




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 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      ["""vqvae"""]





				def __init__(   self       :  Optional[int]      ,   __lowerCamelCase       :  AutoencoderKL      ,   __lowerCamelCase       :  UNetaDConditionModel      ,   __lowerCamelCase       :  Mel      ,   __lowerCamelCase       :  Union[DDIMScheduler, DDPMScheduler]      ,   )      ->  List[Any]:
							super().__init__()
							self.register_modules(unet=__lowerCamelCase      ,   scheduler=__lowerCamelCase      ,   mel=__lowerCamelCase      ,   vqvae=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							return 50 if isinstance(self.scheduler      ,   __lowerCamelCase							) else 10_00





				@torch.no_grad()
				def __call__(   self       :  int      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  str = None      ,   __lowerCamelCase       :  np.ndarray = None      ,   __lowerCamelCase       :  int = 0      ,   __lowerCamelCase       :  int = 0      ,   __lowerCamelCase       :  int = None      ,   __lowerCamelCase       :  torch.Generator = None      ,   __lowerCamelCase       :  float = 0      ,   __lowerCamelCase       :  float = 0      ,   __lowerCamelCase       :  torch.Generator = None      ,   __lowerCamelCase       :  float = 0      ,   __lowerCamelCase       :  torch.Tensor = None      ,   __lowerCamelCase       :  torch.Tensor = None      ,   __lowerCamelCase       :  List[Any]=True      ,   )      ->  Union[
				    Union[AudioPipelineOutput, ImagePipelineOutput],
				    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
				]:
							a					=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__lowerCamelCase							)
							a					=    step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size							) == int:
										a					=    (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
										a					=    randn_tensor(
										    (
										        batch_size,
										        self.unet.config.in_channels,
										        self.unet.config.sample_size[0],
										        self.unet.config.sample_size[1],
										    )      ,   generator=__lowerCamelCase      ,   device=self.device      ,   )
							a					=    noise
							a					=    None

							if audio_file is not None or raw_audio is not None:
										self.mel.load_audio(__lowerCamelCase      ,   __lowerCamelCase							)
										a					=    self.mel.audio_slice_to_image(__lowerCamelCase							)
										a					=    np.frombuffer(input_image.tobytes()      ,   dtype="uint8"							).reshape(
										    (input_image.height, input_image.width)							)
										a					=    (input_image / 2_55) * 2 - 1
										a					=    torch.tensor(input_image[np.newaxis, :, :]      ,   dtype=torch.float							).to(self.device							)

										if self.vqvae is not None:
													a					=    self.vqvae.encode(torch.unsqueeze(__lowerCamelCase      ,   0							)							).latent_dist.sample(
													    generator=__lowerCamelCase							)[0]
													a					=    self.vqvae.config.scaling_factor * input_images

										if start_step > 0:
													a					=    self.scheduler.add_noise(__lowerCamelCase      ,   __lowerCamelCase      ,   self.scheduler.timesteps[start_step - 1]							)

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

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
										if isinstance(self.unet      ,   __lowerCamelCase							):
													a					=    self.unet(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase							)["sample"]
										else:
													a					=    self.unet(__lowerCamelCase      ,   __lowerCamelCase							)["sample"]

										if isinstance(self.scheduler      ,   __lowerCamelCase							):
													a					=    self.scheduler.step(
													    model_output=__lowerCamelCase      ,   timestep=__lowerCamelCase      ,   sample=__lowerCamelCase      ,   eta=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   )["prev_sample"]
										else:
													a					=    self.scheduler.step(
													    model_output=__lowerCamelCase      ,   timestep=__lowerCamelCase      ,   sample=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   )["prev_sample"]

										if mask is not None:
													if mask_start > 0:
																a					=    mask[:, step, :, :mask_start]
													if mask_end > 0:
																a					=    mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
										# 0.18215 was scaling factor used in training to ensure unit variance
										a					=    1 / self.vqvae.config.scaling_factor * images
										a					=    self.vqvae.decode(__lowerCamelCase							)["sample"]

							a					=    (images / 2 + 0.5).clamp(0      ,   1							)
							a					=    images.cpu().permute(0      ,   2      ,   3      ,   1							).numpy()
							a					=    (images * 2_55).round().astype("uint8"							)
							a					=    list(
							    (Image.fromarray(_[:, :, 0]							) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__lowerCamelCase      ,   mode="RGB"							).convert("L"							) for _ in images)							)

