
"""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(       lowerCamelCase      ):
		lowercase__         = ['vqvae']



		def __init__(   self							,						__a							,						__a							,						__a							,						__a							,						)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a							,						mel=__a							,						vqvae=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 50 if isinstance(self.scheduler							,						__a) else 10_00



		@torch.no_grad()
		def __call__(   self							,						__a = 1							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = 0							,						__a = None							,						__a = None							,						__a=True							,						)			->			Union[
		    Union[AudioPipelineOutput, ImagePipelineOutput],
		    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
		]:






							'''simple docstring'''
							_UpperCamelCase									=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__a)
							_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=__a							,						device=self.device							,						)
							_UpperCamelCase									=    noise
							_UpperCamelCase									=    None

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

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

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
												if isinstance(self.unet							,						__a):
																	_UpperCamelCase									=    self.unet(__a							,						__a							,						__a)['''sample''']
												else:
																	_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']

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

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

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



		@torch.no_grad()
		def 		UpperCAmelCase				(   self							,						__a							,						__a = 50)			->			np.ndarray:






							'''simple docstring'''
							assert isinstance(self.scheduler							,						__a)
							self.scheduler.set_timesteps(__a)
							_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(__a).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(__a							,						__a)['''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 		UpperCAmelCase				(   __a							,						__a							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    acos(torch.dot(torch.flatten(__a)							,						torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
							return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)








"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
			import textwrap

			from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
			import torch

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			SCREAMING_SNAKE_CASE_					     =	False


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_					     =	'''ybelkada/fonts'''

def  lowercase     ():
						if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
												raise ImportError(
												    f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
												    """Pix2StructImageProcessor. Please upgrade torch."""						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						requires_backends(_lowerCAmelCase ,    ["""torch"""]						)
						_check_torch_version()

						__lowerCAmelCase     =  image_tensor.unsqueeze(0						)
						__lowerCAmelCase     =  torch.nn.functional.unfold(_lowerCAmelCase ,    (patch_height, patch_width) ,    stride=(patch_height, patch_width)						)
						__lowerCAmelCase     =  patches.reshape(image_tensor.size(0						) ,    image_tensor.size(1						) ,    _lowerCAmelCase ,    _lowerCAmelCase ,    -1						)
						__lowerCAmelCase     =  patches.permute(0 ,    4 ,    2 ,    3 ,    1						).reshape(
						    image_tensor.size(2						) // patch_height ,    image_tensor.size(3						) // patch_width ,    image_tensor.size(1						) * patch_height * patch_width ,    )
						return patches.unsqueeze(0						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = 36 ,    _lowerCAmelCase = "black" ,    _lowerCAmelCase = "white" ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = 5 ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    ):
						requires_backends(_lowerCAmelCase ,    """vision"""						)
						# Add new lines so that each line is no more than 80 characters.

						__lowerCAmelCase     =  textwrap.TextWrapper(width=80						)
						__lowerCAmelCase     =  wrapper.wrap(text=_lowerCAmelCase						)
						__lowerCAmelCase     =  """\n""".join(_lowerCAmelCase						)

						if font_bytes is not None and font_path is None:
												__lowerCAmelCase     =  io.BytesIO(_lowerCAmelCase						)
						elif font_path is not None:
												__lowerCAmelCase     =  font_path
						else:
												__lowerCAmelCase     =  hf_hub_download(_lowerCAmelCase ,    """Arial.TTF"""						)
						__lowerCAmelCase     =  ImageFont.truetype(_lowerCAmelCase ,    encoding="""UTF-8""" ,    size=_lowerCAmelCase						)

