
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    Adafactor,
					    AdamW,
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_inverse_sqrt_schedule,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					)





def 				_lowerCamelCase      (       lowerCamelCase_:	Any					,						lowerCamelCase_:	Optional[Any]=10		):



				'''simple docstring'''







				A  :	Tuple				=					[]
				for _ in range(_lowerCamelCase		):
								lrs.append(scheduler.get_lr()[0]		)
								scheduler.step()
				return lrs





def 				_lowerCamelCase      (       lowerCamelCase_:	Tuple					,						lowerCamelCase_:	List[str]=10		):



				'''simple docstring'''







				A  :	Union[str, Any]				=					[]
				for step in range(_lowerCamelCase		):
								lrs.append(scheduler.get_lr()[0]		)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																A  :	Optional[Any]				=					os.path.join(_lowerCamelCase					,						'''schedule.bin'''		)
																torch.save(scheduler.state_dict()					,						_lowerCamelCase		)

																A  :	Optional[Any]				=					torch.load(_lowerCamelCase		)
																scheduler.load_state_dict(_lowerCamelCase		)
				return lrs

@require_torch
class      _SCREAMING_SNAKE_CASE				(		unittest.TestCase				):





				def 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		Any       ,   snake_case_			:		List[Any]       ,   snake_case_			:		Optional[Any]				):


								"""simple docstring"""

								self.assertEqual(len(lowercase_				)       ,   len(lowercase_				)				)
								for a, b in zip(lowercase_       ,   lowercase_				):
												self.assertAlmostEqual(lowercase_       ,   lowercase_       ,   delta=lowercase_				)





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	int				=					torch.tensor([0.1, -0.2, -0.1]       ,   requires_grad=lowercase_				)
								A  :	Dict				=					torch.tensor([0.4, 0.2, -0.5]				)
								A  :	int				=					nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								A  :	Union[str, Any]				=					AdamW(params=[w]       ,   lr=2E-1       ,   weight_decay=0.0				)
								for _ in range(100				):
												A  :	Optional[Any]				=					criterion(lowercase_       ,   lowercase_				)
												loss.backward()
												optimizer.step()
												w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
												w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()       ,   [0.4, 0.2, -0.5]       ,   tol=1E-2				)






				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								A  :	Optional[int]				=					torch.tensor([0.1, -0.2, -0.1]       ,   requires_grad=lowercase_				)
								A  :	List[Any]				=					torch.tensor([0.4, 0.2, -0.5]				)
								A  :	str				=					nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								A  :	Dict				=					Adafactor(
								    params=[w]       ,   lr=1E-2       ,   eps=(1E-30, 1E-3)       ,   clip_threshold=1.0       ,   decay_rate=-0.8       ,   betaa=lowercase_       ,   weight_decay=0.0       ,   relative_step=lowercase_       ,   scale_parameter=lowercase_       ,   warmup_init=lowercase_       ,   )
								for _ in range(1000				):
												A  :	Optional[int]				=					criterion(lowercase_       ,   lowercase_				)
												loss.backward()
												optimizer.step()
												w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
												w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()       ,   [0.4, 0.2, -0.5]       ,   tol=1E-2				)



@require_torch
class      _SCREAMING_SNAKE_CASE				(		unittest.TestCase				):
				lowerCamelCase_	  =  nn.Linear(5_0,     5_0				) if is_torch_available() else None
				lowerCamelCase_	  =  AdamW(m.parameters(),     lr=1_0.0				) if is_torch_available() else None
				lowerCamelCase_	  =  1_0





				def 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		Any       ,   snake_case_			:		List[Any]       ,   snake_case_			:		List[Any]       ,   snake_case_			:		str=None				):


								"""simple docstring"""

								self.assertEqual(len(lowercase_				)       ,   len(lowercase_				)				)
								for a, b in zip(lowercase_       ,   lowercase_				):
												self.assertAlmostEqual(lowercase_       ,   lowercase_       ,   delta=lowercase_       ,   msg=lowercase_				)






				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								A  :	Any				=					{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								A  :	Optional[Any]				=					{
								    get_constant_schedule: ({}, [10.0] * self.num_steps),
								    get_constant_schedule_with_warmup: (
								        {'''num_warmup_steps''': 4},
								        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
								    ),
								    get_linear_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
								    ),
								    get_cosine_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
								    ),
								    get_cosine_with_hard_restarts_schedule_with_warmup: (
								        {**common_kwargs, '''num_cycles''': 2},
								        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
								    ),
								    get_polynomial_decay_schedule_with_warmup: (
								        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
								        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
								    ),
								    get_inverse_sqrt_schedule: (
								        {'''num_warmup_steps''': 2},
								        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
								    ),
								}

								for scheduler_func, data in scheds.items():
												A			,							A  :	Optional[int]				=					data

												A  :	str				=					scheduler_func(self.optimizer       ,   **lowercase_				)
												self.assertEqual(len([scheduler.get_lr()[0]]				)       ,   1				)
												A  :	int				=					unwrap_schedule(lowercase_       ,   self.num_steps				)
												self.assertListAlmostEqual(
												    lowercase_       ,   lowercase_       ,   tol=1E-2       ,   msg=f"""failed for {scheduler_func} in normal scheduler"""       ,   )

												A  :	List[Any]				=					scheduler_func(self.optimizer       ,   **lowercase_				)
												if scheduler_func.__name__ != "get_constant_schedule":
																LambdaScheduleWrapper.wrap_scheduler(lowercase_				)  # wrap to test picklability of the schedule
												A  :	List[str]				=					unwrap_and_save_reload_schedule(lowercase_       ,   self.num_steps				)
												self.assertListEqual(lowercase_       ,   lowercase_       ,   msg=f"""failed for {scheduler_func} in save and reload"""				)



class      _SCREAMING_SNAKE_CASE				:





				def __init__(		self			:		str       ,   snake_case_			:		List[str]				):


								"""simple docstring"""

								A  :	Optional[int]				=					fn





				def __call__(		self			:		List[Any]       ,   *snake_case_			:		Dict       ,   **snake_case_			:		Dict				):


								"""simple docstring"""

								return self.fn(*lowercase_       ,   **lowercase_				)






				@classmethod
				def 							_UpperCAmelCase    (		self			:		Any       ,   snake_case_			:		Tuple				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					list(map(self       ,   scheduler.lr_lambdas				)				)






'''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_				)



