output: {output}"""   )

    return output_types







@is_tool_test
class 	__UpperCamelCase    :

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.assertTrue(hasattr(self.tool    ,'inputs'      )      )
      self.assertTrue(hasattr(self.tool    ,'outputs'      )      )

      _lowerCAmelCase       :   str  =    self.tool.inputs
      for _input in inputs:
          if isinstance(_input    ,_A      ):
              for __input in _input:
                  self.assertTrue(__input in authorized_types      )
          else:
              self.assertTrue(_input in authorized_types      )

      _lowerCAmelCase       :   Tuple  =    self.tool.outputs
      for _output in outputs:
          self.assertTrue(_output in authorized_types      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    create_inputs(self.tool.inputs      )
      _lowerCAmelCase       :   str  =    self.tool(*_A      )

      # There is a single output
      if len(self.tool.outputs      ) == 1:
          _lowerCAmelCase       :   Union[str, Any]  =    [outputs]

      self.assertListEqual(output_types(_A      )    ,self.tool.outputs      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.assertTrue(hasattr(self.tool    ,'description'      )      )
      self.assertTrue(hasattr(self.tool    ,'default_checkpoint'      )      )
      self.assertTrue(self.tool.description.startswith('This is a tool that'      )      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    create_inputs(self.tool.inputs      )
      _lowerCAmelCase       :   List[Any]  =    self.tool(*_A      )

      if not isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   List[Any]  =    [outputs]

      self.assertEqual(len(_A      )    ,len(self.tool.outputs      )      )

      for output, output_type in zip(_A    ,self.tool.outputs      ):
          _lowerCAmelCase       :   Dict  =    AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(_A    ,_A      )      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    create_inputs(self.tool.inputs      )

      _lowerCAmelCase       :   int  =    []

      for _input, input_type in zip(_A    ,self.tool.inputs      ):
          if isinstance(_A    ,_A      ):
              _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
          else:
              _inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )

        # Should not raise an error
      _lowerCAmelCase       :   Optional[int]  =    self.tool(*_A      )

      if not isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Dict  =    [outputs]

      self.assertEqual(len(_A      )    ,len(self.tool.outputs      )      )








"""simple docstring"""






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 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		["vqvae"]

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,):
      '''simple docstring'''

      super().__init__()
      self.register_modules(unet=_A    ,scheduler=_A    ,mel=_A    ,vqvae=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return 50 if isinstance(self.scheduler    ,_A      ) else 1000

  @torch.no_grad()
  def __call__(		self    ,_A = 1    ,_A = None    ,_A = None    ,_A = 0    ,_A = 0    ,_A = None    ,_A = None    ,_A = 0    ,_A = 0    ,_A = None    ,_A = 0    ,_A = None    ,_A = None    ,_A=True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    steps or self.get_default_steps()
      self.scheduler.set_timesteps(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size      ) == int:
          _lowerCAmelCase       :   Tuple  =    (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
          _lowerCAmelCase       :   Optional[Any]  =    randn_tensor(
              (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size[0],
                  self.unet.config.sample_size[1],
              )    ,generator=_A    ,device=self.device    ,)
      _lowerCAmelCase       :   Dict  =    noise
      _lowerCAmelCase       :   Optional[Any]  =    None

      if audio_file is not None or raw_audio is not None:
          self.mel.load_audio(_A    ,_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    self.mel.audio_slice_to_image(_A      )
          _lowerCAmelCase       :   int  =    np.frombuffer(input_image.tobytes()    ,dtype='uint8'      ).reshape(
              (input_image.height, input_image.width)      )
          _lowerCAmelCase       :   int  =    (input_image / 255) * 2 - 1
          _lowerCAmelCase       :   str  =    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(_A    ,0      )      ).latent_dist.sample(
                  generator=_A      )[0]
              _lowerCAmelCase       :   Tuple  =    self.vqvae.config.scaling_factor * input_images

          if start_step > 0:
              _lowerCAmelCase       :   List[Any]  =    self.scheduler.add_noise(_A    ,_A    ,self.scheduler.timesteps[start_step - 1]      )

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

      for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]      )      ):
          if isinstance(self.unet    ,_A      ):
              _lowerCAmelCase       :   str  =    self.unet(_A    ,_A    ,_A      )['sample']
          else:
              _lowerCAmelCase       :   Any  =    self.unet(_A    ,_A      )['sample']

          if isinstance(self.scheduler    ,_A      ):
              _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler.step(
                  model_output=_A    ,timestep=_A    ,sample=_A    ,eta=_A    ,generator=_A    ,)['prev_sample']
          else:
              _lowerCAmelCase       :   Any  =    self.scheduler.step(
                  model_output=_A    ,timestep=_A    ,sample=_A    ,generator=_A    ,)['prev_sample']

          if mask is not None:
              if mask_start > 0:
                  _lowerCAmelCase       :   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       :   Union[str, Any]  =    1 / self.vqvae.config.scaling_factor * images
          _lowerCAmelCase       :   Any  =    self.vqvae.decode(_A      )['sample']

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

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

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

  @torch.no_grad()
  def __lowerCamelCase		(		self    ,_A    ,_A = 50      ):
      '''simple docstring'''

      assert isinstance(self.scheduler    ,_A      )
      self.scheduler.set_timesteps(_A      )
      _lowerCAmelCase       :   Dict  =    np.array(
          [np.frombuffer(image.tobytes()    ,dtype='uint8'      ).reshape((1, image.height, image.width)      ) for image in images]      )
      _lowerCAmelCase       :   Dict  =    (sample / 255) * 2 - 1
      _lowerCAmelCase       :   List[str]  =    torch.Tensor(_A      ).to(self.device      )

      for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,(0,)      )      ):
          _lowerCAmelCase       :   Tuple  =    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
          _lowerCAmelCase       :   Optional[int]  =    self.scheduler.alphas_cumprod[t]
          _lowerCAmelCase       :   Dict  =    (
              self.scheduler.alphas_cumprod[prev_timestep]
              if prev_timestep >= 0
              else self.scheduler.final_alpha_cumprod
          )
          _lowerCAmelCase       :   Union[str, Any]  =    1 - alpha_prod_t
          _lowerCAmelCase       :   Union[str, Any]  =    self.unet(_A    ,_A      )['sample']
          _lowerCAmelCase       :   Optional[int]  =    (1 - alpha_prod_t_prev) ** 0.5 * model_output
          _lowerCAmelCase       :   Any  =    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
          _lowerCAmelCase       :   Dict  =    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample







  @staticmethod
  def __lowerCamelCase		(		_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    acos(torch.dot(torch.flatten(_A      )    ,torch.flatten(_A      )      ) / torch.norm(_A      ) / torch.norm(_A      )      )
      return sin((1 - alpha) * theta      ) * xa / sin(_A      ) + sin(alpha * theta      ) * xa / sin(_A      )