						# Use a temporary canvas to determine the width and height in pixels when
						# rendering the text.
						__lowerCAmelCase     =  ImageDraw.Draw(Image.new("""RGB""" ,    (1, 1) ,    _lowerCAmelCase						)						)
						__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  temp_draw.textbbox((0, 0) ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# Create the actual image with a bit of padding around the text.
						__lowerCAmelCase     =  text_width + left_padding + right_padding
						__lowerCAmelCase     =  text_height + top_padding + bottom_padding
						__lowerCAmelCase     =  Image.new("""RGB""" ,    (image_width, image_height) ,    _lowerCAmelCase						)
						__lowerCAmelCase     =  ImageDraw.Draw(_lowerCAmelCase						)
						draw.text(xy=(left_padding, top_padding) ,    text=_lowerCAmelCase ,    fill=_lowerCAmelCase ,    font=_lowerCAmelCase						)
						return image

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    **_lowerCAmelCase						):
						requires_backends(_lowerCAmelCase ,    """vision"""						)

						# Convert to PIL image if necessary
						__lowerCAmelCase     =  to_pil_image(_lowerCAmelCase						)

						__lowerCAmelCase     =  render_text(_lowerCAmelCase ,    **_lowerCAmelCase						)
						__lowerCAmelCase     =  max(header_image.width ,    image.width						)

						__lowerCAmelCase     =  int(image.height * (new_width / image.width)						)
						__lowerCAmelCase     =  int(header_image.height * (new_width / header_image.width)						)

						__lowerCAmelCase     =  Image.new("""RGB""" ,    (new_width, new_height + new_header_height) ,    """white"""						)
						new_image.paste(header_image.resize((new_width, new_header_height)						) ,    (0, 0)						)
						new_image.paste(image.resize((new_width, new_height)						) ,    (0, new_header_height)						)

						# Convert back to the original framework if necessary
						__lowerCAmelCase     =  to_numpy_array(_lowerCAmelCase						)

						if infer_channel_dimension_format(_lowerCAmelCase						) == ChannelDimension.LAST:
												__lowerCAmelCase     =  to_channel_dimension_format(_lowerCAmelCase ,    ChannelDimension.LAST						)

						return new_image



class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		['''flattened_patches''']




							def __init__( self    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = 2_048    ,				snake_case_ = False    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)
													__lowerCAmelCase     =  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
													__lowerCAmelCase     =  do_normalize
													__lowerCAmelCase     =  do_convert_rgb
													__lowerCAmelCase     =  max_patches
													__lowerCAmelCase     =  is_vqa




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				**snake_case_				)       ->  np.ndarray:
													requires_backends(self.extract_flattened_patches    ,				"""torch"""				)
													_check_torch_version()

													# convert to torch
													__lowerCAmelCase     =  to_channel_dimension_format(snake_case_    ,				ChannelDimension.FIRST				)
													__lowerCAmelCase     =  torch.from_numpy(snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  patch_size["""height"""], patch_size["""width"""]
													__lowerCAmelCase   ,     __lowerCAmelCase     =  get_image_size(snake_case_				)

													# maximize scale s.t.
													__lowerCAmelCase     =  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
													__lowerCAmelCase     =  max(min(math.floor(scale * image_height / patch_height				)    ,				snake_case_				)    ,				1				)
													__lowerCAmelCase     =  max(min(math.floor(scale * image_width / patch_width				)    ,				snake_case_				)    ,				1				)
													__lowerCAmelCase     =  max(num_feasible_rows * patch_height    ,				1				)
													__lowerCAmelCase     =  max(num_feasible_cols * patch_width    ,				1				)

													__lowerCAmelCase     =  torch.nn.functional.interpolate(
													    image.unsqueeze(0				)    ,				size=(resized_height, resized_width)    ,				mode="""bilinear"""    ,				align_corners=snake_case_    ,				antialias=snake_case_    ,				).squeeze(0				)

													# [1, rows, columns, patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  torch_extract_patches(snake_case_    ,				snake_case_    ,				snake_case_				)

													__lowerCAmelCase     =  patches.shape
													__lowerCAmelCase     =  patches_shape[1]
													__lowerCAmelCase     =  patches_shape[2]
													__lowerCAmelCase     =  patches_shape[3]

													# [rows * columns, patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  patches.reshape([rows * columns, depth]				)

