




'''simple docstring'''

import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor







class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def __init__(					self      : Optional[Any]	,    A__      : Union[str, Any]	,    A__      : str=7	,    A__      : List[str]=3	,    A__      : Tuple=18	,    A__      : Union[str, Any]=30	,    A__      : Optional[Any]=400	,    A__      : List[str]=True	,    A__      : Dict=None	,    A__      : List[str]=True	,    A__      : Dict=[0.5, 0.5, 0.5]	,    A__      : int=[0.5, 0.5, 0.5]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				size if size is not None else {"""height""": 18, """width""": 18}
            __lowerCamelCase						:       Tuple				   =				parent
            __lowerCamelCase						:       int				   =				batch_size
            __lowerCamelCase						:       List[str]				   =				num_channels
            __lowerCamelCase						:       int				   =				image_size
            __lowerCamelCase						:       Dict				   =				min_resolution
            __lowerCamelCase						:       int				   =				max_resolution
            __lowerCamelCase						:       Optional[Any]				   =				do_resize
            __lowerCamelCase						:       Dict				   =				size
            __lowerCamelCase						:       str				   =				do_normalize
            __lowerCamelCase						:       int				   =				image_mean
            __lowerCamelCase						:       int				   =				image_std




       def 					a_					(					self      : Any					):


            """simple docstring"""







            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
            }







@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     List[Any]   		=			DPTImageProcessor if is_vision_available() else None




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				DPTImageProcessingTester(self					)




       @property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(A__	,    """image_mean"""					)					)
            self.assertTrue(hasattr(A__	,    """image_std"""					)					)
            self.assertTrue(hasattr(A__	,    """do_normalize"""					)					)
            self.assertTrue(hasattr(A__	,    """do_resize"""					)					)
            self.assertTrue(hasattr(A__	,    """size"""					)					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size	,    {"""height""": 18, """width""": 18}					)

            __lowerCamelCase						:       str				   =				self.image_processing_class.from_dict(self.image_processor_dict	,    size=42					)
            self.assertEqual(image_processor.size	,    {"""height""": 42, """width""": 42}					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            __lowerCamelCase						:       Tuple				   =				prepare_image_inputs(self.image_processor_tester	,    equal_resolution=A__					)
            for image in image_inputs:
                 self.assertIsInstance(A__	,    Image.Image					)

            # Test not batched input
            __lowerCamelCase						:       Optional[int]				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       Optional[int]				   =				image_processing(A__	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )	,    )




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            __lowerCamelCase						:       Union[str, Any]				   =				prepare_image_inputs(self.image_processor_tester	,    equal_resolution=A__	,    numpify=A__					)
            for image in image_inputs:
                 self.assertIsInstance(A__	,    np.ndarray					)

            # Test not batched input
            __lowerCamelCase						:       Union[str, Any]				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       int				   =				image_processing(A__	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )	,    )




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            __lowerCamelCase						:       Any				   =				prepare_image_inputs(self.image_processor_tester	,    equal_resolution=A__	,    torchify=A__					)
            for image in image_inputs:
                 self.assertIsInstance(A__	,    torch.Tensor					)

            # Test not batched input
            __lowerCamelCase						:       Tuple				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       str				   =				image_processing(A__	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )	,    )











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase__						:Dict          =			logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__()
            self.register_modules(unet=A__	,    scheduler=A__					)




       @torch.no_grad()
       def __call__(					self      : List[str]	,    A__      : int = 1	,    A__      : int = 100	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[float] = None	,    A__      : bool = True	,    ):


            """simple docstring"""







            if audio_length_in_s is None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[Any]				   =				2 ** len(self.unet.up_blocks					)
            if sample_size < 3 * down_scale_factor:
                 raise ValueError(
                     f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
                     f" {3 * down_scale_factor / self.unet.config.sample_rate}."					)

            __lowerCamelCase						:       Any				   =				int(A__					)
            if sample_size % down_scale_factor != 0:
                 __lowerCamelCase						:       Optional[Any]				   =				(
                     (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                 ) * down_scale_factor
                 logger.info(
                     f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
                     f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
                     """ process."""					)
            __lowerCamelCase						:       Dict				   =				int(A__					)

            __lowerCamelCase						:       Optional[int]				   =				next(iter(self.unet.parameters()					)					).dtype
            __lowerCamelCase						:       Optional[Any]				   =				(batch_size, self.unet.config.in_channels, sample_size)
            if isinstance(A__	,    A__					) and len(A__					) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(A__					)}, but requested an effective batch"
                     f" size of {batch_size}. Make sure the batch size matches the length of the generators."					)

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

            # set step values
            self.scheduler.set_timesteps(A__	,    device=audio.device					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps.to(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

                 # 2. compute previous image: x_t -> t_t-1
                 __lowerCamelCase						:       Dict				   =				self.scheduler.step(A__	,    A__	,    A__					).prev_sample

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



