




import flax.linen as nn
import jax
import jax.numpy as jnp







class   A(						nn.Module   ):



 '''simple docstring'''


 UpperCamelCase						   =						42
 UpperCamelCase						   =						jnp.floataa

 def 				a__ (				self					:  int  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    nn.Conv(
           self.out_channels						,							kernel_size=(3, 3)						,							strides=(1, 1)						,							padding=((1, 1), (1, 1))						,							dtype=self.dtype						,							)





 def __call__(				self					:  List[Any]						,							A_					:  List[str]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    hidden_states.shape
       lowerCamelCase_		     =    jax.image.resize(
           A_						,							shape=(batch, height * 2, width * 2, channels)						,							method='nearest'						,							)
       lowerCamelCase_		     =    self.conv(A_  )
       return hidden_states







class   A(						nn.Module   ):



 '''simple docstring'''


 UpperCamelCase						   =						42
 UpperCamelCase						   =						jnp.floataa

 def 				a__ (				self					:  List[Any]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    nn.Conv(
           self.out_channels						,							kernel_size=(3, 3)						,							strides=(2, 2)						,							padding=((1, 1), (1, 1))						,							dtype=self.dtype						,							)





 def __call__(				self					:  Optional[int]						,							A_					:  Tuple  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    self.conv(A_  )
       return hidden_states







class   A(						nn.Module   ):



 '''simple docstring'''


 UpperCamelCase						   =						42
 UpperCamelCase						   =						None
 UpperCamelCase						   =						0.0
 UpperCamelCase						   =						None
 UpperCamelCase						   =						jnp.floataa

 def 				a__ (				self					:  Tuple  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.in_channels if self.out_channels is None else self.out_channels

       lowerCamelCase_		     =    nn.GroupNorm(num_groups=32						,							epsilon=1E-5  )
       lowerCamelCase_		     =    nn.Conv(
           A_						,							kernel_size=(3, 3)						,							strides=(1, 1)						,							padding=((1, 1), (1, 1))						,							dtype=self.dtype						,							)

       lowerCamelCase_		     =    nn.Dense(A_						,							dtype=self.dtype  )

       lowerCamelCase_		     =    nn.GroupNorm(num_groups=32						,							epsilon=1E-5  )
       lowerCamelCase_		     =    nn.Dropout(self.dropout_prob  )
       lowerCamelCase_		     =    nn.Conv(
           A_						,							kernel_size=(3, 3)						,							strides=(1, 1)						,							padding=((1, 1), (1, 1))						,							dtype=self.dtype						,							)

       lowerCamelCase_		     =    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       lowerCamelCase_		     =    None
       if use_nin_shortcut:
             lowerCamelCase_		     =    nn.Conv(
                 A_						,							kernel_size=(1, 1)						,							strides=(1, 1)						,							padding='VALID'						,							dtype=self.dtype						,							)





 def __call__(				self					:  List[str]						,							A_					:  Any						,							A_					:  str						,							A_					:  Tuple=True  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    hidden_states
       lowerCamelCase_		     =    self.norma(A_  )
       lowerCamelCase_		     =    nn.swish(A_  )
       lowerCamelCase_		     =    self.conva(A_  )

       lowerCamelCase_		     =    self.time_emb_proj(nn.swish(A_  )  )
       lowerCamelCase_		     =    jnp.expand_dims(jnp.expand_dims(A_						,							1  )						,							1  )
       lowerCamelCase_		     =    hidden_states + temb

       lowerCamelCase_		     =    self.norma(A_  )
       lowerCamelCase_		     =    nn.swish(A_  )
       lowerCamelCase_		     =    self.dropout(A_						,							A_  )
       lowerCamelCase_		     =    self.conva(A_  )

       if self.conv_shortcut is not None:
             lowerCamelCase_		     =    self.conv_shortcut(A_  )

       return hidden_states + residual









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



