


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__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['vqvae']

					def __init__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   AutoencoderKL							,					UpperCamelCase__       :   UNetaDConditionModel							,					UpperCamelCase__       :   Mel							,					UpperCamelCase__       :   Union[DDIMScheduler, DDPMScheduler]							,					):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										return 50 if isinstance(self.scheduler							,					UpperCamelCase__       ) else 1_000

					@torch.no_grad()
					def __call__(     self       :   List[Any]							,					UpperCamelCase__       :   int = 1							,					UpperCamelCase__       :   str = None							,					UpperCamelCase__       :   np.ndarray = None							,					UpperCamelCase__       :   int = 0							,					UpperCamelCase__       :   int = 0							,					UpperCamelCase__       :   int = None							,					UpperCamelCase__       :   torch.Generator = None							,					UpperCamelCase__       :   float = 0							,					UpperCamelCase__       :   float = 0							,					UpperCamelCase__       :   torch.Generator = None							,					UpperCamelCase__       :   float = 0							,					UpperCamelCase__       :   torch.Tensor = None							,					UpperCamelCase__       :   torch.Tensor = None							,					UpperCamelCase__       :   Union[str, Any]=True							,					):






										'''simple docstring'''






										lowercase_  							=			steps or self.get_default_steps()
										self.scheduler.set_timesteps(UpperCamelCase__       )
										lowercase_  							=			step_generator or generator
										# For backwards compatibility
										if type(self.unet.config.sample_size       ) == int:
															lowercase_  							=			(self.unet.config.sample_size, self.unet.config.sample_size)
										if noise is None:
															lowercase_  							=			randn_tensor(
															    (
															        batch_size,
															        self.unet.config.in_channels,
															        self.unet.config.sample_size[0],
															        self.unet.config.sample_size[1],
															    )							,					generator=UpperCamelCase__							,					device=self.device							,					)
										lowercase_  							=			noise
										lowercase_  							=			None

										if audio_file is not None or raw_audio is not None:
															self.mel.load_audio(UpperCamelCase__							,					UpperCamelCase__       )
															lowercase_  							=			self.mel.audio_slice_to_image(UpperCamelCase__       )
															lowercase_  							=			np.frombuffer(input_image.tobytes()							,					dtype="""uint8"""       ).reshape(
															    (input_image.height, input_image.width)       )
															lowercase_  							=			(input_image / 255) * 2 - 1
															lowercase_  							=			torch.tensor(input_image[np.newaxis, :, :]							,					dtype=torch.float       ).to(self.device       )

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

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

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

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

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

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

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

										lowercase_  							=			(images / 2 + 0.5).clamp(0							,					1       )
										lowercase_  							=			images.cpu().permute(0							,					2							,					3							,					1       ).numpy()
										lowercase_  							=			(images * 255).round().astype("""uint8"""       )
										lowercase_  							=			list(
										    (Image.fromarray(_[:, :, 0]       ) for _ in images)
										    if images.shape[3] == 1
										    else (Image.fromarray(UpperCamelCase__							,					mode="""RGB"""       ).convert("""L"""       ) for _ in images)       )

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

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

					@torch.no_grad()
					def   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   List[Image.Image]							,					UpperCamelCase__       :   int = 50       ):






										'''simple docstring'''






										assert isinstance(self.scheduler							,					UpperCamelCase__       )
										self.scheduler.set_timesteps(UpperCamelCase__       )
										lowercase_  							=			np.array(
										    [np.frombuffer(image.tobytes()							,					dtype="""uint8"""       ).reshape((1, image.height, image.width)       ) for image in images]       )
										lowercase_  							=			(sample / 255) * 2 - 1
										lowercase_  							=			torch.Tensor(UpperCamelCase__       ).to(self.device       )

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

										return sample







					@staticmethod
					def   UpperCAmelCase__       (     UpperCamelCase__       :   torch.Tensor							,					UpperCamelCase__       :   torch.Tensor							,					UpperCamelCase__       :   float       ):






										'''simple docstring'''






										lowercase_  							=			acos(torch.dot(torch.flatten(UpperCamelCase__       )							,					torch.flatten(UpperCamelCase__       )       ) / torch.norm(UpperCamelCase__       ) / torch.norm(UpperCamelCase__       )       )
										return sin((1 - alpha) * theta       ) * xa / sin(UpperCamelCase__       ) + sin(alpha * theta       ) * xa / sin(UpperCamelCase__       )






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:
							a					  =     False