													# [rows * columns, 1]
													__lowerCAmelCase     =  torch.arange(snake_case_				).reshape([rows, 1]				).repeat(1    ,				snake_case_				).reshape([rows * columns, 1]				)
													__lowerCAmelCase     =  torch.arange(snake_case_				).reshape([1, columns]				).repeat(snake_case_    ,				1				).reshape([rows * columns, 1]				)

													# Offset by 1 so the ids do not contain zeros, which represent padding.
													row_ids += 1
													col_ids += 1

													# Prepare additional patch features.
													# [rows * columns, 1]
													__lowerCAmelCase     =  row_ids.to(torch.floataa				)
													__lowerCAmelCase     =  col_ids.to(torch.floataa				)

													# [rows * columns, 2 + patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  torch.cat([row_ids, col_ids, patches]    ,				-1				)

													# [max_patches, 2 + patch_height * patch_width * image_channels]
													__lowerCAmelCase     =  torch.nn.functional.pad(snake_case_    ,				[0, 0, 0, max_patches - (rows * columns)]				).float()

													__lowerCAmelCase     =  to_numpy_array(snake_case_				)

													return result




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_				)       ->  np.ndarray:
													if image.dtype == np.uinta:
																			__lowerCAmelCase     =  image.astype(np.floataa				)

													# take mean across the whole `image`
													__lowerCAmelCase     =  np.mean(snake_case_				)
													__lowerCAmelCase     =  np.std(snake_case_				)
													__lowerCAmelCase     =  max(snake_case_    ,				1.0 / math.sqrt(np.prod(image.shape				)				)				)

													return normalize(snake_case_    ,				mean=snake_case_    ,				std=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = ChannelDimension.FIRST    ,				**snake_case_    ,				)       ->  ImageInput:
													__lowerCAmelCase     =  do_normalize if do_normalize is not None else self.do_normalize
													__lowerCAmelCase     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
													__lowerCAmelCase     =  patch_size if patch_size is not None else self.patch_size
													__lowerCAmelCase     =  max_patches if max_patches is not None else self.max_patches
													__lowerCAmelCase     =  self.is_vqa

													if kwargs.get("""data_format"""    ,				snake_case_				) is not None:
																			raise ValueError("""data_format is not an accepted input as the outputs are """				)

													__lowerCAmelCase     =  make_list_of_images(snake_case_				)

													if not valid_images(snake_case_				):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			__lowerCAmelCase     =  [convert_to_rgb(snake_case_				) for image in images]

													# All transformations expect numpy arrays.
													__lowerCAmelCase     =  [to_numpy_array(snake_case_				) for image in images]

													if is_vqa:
																			if header_text is None:
																									raise ValueError("""A header text must be provided for VQA models."""				)
																			__lowerCAmelCase     =  kwargs.pop("""font_bytes"""    ,				snake_case_				)
																			__lowerCAmelCase     =  kwargs.pop("""font_path"""    ,				snake_case_				)

																			if isinstance(snake_case_    ,				snake_case_				):
																									__lowerCAmelCase     =  [header_text] * len(snake_case_				)

																			__lowerCAmelCase     =  [
																			    render_header(snake_case_    ,				header_text[i]    ,				font_bytes=snake_case_    ,				font_path=snake_case_				)
																			    for i, image in enumerate(snake_case_				)
																			]

													if do_normalize:
																			__lowerCAmelCase     =  [self.normalize(image=snake_case_				) for image in images]

													# convert to torch tensor and permute
													__lowerCAmelCase     =  [
													    self.extract_flattened_patches(image=snake_case_    ,				max_patches=snake_case_    ,				patch_size=snake_case_				)
													    for image in images
													]

													# create attention mask in numpy
													__lowerCAmelCase     =  [(image.sum(axis=-1				) != 0).astype(np.floataa				) for image in images]

													__lowerCAmelCase     =  BatchFeature(
													    data={"""flattened_patches""": images, """attention_mask""": attention_masks}    ,				tensor_type=snake_case_				)

													return encoded_outputs
