





'''simple docstring'''

import unittest

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor






class   UpperCAmelCase      (			unittest.TestCase							):
 def __init__(							self :Union[str, Any]		,			lowercase_ :List[Any]		,			lowercase_ :List[str]=7		,			lowercase_ :Optional[Any]=3		,			lowercase_ :Optional[Any]=18		,			lowercase_ :List[Any]=30		,			lowercase_ :List[str]=4_00		,			lowercase_ :int=True		,			lowercase_ :Union[str, Any]=None		,			lowercase_ :List[Any]=True		,			lowercase_ :int=None		,			lowercase_ :List[str]=True		,			lowercase_ :Tuple=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]		,			lowercase_ :Tuple=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]		,			lowercase_ :List[Any]=True		,			)->				Optional[Any]:
     A__							=     size if size is not None else {"height": 2_24, "width": 2_24}
     A__							=     crop_size if crop_size is not None else {"height": 18, "width": 18}
     A__							=     parent
     A__							=     batch_size
     A__							=     num_channels
     A__							=     image_size
     A__							=     min_resolution
     A__							=     max_resolution
     A__							=     do_resize
     A__							=     size
     A__							=     do_center_crop
     A__							=     crop_size
     A__							=     do_normalize
     A__							=     image_mean
     A__							=     image_std
     A__							=     do_convert_rgb
 def 	UpperCAmelCase_				(							self :str				)->				int:
     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_convert_rgb": self.do_convert_rgb,
     }



 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Dict=False		,			lowercase_ :Dict=False		,			lowercase_ :List[Any]=False				)->				Optional[Any]:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
         A__							=     []
         for i in range(self.batch_size				):
             image_inputs.append(
                 np.random.randint(
                     2_55		,			size=(self.num_channels, self.max_resolution, self.max_resolution)		,			dtype=np.uinta				)				)
     else:
         A__							=     []
         for i in range(self.batch_size				):
             A__,      A__							=     np.random.choice(np.arange(self.min_resolution		,			self.max_resolution				)		,			2				)
             image_inputs.append(np.random.randint(2_55		,			size=(self.num_channels, width, height)		,			dtype=np.uinta				)				)

     if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         A__							=     [Image.fromarray(np.moveaxis(lowercase_		,			0		,			-1				)				) for x in image_inputs]

     if torchify:
         A__							=     [torch.from_numpy(lowercase_				) for x in image_inputs]

     return image_inputs






@require_torch
@require_vision
class   UpperCAmelCase      (			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							ChineseCLIPImageProcessor if is_vision_available() else None
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Union[str, Any]:
     A__							=     ChineseCLIPImageProcessingTester(self		,			do_center_crop=lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCAmelCase_				(							self :Tuple				)->				Optional[Any]:
     A__							=     self.image_processing_class(**self.image_processor_dict				)
     self.assertTrue(hasattr(lowercase_		,			"do_resize"				)				)
     self.assertTrue(hasattr(lowercase_		,			"size"				)				)
     self.assertTrue(hasattr(lowercase_		,			"do_center_crop"				)				)
     self.assertTrue(hasattr(lowercase_		,			"center_crop"				)				)
     self.assertTrue(hasattr(lowercase_		,			"do_normalize"				)				)
     self.assertTrue(hasattr(lowercase_		,			"image_mean"				)				)
     self.assertTrue(hasattr(lowercase_		,			"image_std"				)				)
     self.assertTrue(hasattr(lowercase_		,			"do_convert_rgb"				)				)
 def 	UpperCAmelCase_				(							self :str				)->				Tuple:
     A__							=     self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size		,			{"height": 2_24, "width": 2_24}				)
     self.assertEqual(image_processor.crop_size		,			{"height": 18, "width": 18}				)

     A__							=     self.image_processing_class.from_dict(self.image_processor_dict		,			size=42		,			crop_size=84				)
     self.assertEqual(image_processor.size		,			{"shortest_edge": 42}				)
     self.assertEqual(image_processor.crop_size		,			{"height": 84, "width": 84}				)
 def 	UpperCAmelCase_				(							self :Dict				)->				Dict:
     pass
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				List[str]:
     # Initialize image_processing
     A__							=     self.image_processing_class(**self.image_processor_dict				)
     # create random PIL images
     A__							=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_				)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		,			Image.Image				)

