from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            super().__init__()

            # make sure scheduler can always be converted to DDIM
            _A=	DDIMScheduler.from_config(scheduler.config       )

            self.register_modules(unet=lowerCAmelCase__				,      scheduler=lowerCAmelCase__       )





     @torch.no_grad()
     def __call__(					self				,      lowerCAmelCase__ = 1				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = 0.0				,      lowerCAmelCase__ = 50				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = "pil"				,      lowerCAmelCase__ = True				,      ):

            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size				,      lowerCAmelCase__       ):
                   _A=	(
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
            else:
                   _A=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ) and len(lowerCAmelCase__       ) != batch_size:
                   raise ValueError(
                       f"You have passed a list of generators of length {len(lowerCAmelCase__       )}, but requested an effective batch"
                       f" size of {batch_size}. Make sure the batch size matches the length of the generators."       )

            _A=	randn_tensor(lowerCAmelCase__				,      generator=lowerCAmelCase__				,      device=self.device				,      dtype=self.unet.dtype       )

            # set step values
            self.scheduler.set_timesteps(lowerCAmelCase__       )

            for t in self.progress_bar(self.scheduler.timesteps       ):
                   # 1. predict noise model_output
                   _A=	self.unet(lowerCAmelCase__				,      lowerCAmelCase__       ).sample

                   # 2. predict previous mean of image x_t-1 and add variance depending on eta
                   # eta corresponds to η in paper and should be between [0, 1]
                   # do x_t -> x_t-1
                   _A=	self.scheduler.step(
                       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      eta=lowerCAmelCase__				,      use_clipped_model_output=lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

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

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__       )
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 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






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





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



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

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

            del agent_type

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

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





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	AgentAudio(lowerCAmelCase__       )

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





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



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      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(lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            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(lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            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(lowerCAmelCase__       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

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