
"""simple docstring"""

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
   import PIL

# soft dependency
if is_pytesseract_available():
   import pytesseract

SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Optional[Any] ) ->      Dict:






    """simple docstring"""




    return [
        int(1_000 * (box[0] / width) ),
        int(1_000 * (box[1] / height) ),
        int(1_000 * (box[2] / width) ),
        int(1_000 * (box[3] / height) ),
    ]




def     lowercase__						(					lowerCAmelCase				:					np.ndarray ,				lowerCAmelCase				:					Optional[str] ,				lowerCAmelCase				:					Optional[str] = None ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					tesseract_config if tesseract_config is not None else ''

    # apply OCR
    UpperCAmelCase									=					to_pil_image(lowerCAmelCase )
    UpperCAmelCase			,							UpperCAmelCase									=					pil_image.size
    UpperCAmelCase									=					pytesseract.image_to_data(lowerCAmelCase ,				lang=lowerCAmelCase ,				output_type='dict' ,				config=lowerCAmelCase )
    UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					data['text'], data['left'], data['top'], data['width'], data['height']

    # filter empty words and corresponding coordinates
    UpperCAmelCase									=					[idx for idx, word in enumerate(lowerCAmelCase ) if not word.strip()]
    UpperCAmelCase									=					[word for idx, word in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
    UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
    UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
    UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]
    UpperCAmelCase									=					[coord for idx, coord in enumerate(lowerCAmelCase ) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    UpperCAmelCase									=					[]
    for x, y, w, h in zip(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ):
        UpperCAmelCase									=					[x, y, x + w, y + h]
        actual_boxes.append(lowerCAmelCase )

    # finally, normalize the bounding boxes
    UpperCAmelCase									=					[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase ) )

    assert len(lowerCAmelCase ) == len(lowerCAmelCase ), "Not as many words as there are bounding boxes"

    return words, normalized_boxes







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	["pixel_values"]




      def __init__(							self   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = PILImageResampling.BILINEAR   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = ""   ,       **lowercase_   ,       )						->					None:
          super().__init__(**lowercase_							)
          UpperCAmelCase									=					size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
          UpperCAmelCase									=					get_size_dict(lowercase_							)

          UpperCAmelCase									=					do_resize
          UpperCAmelCase									=					size
          UpperCAmelCase									=					resample
          UpperCAmelCase									=					apply_ocr
          UpperCAmelCase									=					ocr_lang
          UpperCAmelCase									=					tesseract_config




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = PILImageResampling.BILINEAR   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					np.ndarray:
          UpperCAmelCase									=					get_size_dict(lowercase_							)
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"							)
          UpperCAmelCase									=					(size['height'], size['width'])
          return resize(lowercase_   ,       size=lowercase_   ,       resample=lowercase_   ,       data_format=lowercase_   ,       **lowercase_							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = ChannelDimension.FIRST   ,       **lowercase_   ,       )						->					PIL.Image.Image:
          UpperCAmelCase									=					do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase									=					size if size is not None else self.size
          UpperCAmelCase									=					get_size_dict(lowercase_							)
          UpperCAmelCase									=					resample if resample is not None else self.resample
          UpperCAmelCase									=					apply_ocr if apply_ocr is not None else self.apply_ocr
          UpperCAmelCase									=					ocr_lang if ocr_lang is not None else self.ocr_lang
          UpperCAmelCase									=					tesseract_config if tesseract_config is not None else self.tesseract_config

          UpperCAmelCase									=					make_list_of_images(lowercase_							)

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

          if do_resize and size is None:
              raise ValueError('Size must be specified if do_resize is True.'							)

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

          if apply_ocr:
              requires_backends(self   ,       'pytesseract'							)
              UpperCAmelCase									=					[]
              UpperCAmelCase									=					[]
              for image in images:
                  UpperCAmelCase			,							UpperCAmelCase									=					apply_tesseract(lowercase_   ,       lowercase_   ,       lowercase_							)
                  words_batch.append(lowercase_							)
                  boxes_batch.append(lowercase_							)

          if do_resize:
              UpperCAmelCase									=					[self.resize(image=lowercase_   ,       size=lowercase_   ,       resample=lowercase_							) for image in images]

          # flip color channels from RGB to BGR (as Detectron2 requires this)
          UpperCAmelCase									=					[flip_channel_order(lowercase_							) for image in images]
          UpperCAmelCase									=					[to_channel_dimension_format(lowercase_   ,       lowercase_							) for image in images]

          UpperCAmelCase									=					BatchFeature(data={'pixel_values': images}   ,       tensor_type=lowercase_							)

          if apply_ocr:
              UpperCAmelCase									=					words_batch
              UpperCAmelCase									=					boxes_batch
          return data





"""simple docstring"""

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_    					=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

    UpperCAmelCase									=					[trans(img.convert('RGB' ) ) for img in image]
    UpperCAmelCase									=					torch.stack(lowerCAmelCase )
    return image







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          UpperCAmelCase									=					DDIMScheduler.from_config(scheduler.config							)

          self.register_modules(unet=lowercase_   ,       scheduler=lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          if strength < 0 or strength > 1:
              raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

          UpperCAmelCase									=					max(num_inference_steps - init_timestep   ,       0							)
          UpperCAmelCase									=					self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          if not isinstance(lowercase_   ,       (torch.Tensor, PIL.Image.Image, list)							):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_							)}"							)

          UpperCAmelCase									=					image.to(device=lowercase_   ,       dtype=lowercase_							)

          if isinstance(lowercase_   ,       lowercase_							) and len(lowercase_							) != batch_size:
              raise ValueError(
                  F"You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"
                  F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

          # get latents
          print('add noise to latents at timestep'   ,       lowercase_							)
          UpperCAmelCase									=					self.scheduler.add_noise(lowercase_   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					init_latents

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							UpperCAmelCase									=					self.get_timesteps(lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					timesteps[:1].repeat(lowercase_							)

          # 4. Prepare latent variables
          UpperCAmelCase									=					self.prepare_latents(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.unet.dtype   ,       self.device   ,       lowercase_							)
          UpperCAmelCase									=					latents

          # 5. Denoising loop
          for t in self.progress_bar(lowercase_							):
              # 1. predict noise model_output
              UpperCAmelCase									=					self.unet(lowercase_   ,       lowercase_							).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       eta=lowercase_   ,       use_clipped_model_output=lowercase_   ,       generator=lowercase_   ,       ).prev_sample

          UpperCAmelCase									=					(image / 2 + 0.5).clamp(0   ,       1							)
          UpperCAmelCase									=					image.cpu().permute(0   ,       2   ,       3   ,       1							).numpy()
          if output_type == "pil":
              UpperCAmelCase									=					self.numpy_to_pil(lowercase_							)

          if not return_dict:
              return (image, latent_timestep.item())

          return ImagePipelineOutput(images=lowercase_							)



