

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

UpperCamelCase__  :      Optional[Any]			=    logging.get_logger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.ndarray ,   _SCREAMING_SNAKE_CASE					:		Optional[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[str] = None       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tesseract_config if tesseract_config is not None else ''

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

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

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

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

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

    return words, normalized_boxes





class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	str						 =  ['pixel_values']

  def __init__(					self						,		_A = True						,		_A = None						,		_A = PILImageResampling.BILINEAR						,		_A = True						,		_A = None						,		_A = ""						,		**_A						,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A)

      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						resample
      SCREAMING_SNAKE_CASE_									=						apply_ocr
      SCREAMING_SNAKE_CASE_									=						ocr_lang
      SCREAMING_SNAKE_CASE_									=						tesseract_config

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = PILImageResampling.BILINEAR						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						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()}""")
      SCREAMING_SNAKE_CASE_									=						(size['height'], size['width'])
      return resize(_A						,		size=_A						,		resample=_A						,		data_format=_A						,		**_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = ChannelDimension.FIRST						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_									=						size if size is not None else self.size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A)
      SCREAMING_SNAKE_CASE_									=						resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_									=						apply_ocr if apply_ocr is not None else self.apply_ocr
      SCREAMING_SNAKE_CASE_									=						ocr_lang if ocr_lang is not None else self.ocr_lang
      SCREAMING_SNAKE_CASE_									=						tesseract_config if tesseract_config is not None else self.tesseract_config

      SCREAMING_SNAKE_CASE_									=						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.')

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

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

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

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

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

      if apply_ocr:
          SCREAMING_SNAKE_CASE_									=						words_batch
          SCREAMING_SNAKE_CASE_									=						boxes_batch
      return data








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

      # pass init params to Encoder
      SCREAMING_SNAKE_CASE_									=						Encoder(
          in_channels=_A						,		out_channels=_A						,		down_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		double_z=_A						,		)

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