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

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





				@torch.no_grad()
				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[Image.Image]      ,   __lowerCamelCase       :  int = 50							)      ->  np.ndarray:
							assert isinstance(self.scheduler      ,   __lowerCamelCase							)
							self.scheduler.set_timesteps(__lowerCamelCase							)
							a					=    np.array(
							    [np.frombuffer(image.tobytes()      ,   dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
							a					=    (sample / 2_55) * 2 - 1
							a					=    torch.Tensor(__lowerCamelCase							).to(self.device							)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,   (0,)							)							):
										a					=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
										a					=    self.scheduler.alphas_cumprod[t]
										a					=    (
										    self.scheduler.alphas_cumprod[prev_timestep]
										    if prev_timestep >= 0
										    else self.scheduler.final_alpha_cumprod
										)
										a					=    1 - alpha_prod_t
										a					=    self.unet(__lowerCamelCase      ,   __lowerCamelCase							)["sample"]
										a					=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
										a					=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
										a					=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  torch.Tensor      ,   __lowerCamelCase       :  torch.Tensor      ,   __lowerCamelCase       :  float							)      ->  torch.Tensor:
							a					=    acos(torch.dot(torch.flatten(__lowerCamelCase							)      ,   torch.flatten(__lowerCamelCase							)							) / torch.norm(__lowerCamelCase							) / torch.norm(__lowerCamelCase							)							)
							return sin((1 - alpha) * theta							) * xa / sin(__lowerCamelCase							) + sin(alpha * theta							) * xa / sin(__lowerCamelCase							)












import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor




class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def __init__(   self       :  Dict      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Any=7      ,   __lowerCamelCase       :  List[Any]=3      ,   __lowerCamelCase       :  int=30      ,   __lowerCamelCase       :  int=4_00      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  Tuple=None      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  Optional[Any]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  Optional[Any]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  List[str]=1 / 2_55      ,   __lowerCamelCase       :  Optional[int]=True      ,   )      ->  str:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							a					=    size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
							a					=    parent
							a					=    batch_size
							a					=    num_channels
							a					=    min_resolution
							a					=    max_resolution
							a					=    do_resize
							a					=    size
							a					=    do_normalize
							a					=    image_mean
							a					=    image_std
							a					=    do_rescale
							a					=    rescale_factor
							a					=    do_pad





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  str=False							)      ->  List[str]:
							if not batched:
										a					=    image_inputs[0]
										if isinstance(__lowerCamelCase      ,   Image.Image							):
													a					,	a					=    image.size
										else:
													a					,	a					=    image.shape[1], image.shape[2]
										if w < h:
													a					=    int(self.size["shortest_edge"] * h / w							)
													a					=    self.size["shortest_edge"]
										elif w > h:
													a					=    self.size["shortest_edge"]
													a					=    int(self.size["shortest_edge"] * w / h							)
										else:
													a					=    self.size["shortest_edge"]
													a					=    self.size["shortest_edge"]

							else:
										a					=    []
										for image in image_inputs:
													a					,	a					=    self.get_expected_values([image]							)
													expected_values.append((expected_height, expected_width)							)
										a					=    max(__lowerCamelCase      ,   key=lambda __lowerCamelCase							: item[0]							)[0]
										a					=    max(__lowerCamelCase      ,   key=lambda __lowerCamelCase							: item[1]							)[1]

							return expected_height, expected_width




@require_torch
@require_vision
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Tuple											=      DetaImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    DetaImageProcessingTester(self							)





				@property
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							a					=    self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_mean"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_std"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_normalize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_resize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_rescale"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_pad"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "size"							)							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Union[str, Any]:
							a					=    self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size      ,   {"shortest_edge": 18, "longest_edge": 13_33}							)
							self.assertEqual(image_processor.do_pad      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  int:
							pass





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Any:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   Image.Image							)

							# Test not batched input
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

							self.assertEqual(
							    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

							# Test batched
							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,   )





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Optional[Any]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase      ,   numpify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   np.ndarray							)

							# Test not batched input
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

							self.assertEqual(
							    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,   )





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  List[str]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase      ,   torchify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   torch.Tensor							)

							# Test not batched input
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

							self.assertEqual(
							    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,   )





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  List[Any]:
							# prepare image and target
							a					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,   "r"							) as f:
										a					=    json.loads(f.read()							)

							a					=    {"image_id": 3_97_69, "annotations": target}

							# encode them
							a					=    DetaImageProcessor()
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   return_tensors="pt"							)

							# verify pixel values
							a					=    torch.Size([1, 3, 8_00, 10_66]							)
							self.assertEqual(encoding["pixel_values"].shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Union[str, Any]:
							# prepare image, target and masks_path
							a					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,   "r"							) as f:
										a					=    json.loads(f.read()							)

							a					=    {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

							a					=    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"							)

							# encode them
							a					=    DetaImageProcessor(format="coco_panoptic"							)
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   masks_path=__lowerCamelCase      ,   return_tensors="pt"							)

							# verify pixel values
							a					=    torch.Size([1, 3, 8_00, 10_66]							)
							self.assertEqual(encoding["pixel_values"].shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify masks
							a					=    82_28_73
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,   __lowerCamelCase							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)






