






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

_UpperCamelCase			:	int								=logging.get_logger(__name__)



def  a__ (__lowercase							:Optional[Any]					,  __lowercase							:List[Any]					,  __lowercase							:Union[str, Any]   )		-> Any:
				return [
				    int(1000 * (box[0] / width)   ),
				    int(1000 * (box[1] / height)   ),
				    int(1000 * (box[2] / width)   ),
				    int(1000 * (box[3] / height)   ),
				]



def  a__ (__lowercase							:np.ndarray					,  __lowercase							:Optional[str]					,  __lowercase							:Optional[str]   )		-> Optional[int]:
				_A	:				Tuple   =      to_pil_image(__lowercase   )
				_A  ,				_A	:				Union[str, Any]   =      pil_image.size
				_A	:				Tuple   =      pytesseract.image_to_data(__lowercase					,  lang=__lowercase					,  output_type='''dict'''					,  config=__lowercase   )
				_A  ,				_A  ,				_A  ,				_A  ,				_A	:				Tuple   =      data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

				# filter empty words and corresponding coordinates
				_A	:				Union[str, Any]   =      [idx for idx, word in enumerate(__lowercase   ) if not word.strip()]
				_A	:				int   =      [word for idx, word in enumerate(__lowercase   ) if idx not in irrelevant_indices]
				_A	:				List[str]   =      [coord for idx, coord in enumerate(__lowercase   ) if idx not in irrelevant_indices]
				_A	:				Tuple   =      [coord for idx, coord in enumerate(__lowercase   ) if idx not in irrelevant_indices]
				_A	:				List[str]   =      [coord for idx, coord in enumerate(__lowercase   ) if idx not in irrelevant_indices]
				_A	:				Optional[int]   =      [coord for idx, coord in enumerate(__lowercase   ) if idx not in irrelevant_indices]

				# turn coordinates into (left, top, left+width, top+height) format
				_A	:				Union[str, Any]   =      []
				for x, y, w, h in zip(__lowercase					,  __lowercase					,  __lowercase					,  __lowercase   ):
								_A	:				Union[str, Any]   =      [x, y, x + w, y + h]
								actual_boxes.append(__lowercase   )

				# finally, normalize the bounding boxes
				_A	:				Union[str, Any]   =      []
				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 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       str     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BILINEAR       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = None       ,A__ = None       ,A__ = True       ,A__ = None       ,A__ = ""       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				List[Any]   =      size if size is not None else {'''height''': 224, '''width''': 224}
						_A	:				Any   =      get_size_dict(A__		)

						_A	:				str   =      do_resize
						_A	:				str   =      size
						_A	:				Optional[Any]   =      resample
						_A	:				Optional[int]   =      do_rescale
						_A	:				List[str]   =      rescale_value
						_A	:				Dict   =      do_normalize
						_A	:				Optional[Any]   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_A	:				Dict   =      image_std if image_std is not None else IMAGENET_STANDARD_STD
						_A	:				Any   =      apply_ocr
						_A	:				Optional[Any]   =      ocr_lang
						_A	:				Union[str, Any]   =      tesseract_config







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BILINEAR       ,A__ = None       ,**A__       ,):
						_A	:				Dict   =      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()}"""		)
						_A	:				Union[str, Any]   =      (size['''height'''], size['''width'''])
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__=None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				Dict   =      do_resize if do_resize is not None else self.do_resize
						_A	:				Optional[int]   =      size if size is not None else self.size
						_A	:				List[Any]   =      get_size_dict(A__		)
						_A	:				Tuple   =      resample if resample is not None else self.resample
						_A	:				Optional[int]   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				Optional[Any]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				int   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				List[Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				List[Any]   =      image_std if image_std is not None else self.image_std
						_A	:				str   =      apply_ocr if apply_ocr is not None else self.apply_ocr
						_A	:				Optional[int]   =      ocr_lang if ocr_lang is not None else self.ocr_lang
						_A	:				List[Any]   =      tesseract_config if tesseract_config is not None else self.tesseract_config

						_A	:				List[Any]   =      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.
						_A	:				Union[str, Any]   =      [to_numpy_array(A__		) for image in images]

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

						if do_resize:
										_A	:				Union[str, Any]   =      [self.resize(image=A__       ,size=A__       ,resample=A__		) for image in images]

						if do_rescale:
										_A	:				Optional[Any]   =      [self.rescale(image=A__       ,scale=A__		) for image in images]

						if do_normalize:
										_A	:				List[Any]   =      [self.normalize(image=A__       ,mean=A__       ,std=A__		) for image in images]

						_A	:				Optional[Any]   =      [to_channel_dimension_format(A__       ,A__		) for image in images]

						_A	:				Any   =      BatchFeature(data={'''pixel_values''': images}       ,tensor_type=A__		)

						if apply_ocr:
										_A	:				Tuple   =      words_batch
										_A	:				str   =      boxes_batch
						return data











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

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				Optional[Any]   =      []
						_A	:				str   =      []

						for i in range(self.num_layers		):
										_A	:				Union[str, Any]   =      self.in_channels if i == 0 else self.out_channels

										_A	:				str   =      FlaxResnetBlockaD(
										    in_channels=A__       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

										_A	:				List[str]   =      FlaxTransformeraDModel(
										    in_channels=self.out_channels       ,n_heads=self.num_attention_heads       ,d_head=self.out_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,only_cross_attention=self.only_cross_attention       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
										attentions.append(A__		)

