





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  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       ['vqvae']





					def __init__(						self    :  Dict ,					__lowercase    :  AutoencoderKL ,					__lowercase    :  UNetaDConditionModel ,					__lowercase    :  Mel ,					__lowercase    :  Union[DDIMScheduler, DDPMScheduler] ,					):






						'''simple docstring'''







						super().__init__()
						self.register_modules(unet=__lowercase ,					scheduler=__lowercase ,					mel=__lowercase ,					vqvae=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return 50 if isinstance(self.scheduler ,					__lowercase		) else 10_00





					@torch.no_grad()
					def __call__(						self    :  str ,					__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    :  Any=True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     steps or self.get_default_steps()
						self.scheduler.set_timesteps(__lowercase		)
						UpperCAmelCase_								=     step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size		) == int:
							UpperCAmelCase_								=     (self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
							UpperCAmelCase_								=     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 ,					)
						UpperCAmelCase_								=     noise
						UpperCAmelCase_								=     None

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

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

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

							UpperCAmelCase_								=     (
							    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
							)
							UpperCAmelCase_								=     int(mask_start_secs * pixels_per_second		)
							UpperCAmelCase_								=     int(mask_end_secs * pixels_per_second		)
							UpperCAmelCase_								=     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		):
								UpperCAmelCase_								=     self.unet(__lowercase ,					__lowercase ,					__lowercase		)["""sample"""]
							else:
								UpperCAmelCase_								=     self.unet(__lowercase ,					__lowercase		)["""sample"""]

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

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

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

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

						UpperCAmelCase_								=     [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        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  List[Image.Image] ,					__lowercase    :  int = 50		):






						'''simple docstring'''







						assert isinstance(self.scheduler ,					__lowercase		)
						self.scheduler.set_timesteps(__lowercase		)
						UpperCAmelCase_								=     np.array(
						    [np.frombuffer(image.tobytes() ,					dtype="""uint8"""		).reshape((1, image.height, image.width)		) for image in images]		)
						UpperCAmelCase_								=     (sample / 2_55) * 2 - 1
						UpperCAmelCase_								=     torch.Tensor(__lowercase		).to(self.device		)

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

						return sample





					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  torch.Tensor ,					__lowercase    :  torch.Tensor ,					__lowercase    :  float		):






						'''simple docstring'''







						UpperCAmelCase_								=     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		)









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def    A_(			A				,      A   ):
	assert isinstance(A				,      A   )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader(A				,      features=A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      split=A   ).read()
	_check_text_dataset(A				,      A   )
	assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""				,      [str, list]   )
def    A_(			A				,      A				,      A   ):
	if issubclass(A				,      A   ):
		UpperCAmelCase_								=     text_path
	elif issubclass(A				,      A   ):
		UpperCAmelCase_								=     [text_path]
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




def    A_(			A				,      A				,      A=("train",)   ):
	assert isinstance(A				,      A   )
	for split in splits:
		UpperCAmelCase_								=     dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      features=A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	if split:
		UpperCAmelCase_								=     {split: text_path}
	else:
		UpperCAmelCase_								=     """train"""
		UpperCAmelCase_								=     {"""train""": text_path, """test""": text_path}
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A				,      splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )


