
"""simple docstring"""




import unittest

import numpy as np
from datasets import load_dataset

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

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 BeitImageProcessor



class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=18   ,       SCREAMING_SNAKE_CASE__=30   ,       SCREAMING_SNAKE_CASE__=4_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]   ,       SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]   ,       SCREAMING_SNAKE_CASE__=False   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   size if size is not None else {"""height""": 20, """width""": 20}
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											SCREAMING_SNAKE_CASE__						:       int           =   parent
											SCREAMING_SNAKE_CASE__						:       Any           =   batch_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_channels
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   image_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   min_resolution
											SCREAMING_SNAKE_CASE__						:       int           =   max_resolution
											SCREAMING_SNAKE_CASE__						:       Dict           =   do_resize
											SCREAMING_SNAKE_CASE__						:       Any           =   size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   do_center_crop
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   crop_size
											SCREAMING_SNAKE_CASE__						:       Any           =   do_normalize
											SCREAMING_SNAKE_CASE__						:       str           =   image_mean
											SCREAMING_SNAKE_CASE__						:       str           =   image_std
											SCREAMING_SNAKE_CASE__						:       Any           =   do_reduce_labels



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




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






def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Any           =   load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,split="""test"""						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   Image.open(dataset[0]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       int           =   Image.open(dataset[1]["""file"""]						)

							return image, map

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       str           =   load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,split="""test"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   Image.open(ds[0]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   Image.open(ds[1]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.open(ds[2]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   Image.open(ds[3]["""file"""]						)

							return [imagea, imagea], [mapa, mapa]



@require_torch
@require_vision
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Union[str, Any]					       =   BeitImageProcessor if is_vision_available() else None



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   BeitImageProcessingTester(self    )



				@property
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_resize"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """size"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_center_crop"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """center_crop"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_normalize"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """image_mean"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """image_std"""    )    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size   ,       {"""height""": 20, """width""": 20}    )
											self.assertEqual(image_processor.crop_size   ,       {"""height""": 18, """width""": 18}    )
											self.assertEqual(image_processor.do_reduce_labels   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.image_processing_class.from_dict(
											    self.image_processor_dict   ,       size=42   ,       crop_size=84   ,       reduce_labels=SCREAMING_SNAKE_CASE__    )
											self.assertEqual(image_processor.size   ,       {"""height""": 42, """width""": 42}    )
											self.assertEqual(image_processor.crop_size   ,       {"""height""": 84, """width""": 84}    )
											self.assertEqual(image_processor.do_reduce_labels   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=SCREAMING_SNAKE_CASE__    )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       Image.Image    )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       int           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Optional[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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Any           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__						:       List[str]           =   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=SCREAMING_SNAKE_CASE__   ,       torchify=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       torch.Tensor    )
																		maps.append(torch.zeros(image.shape[-2:]    ).long()    )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(image_inputs[0]   ,       maps[0]   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        1,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

											# Test not batched input (PIL images)
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   prepare_semantic_single_inputs()

											SCREAMING_SNAKE_CASE__						:       Dict           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        1,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

											# Test batched input (PIL images)
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   prepare_semantic_batch_inputs()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        2,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        2,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.image_processing_class(**self.image_processor_dict    )

											# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   prepare_semantic_single_inputs()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 1_50    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   True
											SCREAMING_SNAKE_CASE__						:       int           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )








'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

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


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




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


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






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_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:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, 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}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (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.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, 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.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

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

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