						_A	:				List[str]   =      resnets
						_A	:				Any   =      attentions

						if self.add_downsample:
										_A	:				Any   =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						_A	:				List[str]   =      ()

						for resnet, attn in zip(self.resnets       ,self.attentions		):
										_A	:				Optional[Any]   =      resnet(A__       ,A__       ,deterministic=A__		)
										_A	:				Tuple   =      attn(A__       ,A__       ,deterministic=A__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										_A	:				Optional[int]   =      self.downsamplers_a(A__		)
										output_states += (hidden_states,)

						return hidden_states, output_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				List[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				int   =      self.in_channels if i == 0 else self.out_channels

										_A	:				List[Any]   =      FlaxResnetBlockaD(
										    in_channels=A__       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)
						_A	:				Tuple   =      resnets

						if self.add_downsample:
										_A	:				Optional[Any]   =      FlaxDownsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__=True		):
						_A	:				List[Any]   =      ()

						for resnet in self.resnets:
										_A	:				Optional[int]   =      resnet(A__       ,A__       ,deterministic=A__		)
										output_states += (hidden_states,)

						if self.add_downsample:
										_A	:				List[Any]   =      self.downsamplers_a(A__		)
										output_states += (hidden_states,)

						return hidden_states, output_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				List[str]   =      []
						_A	:				Optional[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				Dict   =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										_A	:				List[str]   =      self.prev_output_channel if i == 0 else self.out_channels

										_A	:				Optional[int]   =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

										_A	:				Union[str, Any]   =      FlaxTransformeraDModel(
										    in_channels=self.out_channels       ,n_heads=self.num_attention_heads       ,d_head=self.out_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,only_cross_attention=self.only_cross_attention       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
										attentions.append(A__		)

						_A	:				Dict   =      resnets
						_A	:				int   =      attentions

						if self.add_upsample:
										_A	:				str   =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__       ,A__=True		):
						for resnet, attn in zip(self.resnets       ,self.attentions		):
										# pop res hidden states
										_A	:				List[Any]   =      res_hidden_states_tuple[-1]
										_A	:				int   =      res_hidden_states_tuple[:-1]
										_A	:				List[Any]   =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1		)

										_A	:				str   =      resnet(A__       ,A__       ,deterministic=A__		)
										_A	:				Optional[int]   =      attn(A__       ,A__       ,deterministic=A__		)

						if self.add_upsample:
										_A	:				Union[str, Any]   =      self.upsamplers_a(A__		)

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   True
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						_A	:				Optional[Any]   =      []

						for i in range(self.num_layers		):
										_A	:				Any   =      self.in_channels if (i == self.num_layers - 1) else self.out_channels
										_A	:				Optional[Any]   =      self.prev_output_channel if i == 0 else self.out_channels

										_A	:				List[Any]   =      FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels       ,out_channels=self.out_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

						_A	:				int   =      resnets

						if self.add_upsample:
										_A	:				List[str]   =      FlaxUpsampleaD(self.out_channels       ,dtype=self.dtype		)







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						for resnet in self.resnets:
										# pop res hidden states
										_A	:				Tuple   =      res_hidden_states_tuple[-1]
										_A	:				Optional[int]   =      res_hidden_states_tuple[:-1]
										_A	:				Any   =      jnp.concatenate((hidden_states, res_hidden_states)       ,axis=-1		)

										_A	:				Optional[Any]   =      resnet(A__       ,A__       ,deterministic=A__		)

						if self.add_upsample:
										_A	:				Tuple   =      self.upsamplers_a(A__		)

						return hidden_states






class 						UpperCAmelCase__ (							nn.Module							):
		__snake_case      :       int
		__snake_case      :       float     =   0.0
		__snake_case      :       int     =   1
		__snake_case      :       int     =   1
		__snake_case      :       bool     =   False
		__snake_case      :       bool     =   False
		__snake_case      :       jnp.dtype     =   jnp.floataa







		def  A__      (					self		):
						# there is always at least one resnet
						_A	:				List[str]   =      [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
						]

						_A	:				Union[str, Any]   =      []

						for _ in range(self.num_layers		):
										_A	:				Any   =      FlaxTransformeraDModel(
										    in_channels=self.in_channels       ,n_heads=self.num_attention_heads       ,d_head=self.in_channels // self.num_attention_heads       ,depth=1       ,use_linear_projection=self.use_linear_projection       ,use_memory_efficient_attention=self.use_memory_efficient_attention       ,dtype=self.dtype       ,)
										attentions.append(A__		)

										_A	:				Any   =      FlaxResnetBlockaD(
										    in_channels=self.in_channels       ,out_channels=self.in_channels       ,dropout_prob=self.dropout       ,dtype=self.dtype       ,)
										resnets.append(A__		)

						_A	:				List[str]   =      resnets
						_A	:				str   =      attentions







		def __call__(					self       ,A__       ,A__       ,A__       ,A__=True		):
						_A	:				Optional[int]   =      self.resnets[0](A__       ,A__		)
						for attn, resnet in zip(self.attentions       ,self.resnets[1:]		):
										_A	:				Dict   =      attn(A__       ,A__       ,deterministic=A__		)
										_A	:				Any   =      resnet(A__       ,A__       ,deterministic=A__		)

						return hidden_states



