
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowerCamelCase_  :	Tuple 				=					"scheduler_config.json"

class     _UpperCamelCase			(   __lowerCAmelCase					):






			'''simple docstring'''

			__UpperCamelCase :  Dict    =	1
			__UpperCamelCase :  List[str]    =	2
			__UpperCamelCase :  Dict    =	3
			__UpperCamelCase :  int    =	4
			__UpperCamelCase :  int    =	5




@dataclass
class     _UpperCamelCase			(   __lowerCAmelCase					):






			'''simple docstring'''

			__UpperCamelCase :  jnp.ndarray




class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  str    =	SCHEDULER_CONFIG_NAME
			__UpperCamelCase :  Optional[int]    =	["dtype"]
			__UpperCamelCase :  str    =	[]
			__UpperCamelCase :  List[Any]    =	True

			@classmethod
			def  lowerCAmelCase__		(    cls	:    int  ,		snake_case_	:    Dict[str, Any] = None  ,		snake_case_	:    Optional[str] = None  ,		snake_case_	:    int=False  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							Optional[Any]		=  cls.load_config(
							    pretrained_model_name_or_path=lowerCamelCase__  ,		subfolder=lowerCamelCase__  ,		return_unused_kwargs=lowerCamelCase__  ,		**lowerCamelCase__  ,		)
							UpperCamelCase_:							Dict		=  cls.from_config(lowerCamelCase__  ,		return_unused_kwargs=lowerCamelCase__  ,		**lowerCamelCase__ )

							if hasattr(lowerCamelCase__  ,		"""create_state""" ) and getattr(lowerCamelCase__  ,		"""has_state"""  ,		lowerCamelCase__ ):
											UpperCamelCase_:							Dict		=  scheduler.create_state()

							if return_unused_kwargs:
											return scheduler, state, unused_kwargs

							return scheduler, state

			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    Union[str, os.PathLike]  ,		snake_case_	:    bool = False  ,		**snake_case_	:    Optional[Any] ):
							self.save_config(save_directory=lowerCamelCase__  ,		push_to_hub=lowerCamelCase__  ,		**lowerCamelCase__ )

			@property
			def  lowerCAmelCase__		(    self	:    List[str] ):
							return self._get_compatibles()




			@classmethod
			def  lowerCAmelCase__		(    cls	:    Optional[int] ):
							UpperCamelCase_:							str		=  list(set([cls.__name__] + cls._compatibles ) )
							UpperCamelCase_:							Union[str, Any]		=  importlib.import_module(__name__.split(""".""" )[0] )
							UpperCamelCase_:							int		=  [
							    getattr(lowerCamelCase__  ,		lowerCamelCase__ ) for c in compatible_classes_str if hasattr(lowerCamelCase__  ,		lowerCamelCase__ )
							]
							return compatible_classes







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[str]:
				assert len(lowerCamelCase  ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase  ) - x.ndim)  )       ,      lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase=0.999       ,      lowerCamelCase=jnp.floataa  )					->		Dict:



				def alpha_bar(lowerCamelCase  ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2  ) ** 2

				UpperCamelCase_:							Union[str, Any]		=  []
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Union[str, Any]		=  i / num_diffusion_timesteps
								UpperCamelCase_:							Union[str, Any]		=  (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCamelCase  ) / alpha_bar(lowerCamelCase  )       ,      lowerCamelCase  )  )
				return jnp.array(lowerCamelCase       ,      dtype=lowerCamelCase  )

@flax.struct.dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  jnp.ndarray
			__UpperCamelCase :  jnp.ndarray
			__UpperCamelCase :  jnp.ndarray

			@classmethod
			def  lowerCAmelCase__		(    cls	:    List[Any]  ,		snake_case_	:    Union[str, Any] ):
							UpperCamelCase_:							List[Any]		=  scheduler.config

							if config.trained_betas is not None:
											UpperCamelCase_:							Tuple		=  jnp.asarray(config.trained_betas  ,		dtype=scheduler.dtype )
							elif config.beta_schedule == "linear":
											UpperCamelCase_:							Dict		=  jnp.linspace(config.beta_start  ,		config.beta_end  ,		config.num_train_timesteps  ,		dtype=scheduler.dtype )
							elif config.beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											UpperCamelCase_:							Optional[Any]		=  (
											    jnp.linspace(
											        config.beta_start**0.5  ,		config.beta_end**0.5  ,		config.num_train_timesteps  ,		dtype=scheduler.dtype )
											    ** 2
											)
							elif config.beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											UpperCamelCase_:							int		=  betas_for_alpha_bar(config.num_train_timesteps  ,		dtype=scheduler.dtype )
							else:
											raise NotImplementedError(
											    f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