     # Test not batched input
     A__							=     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
     A__							=     image_processing(lowercase_		,			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 	UpperCAmelCase_				(							self :int				)->				Tuple:
     # Initialize image_processing
     A__							=     self.image_processing_class(**self.image_processor_dict				)
     # create random numpy tensors
     A__							=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_		,			numpify=lowercase_				)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		,			np.ndarray				)

     # Test not batched input
     A__							=     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
     A__							=     image_processing(lowercase_		,			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 	UpperCAmelCase_				(							self :List[str]				)->				Optional[int]:
     # Initialize image_processing
     A__							=     self.image_processing_class(**self.image_processor_dict				)
     # create random PyTorch tensors
     A__							=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_		,			torchify=lowercase_				)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		,			torch.Tensor				)

     # Test not batched input
     A__							=     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
     A__							=     image_processing(lowercase_		,			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"],
         )		,			)






@require_torch
@require_vision
class   UpperCAmelCase      (			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							ChineseCLIPImageProcessor if is_vision_available() else None
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				List[str]:
     A__							=     ChineseCLIPImageProcessingTester(self		,			num_channels=4		,			do_center_crop=lowercase_				)
     A__							=     3
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     A__							=     self.image_processing_class(**self.image_processor_dict				)
     self.assertTrue(hasattr(lowercase_		,			"do_resize"				)				)
     self.assertTrue(hasattr(lowercase_		,			"size"				)				)
     self.assertTrue(hasattr(lowercase_		,			"do_center_crop"				)				)
     self.assertTrue(hasattr(lowercase_		,			"center_crop"				)				)
     self.assertTrue(hasattr(lowercase_		,			"do_normalize"				)				)
     self.assertTrue(hasattr(lowercase_		,			"image_mean"				)				)
     self.assertTrue(hasattr(lowercase_		,			"image_std"				)				)
     self.assertTrue(hasattr(lowercase_		,			"do_convert_rgb"				)				)
 def 	UpperCAmelCase_				(							self :List[str]				)->				Optional[Any]:
     pass



 def 	UpperCAmelCase_				(							self :str				)->				Optional[Any]:
     # Initialize image_processing
     A__							=     self.image_processing_class(**self.image_processor_dict				)
     # create random PIL images
     A__							=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_				)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		,			Image.Image				)

     # Test not batched input
     A__							=     image_processing(image_inputs[0]		,			return_tensors="pt"				).pixel_values
     self.assertEqual(
         encoded_images.shape		,			(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		,			)

     # Test batched
     A__							=     image_processing(lowercase_		,			return_tensors="pt"				).pixel_values
     self.assertEqual(
         encoded_images.shape		,			(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )		,			)










'''simple docstring'''

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   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=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(lowercase_				)
     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=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         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(lowercase_		,			0				)				).latent_dist.sample(
                 generator=lowercase_				)[0]
             A__							=     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			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(lowercase_		,			lowercase_		,			torch.tensor(self.scheduler.timesteps[start_step:]				)				)

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

         if isinstance(self.scheduler		,			lowercase_				):
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			)["prev_sample"]
         else:
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			generator=lowercase_		,			)["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(lowercase_				)["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(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

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

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     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(lowercase_				).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(lowercase_		,			lowercase_				)["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_				(							lowercase_ :torch.Tensor		,			lowercase_ :torch.Tensor		,			lowercase_ :float				)->				torch.Tensor:
     A__							=     acos(torch.dot(torch.flatten(lowercase_				)		,			torch.flatten(lowercase_				)				) / torch.norm(lowercase_				) / torch.norm(lowercase_				)				)
     return sin((1 - alpha) * theta				) * xa / sin(lowercase_				) + sin(alpha * theta				) * xa / sin(lowercase_				)