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


def 			UpperCAmelCase_				(      ):
					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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					requires_backends(UpperCAmelCase__						,							["""torch"""]  )
					_check_torch_version()

					lowercase_  							=			image_tensor.unsqueeze(0  )
					lowercase_  							=			torch.nn.functional.unfold(UpperCAmelCase__						,							(patch_height, patch_width)						,							stride=(patch_height, patch_width)  )
					lowercase_  							=			patches.reshape(image_tensor.size(0  )						,							image_tensor.size(1  )						,							UpperCAmelCase__						,							UpperCAmelCase__						,							-1  )
					lowercase_  							=			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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__ = 3_6						,							UpperCAmelCase__ = "black"						,							UpperCAmelCase__ = "white"						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = None						,							):
					requires_backends(UpperCAmelCase__						,							"""vision"""  )
					# Add new lines so that each line is no more than 80 characters.

					lowercase_  							=			textwrap.TextWrapper(width=8_0  )
					lowercase_  							=			wrapper.wrap(text=UpperCAmelCase__  )
					lowercase_  							=			"""\n""".join(UpperCAmelCase__  )

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

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							**UpperCAmelCase__  ):
					requires_backends(UpperCAmelCase__						,							"""vision"""  )

					# Convert to PIL image if necessary
					lowercase_  							=			to_pil_image(UpperCAmelCase__  )

					lowercase_  							=			render_text(UpperCAmelCase__						,							**UpperCAmelCase__  )
					lowercase_  							=			max(header_image.width						,							image.width  )

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

					lowercase_  							=			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
					lowercase_  							=			to_numpy_array(UpperCAmelCase__  )

					if infer_channel_dimension_format(UpperCAmelCase__  ) == ChannelDimension.LAST:
										lowercase_  							=			to_channel_dimension_format(UpperCAmelCase__						,							ChannelDimension.LAST  )

					return new_image







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Tuple						=   ['flattened_patches']

					def __init__(     self       :   str							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   int = 2_048							,					UpperCamelCase__       :   bool = False							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )
										lowercase_  							=			patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
										lowercase_  							=			do_normalize
										lowercase_  							=			do_convert_rgb
										lowercase_  							=			max_patches
										lowercase_  							=			is_vqa

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   dict							,					**UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






										requires_backends(self.extract_flattened_patches							,					"""torch"""       )
										_check_torch_version()

										# convert to torch
										lowercase_  							=			to_channel_dimension_format(UpperCamelCase__							,					ChannelDimension.FIRST       )
										lowercase_  							=			torch.from_numpy(UpperCamelCase__       )

										lowercase_							,					lowercase_  							=			patch_size["""height"""], patch_size["""width"""]
										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )

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

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

										# [1, rows, columns, patch_height * patch_width * image_channels]
										lowercase_  							=			torch_extract_patches(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )

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

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

										# [rows * columns, 1]
										lowercase_  							=			torch.arange(UpperCamelCase__       ).reshape([rows, 1]       ).repeat(1							,					UpperCamelCase__       ).reshape([rows * columns, 1]       )
										lowercase_  							=			torch.arange(UpperCamelCase__       ).reshape([1, columns]       ).repeat(UpperCamelCase__							,					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]
										lowercase_  							=			row_ids.to(torch.floataa       )
										lowercase_  							=			col_ids.to(torch.floataa       )

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

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

										lowercase_  							=			to_numpy_array(UpperCamelCase__       )

										return result

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										if image.dtype == np.uinta:
															lowercase_  							=			image.astype(np.floataa       )

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

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







					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[str] = None							,					UpperCamelCase__       :   bool = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   ChannelDimension = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Union[str, Any]							,					):






										'''simple docstring'''






										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_  							=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
										lowercase_  							=			patch_size if patch_size is not None else self.patch_size
										lowercase_  							=			max_patches if max_patches is not None else self.max_patches
										lowercase_  							=			self.is_vqa

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

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															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:
															lowercase_  							=			[convert_to_rgb(UpperCamelCase__       ) for image in images]

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

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

															if isinstance(UpperCamelCase__							,					UpperCamelCase__       ):
																				lowercase_  							=			[header_text] * len(UpperCamelCase__       )

															lowercase_  							=			[
															    render_header(UpperCamelCase__							,					header_text[i]							,					font_bytes=UpperCamelCase__							,					font_path=UpperCamelCase__       )
															    for i, image in enumerate(UpperCamelCase__       )
															]

										if do_normalize:
															lowercase_  							=			[self.normalize(image=UpperCamelCase__       ) for image in images]

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

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

										lowercase_  							=			BatchFeature(
										    data={"""flattened_patches""": images, """attention_mask""": attention_masks}							,					tensor_type=UpperCamelCase__       )

										return encoded_outputs