							UpperCamelCase_:							Union[str, Any]		=  1.0 - betas

							UpperCamelCase_:							Optional[int]		=  jnp.cumprod(lowerCamelCase__  ,		axis=0 )

							return cls(
							    alphas=lowerCamelCase__  ,		betas=lowerCamelCase__  ,		alphas_cumprod=lowerCamelCase__  ,		)







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		int:
				UpperCamelCase_:							Optional[Any]		=  state.alphas_cumprod

				UpperCamelCase_:							Optional[int]		=  alphas_cumprod[timesteps] ** 0.5
				UpperCamelCase_:							List[Any]		=  sqrt_alpha_prod.flatten()
				UpperCamelCase_:							Union[str, Any]		=  broadcast_to_shape_from_left(lowerCamelCase       ,      original_samples.shape  )

				UpperCamelCase_:							Any		=  (1 - alphas_cumprod[timesteps]) ** 0.5
				UpperCamelCase_:							str		=  sqrt_one_minus_alpha_prod.flatten()
				UpperCamelCase_:							Optional[Any]		=  broadcast_to_shape_from_left(lowerCamelCase       ,      original_samples.shape  )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Dict:
				UpperCamelCase_:							Dict		=  get_sqrt_alpha_prod(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
				UpperCamelCase_:							Union[str, Any]		=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				UpperCamelCase_:							Dict		=  get_sqrt_alpha_prod(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
				UpperCamelCase_:							int		=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity







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:
							lowerCamelCase_  :	List[str] 				=					False


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)
lowerCamelCase_  :	Optional[int] 				=					"""ybelkada/fonts"""







def     A__   (  )					->		Dict:
				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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				requires_backends(lowerCamelCase       ,      ["""torch"""]  )
				_check_torch_version()

				UpperCamelCase_:							Tuple		=  image_tensor.unsqueeze(0  )
				UpperCamelCase_:							Any		=  torch.nn.functional.unfold(lowerCamelCase       ,      (patch_height, patch_width)       ,      stride=(patch_height, patch_width)  )
				UpperCamelCase_:							int		=  patches.reshape(image_tensor.size(0  )       ,      image_tensor.size(1  )       ,      lowerCamelCase       ,      lowerCamelCase       ,      -1  )
				UpperCamelCase_:							Any		=  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     A__   (  lowerCamelCase       ,      lowerCamelCase = 36       ,      lowerCamelCase = "black"       ,      lowerCamelCase = "white"       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = None       ,      lowerCamelCase = None       ,      )					->		Image.Image:
				requires_backends(lowerCamelCase       ,      """vision"""  )
				# Add new lines so that each line is no more than 80 characters.

				UpperCamelCase_:							List[str]		=  textwrap.TextWrapper(width=80  )
				UpperCamelCase_:							Optional[int]		=  wrapper.wrap(text=lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  """\n""".join(lowerCamelCase  )

				if font_bytes is not None and font_path is None:
								UpperCamelCase_:							List[Any]		=  io.BytesIO(lowerCamelCase  )
				elif font_path is not None:
								UpperCamelCase_:							List[Any]		=  font_path
				else:
								UpperCamelCase_:							Tuple		=  hf_hub_download(lowerCamelCase       ,      """Arial.TTF"""  )
				UpperCamelCase_:							Optional[Any]		=  ImageFont.truetype(lowerCamelCase       ,      encoding="""UTF-8"""       ,      size=lowerCamelCase  )

				# Use a temporary canvas to determine the width and height in pixels when
				# rendering the text.
				UpperCamelCase_:							str		=  ImageDraw.Draw(Image.new("""RGB"""       ,      (1, 1)       ,      lowerCamelCase  )  )
				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  temp_draw.textbbox((0, 0)       ,      lowerCamelCase       ,      lowerCamelCase  )

				# Create the actual image with a bit of padding around the text.
				UpperCamelCase_:							Optional[int]		=  text_width + left_padding + right_padding
				UpperCamelCase_:							List[str]		=  text_height + top_padding + bottom_padding
				UpperCamelCase_:							Union[str, Any]		=  Image.new("""RGB"""       ,      (image_width, image_height)       ,      lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  ImageDraw.Draw(lowerCamelCase  )
				draw.text(xy=(left_padding, top_padding)       ,      text=lowerCamelCase       ,      fill=lowerCamelCase       ,      font=lowerCamelCase  )
				return image







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )					->		List[str]:
				requires_backends(lowerCamelCase       ,      """vision"""  )

