'''simple docstring'''





import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf

if is_vision_available():
    from PIL import Image





def      lowerCAmelCase_    ( snake_case_	:			Tuple=""      )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       tempfile.mkdtemp()
     return os.path.join(snake_case_ ,   str(uuid.uuida()      ) + suffix      )






@require_soundfile
@require_torch
class 	__A				(			unittest.TestCase     ):



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       torch.rand(12       ,      dtype=torch.floataa			) - 0.5
         UpperCAmelCase_	   =       AgentAudio(__a			)
         UpperCAmelCase_	   =       str(agent_type.to_string()			)

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(__a       ,      agent_type.to_raw()       ,      atol=1E-4			)			)

         del agent_type

         # Ensure the path remains even after the object deletion
         self.assertTrue(os.path.exists(__a			)			)

         # Ensure that the file contains the same value as the original tensor
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       sf.read(__a			)
         self.assertTrue(torch.allclose(__a       ,      torch.tensor(__a			)       ,      atol=1E-4			)			)






    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       torch.rand(12       ,      dtype=torch.floataa			) - 0.5
         UpperCAmelCase_	   =       get_new_path(suffix=".wav"			)
         sf.write(__a       ,      __a       ,      16000			)

         UpperCAmelCase_	   =       AgentAudio(__a			)

         self.assertTrue(torch.allclose(__a       ,      agent_type.to_raw()       ,      atol=1E-4			)			)
         self.assertEqual(agent_type.to_string()       ,      __a			)






@require_vision
@require_torch
class 	__A				(			unittest.TestCase     ):



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       torch.randint(0       ,      256       ,      (64, 64, 3)			)
         UpperCAmelCase_	   =       AgentImage(__a			)
         UpperCAmelCase_	   =       str(agent_type.to_string()			)

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(__a       ,      agent_type._tensor       ,      atol=1E-4			)			)

         self.assertIsInstance(agent_type.to_raw()       ,      Image.Image			)

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(__a			)			)



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"
         UpperCAmelCase_	   =       Image.open(__a			)
         UpperCAmelCase_	   =       AgentImage(__a			)

         self.assertTrue(path.samefile(agent_type.to_string()			)			)
         self.assertTrue(image == agent_type.to_raw()			)

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(__a			)			)






    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"
         UpperCAmelCase_	   =       Image.open(__a			)
         UpperCAmelCase_	   =       AgentImage(__a			)

         self.assertFalse(path.samefile(agent_type.to_string()			)			)
         self.assertTrue(image == agent_type.to_raw()			)

         # Ensure the path remains even after the object deletion
         del agent_type
         self.assertTrue(os.path.exists(__a			)			)






class 	__A				(			unittest.TestCase     ):



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       "Hey!"
         UpperCAmelCase_	   =       AgentText(__a			)

         self.assertEqual(__a       ,      agent_type.to_string()			)
         self.assertEqual(__a       ,      agent_type.to_raw()			)
         self.assertEqual(__a       ,      __a			)





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

         UpperCAmelCase_	   =       (image / 2 + 0.5).clamp(0       ,      1			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




