


'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	UNetaDModel
     lowerCamelCase				:	KarrasVeScheduler





     def __init__( self       :      str    ,      UpperCAmelCase__       :      UNetaDModel    ,      UpperCAmelCase__       :      KarrasVeScheduler    )				->  int:
        super().__init__()
        self.register_modules(unet=UpperCAmelCase__    ,      scheduler=UpperCAmelCase__    )





     @torch.no_grad()
     def __call__( self       :      Dict    ,      UpperCAmelCase__       :      int = 1    ,      UpperCAmelCase__       :      int = 5_0    ,      UpperCAmelCase__       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,      UpperCAmelCase__       :      Optional[str] = "pil"    ,      UpperCAmelCase__       :      bool = True    ,      **UpperCAmelCase__       :      Dict    ,      )				->  Union[Tuple, ImagePipelineOutput]:
        lowerCAmelCase     =			self.unet.config.sample_size
        lowerCAmelCase     =			(batch_size, 3, img_size, img_size)

        lowerCAmelCase     =			self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        lowerCAmelCase     =			randn_tensor(UpperCAmelCase__    ,      generator=UpperCAmelCase__    ,      device=self.device    ) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(UpperCAmelCase__    )

        for t in self.progress_bar(self.scheduler.timesteps    ):
           # here sigma_t == t_i from the paper
           lowerCAmelCase     =			self.scheduler.schedule[t]
           lowerCAmelCase     =			self.scheduler.schedule[t - 1] if t > 0 else 0

           # 1. Select temporarily increased noise level sigma_hat
           # 2. Add new noise to move from sample_i to sample_hat
           lowerCAmelCase     ,  lowerCAmelCase     =			self.scheduler.add_noise_to_input(UpperCAmelCase__    ,      UpperCAmelCase__    ,      generator=UpperCAmelCase__    )

           # 3. Predict the noise residual given the noise magnitude `sigma_hat`
           # The model inputs and output are adjusted by following eq. (213) in [1].
           lowerCAmelCase     =			(sigma_hat / 2) * model((sample_hat + 1) / 2    ,      sigma_hat / 2    ).sample

           # 4. Evaluate dx/dt at sigma_hat
           # 5. Take Euler step from sigma to sigma_prev
           lowerCAmelCase     =			self.scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

           if sigma_prev != 0:
              # 6. Apply 2nd order correction
              # The model inputs and output are adjusted by following eq. (213) in [1].
              lowerCAmelCase     =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,      sigma_prev / 2    ).sample
              lowerCAmelCase     =			self.scheduler.step_correct(
                  UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      step_output.prev_sample    ,      step_output['derivative']    ,      )
           lowerCAmelCase     =			step_output.prev_sample

        lowerCAmelCase     =			(sample / 2 + 0.5).clamp(0    ,      1    )
        lowerCAmelCase     =			sample.cpu().permute(0    ,      2    ,      3    ,      1    ).numpy()
        if output_type == "pil":
           lowerCAmelCase     =			self.numpy_to_pil(UpperCAmelCase__    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase__    )











from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

lowerCamelCase				:		List[str]     =   logging.get_logger(__name__)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Optional[int]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      return [
          int(10_00 * (box[0] / width)     ),
          int(10_00 * (box[1] / height)     ),
          int(10_00 * (box[2] / width)     ),
          int(10_00 * (box[3] / height)     ),
      ]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       np.ndarray	,							lowercase	:       Optional[str]	,							lowercase	:       Optional[str]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    to_pil_image(lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_		     =    pil_image.size
      lowerCamelCase_		     =    pytesseract.image_to_data(lowercase	,							lang=lowercase	,							output_type='dict'	,							config=lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

      return words, normalized_boxes







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  int						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  bool = True						,							A_					:  float = 1 / 255						,							A_					:  bool = True						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = True						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = ""						,							**A_					:  Optional[int]						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_value
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowerCamelCase_		     =    apply_ocr
       lowerCamelCase_		     =    ocr_lang
       lowerCamelCase_		     =    tesseract_config

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       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()}"""  )
       lowerCamelCase_		     =    (size['height'], size['width'])
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Any						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Optional[Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  int						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  List[Any]						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  Dict=None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  Union[float, Iterable[float]] = None						,							A_					:  bool = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[str] = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  ChannelDimension = ChannelDimension.FIRST						,							**A_					:  Any						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    apply_ocr if apply_ocr is not None else self.apply_ocr
       lowerCamelCase_		     =    ocr_lang if ocr_lang is not None else self.ocr_lang
       lowerCamelCase_		     =    tesseract_config if tesseract_config is not None else self.tesseract_config

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             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.'  )

       if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'  )

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

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
             requires_backends(self						,							'pytesseract'  )
             lowerCamelCase_		     =    []
             lowerCamelCase_		     =    []
             for image in images:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    apply_tesseract(A_						,							A_						,							A_  )
                   words_batch.append(A_  )
                   boxes_batch.append(A_  )

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

       if do_rescale:
             lowerCamelCase_		     =    [self.rescale(image=A_						,							scale=A_  ) for image in images]

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    BatchFeature(data={'pixel_values': images}						,							tensor_type=A_  )

       if apply_ocr:
             lowerCamelCase_		     =    words_batch
             lowerCamelCase_		     =    boxes_batch
       return data