				# Convert to PIL image if necessary
				UpperCamelCase_:							List[str]		=  to_pil_image(lowerCamelCase  )

				UpperCamelCase_:							Union[str, Any]		=  render_text(lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  max(header_image.width       ,      image.width  )

				UpperCamelCase_:							Tuple		=  int(image.height * (new_width / image.width)  )
				UpperCamelCase_:							Dict		=  int(header_image.height * (new_width / header_image.width)  )

				UpperCamelCase_:							str		=  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
				UpperCamelCase_:							Optional[Any]		=  to_numpy_array(lowerCamelCase  )

				if infer_channel_dimension_format(lowerCamelCase  ) == ChannelDimension.LAST:
								UpperCamelCase_:							Tuple		=  to_channel_dimension_format(lowerCamelCase       ,      ChannelDimension.LAST  )

				return new_image





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	["""flattened_patches"""]

			def __init__(    self	:    int  ,		snake_case_	:    bool = True  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    int = 2048  ,		snake_case_	:    bool = False  ,		**snake_case_	:    Any  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							int		=  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							List[Any]		=  do_convert_rgb
							UpperCamelCase_:							Tuple		=  max_patches
							UpperCamelCase_:							Tuple		=  is_vqa

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    int  ,		snake_case_	:    dict  ,		**snake_case_	:    Tuple ):
							requires_backends(self.extract_flattened_patches  ,		"""torch""" )
							_check_torch_version()

							# convert to torch
							UpperCamelCase_:							int		=  to_channel_dimension_format(snake_case_  ,		ChannelDimension.FIRST )
							UpperCamelCase_:							List[str]		=  torch.from_numpy(snake_case_ )

							UpperCamelCase_,				UpperCamelCase_:							List[Any]		=  patch_size["""height"""], patch_size["""width"""]
							UpperCamelCase_,				UpperCamelCase_:							Tuple		=  get_image_size(snake_case_ )

							# maximize scale s.t.
							UpperCamelCase_:							List[Any]		=  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
							UpperCamelCase_:							Any		=  max(min(math.floor(scale * image_height / patch_height )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							List[str]		=  max(min(math.floor(scale * image_width / patch_width )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							int		=  max(num_feasible_rows * patch_height  ,		1 )
							UpperCamelCase_:							Optional[Any]		=  max(num_feasible_cols * patch_width  ,		1 )

							UpperCamelCase_:							str		=  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]
							UpperCamelCase_:							List[str]		=  torch_extract_patches(snake_case_  ,		snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  patches.shape
							UpperCamelCase_:							List[str]		=  patches_shape[1]
							UpperCamelCase_:							Optional[Any]		=  patches_shape[2]
							UpperCamelCase_:							List[str]		=  patches_shape[3]

							# [rows * columns, patch_height * patch_width * image_channels]
							UpperCamelCase_:							Union[str, Any]		=  patches.reshape([rows * columns, depth] )

							# [rows * columns, 1]
							UpperCamelCase_:							Optional[Any]		=  torch.arange(snake_case_ ).reshape([rows, 1] ).repeat(1  ,		snake_case_ ).reshape([rows * columns, 1] )
							UpperCamelCase_:							Optional[int]		=  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]
							UpperCamelCase_:							Union[str, Any]		=  row_ids.to(torch.floataa )
							UpperCamelCase_:							str		=  col_ids.to(torch.floataa )

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

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

							UpperCamelCase_:							List[Any]		=  to_numpy_array(snake_case_ )

							return result

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Tuple ):
							if image.dtype == np.uinta:
											UpperCamelCase_:							List[str]		=  image.astype(np.floataa )

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

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




			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    ImageInput  ,		snake_case_	:    Optional[str] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Optional[Dict[str, int]] = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Tuple		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Tuple		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCamelCase_:							Optional[Any]		=  patch_size if patch_size is not None else self.patch_size
							UpperCamelCase_:							Optional[int]		=  max_patches if max_patches is not None else self.max_patches
							UpperCamelCase_:							Tuple		=  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 """ )

							UpperCamelCase_:							Dict		=  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:
											UpperCamelCase_:							str		=  [convert_to_rgb(snake_case_ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Union[str, Any]		=  [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.""" )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_bytes"""  ,		snake_case_ )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_path"""  ,		snake_case_ )

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

											UpperCamelCase_:							str		=  [
											    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:
											UpperCamelCase_:							Union[str, Any]		=  [self.normalize(image=snake_case_ ) for image in images]

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

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

							UpperCamelCase_:							Optional[Any]		=  BatchFeature(
							    data={"""flattened_patches""": images, """attention_mask""": attention_masks}  ,		tensor_type=snake_case_ )

							return encoded_outputs






