






"""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					:				Tuple					    =						logging.get_logger(__name__)







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		List[str] )      ->				Dict:
  return [
      int(1000 * (box[0] / width) ),
      int(1000 * (box[1] / height) ),
      int(1000 * (box[2] / width) ),
      int(1000 * (box[3] / height) ),
  ]







def      lowerCamelCase__       (							_lowerCamelCase       :		np.ndarray		,				_lowerCamelCase       :		Optional[str]		,				_lowerCamelCase       :		Optional[str] = None )      ->				Tuple:
  lowerCamelCase_         =						tesseract_config if tesseract_config is not None else ''

  # apply OCR
  lowerCamelCase_         =						to_pil_image(_lowerCamelCase )
  lowerCamelCase_     ,	lowerCamelCase_         =						pil_image.size
  lowerCamelCase_         =						pytesseract.image_to_data(_lowerCamelCase		,				lang=_lowerCamelCase		,				output_type='dict'		,				config=_lowerCamelCase )
  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(_lowerCamelCase ) if not word.strip()]
  lowerCamelCase_         =						[word for idx, word in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
  lowerCamelCase_         =						[coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
  lowerCamelCase_         =						[coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
  lowerCamelCase_         =						[coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
  lowerCamelCase_         =						[coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]

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

  # finally, normalize the bounding boxes
  lowerCamelCase_         =						[]
  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      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     int						= ["""pixel_values"""]






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Dict[str, int] = None       ,						__SCREAMING_SNAKE_CASE     :							PILImageResampling = PILImageResampling.BILINEAR       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = ""       ,						**__SCREAMING_SNAKE_CASE     :							int       ,						)     ->		None:
      super().__init__(**__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						size if size is not None else {'height': 224, 'width': 224}
      lowerCamelCase_         =						get_size_dict(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						do_resize
      lowerCamelCase_         =						size
      lowerCamelCase_         =						resample
      lowerCamelCase_         =						apply_ocr
      lowerCamelCase_         =						ocr_lang
      lowerCamelCase_         =						tesseract_config






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							np.ndarray       ,						__SCREAMING_SNAKE_CASE     :							Dict[str, int]       ,						__SCREAMING_SNAKE_CASE     :							PILImageResampling = PILImageResampling.BILINEAR       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, ChannelDimension]] = None       ,						**__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						)     ->		np.ndarray:
      lowerCamelCase_         =						get_size_dict(__SCREAMING_SNAKE_CASE					)
      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(__SCREAMING_SNAKE_CASE       ,						size=__SCREAMING_SNAKE_CASE       ,						resample=__SCREAMING_SNAKE_CASE       ,						data_format=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							ImageInput       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							Dict[str, int] = None       ,						__SCREAMING_SNAKE_CASE     :							PILImageResampling = None       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, TensorType]] = None       ,						__SCREAMING_SNAKE_CASE     :							ChannelDimension = ChannelDimension.FIRST       ,						**__SCREAMING_SNAKE_CASE     :							List[str]       ,						)     ->		PIL.Image.Image:
      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(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						resample if resample is not None else self.resample
      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(__SCREAMING_SNAKE_CASE					)

      if not valid_images(__SCREAMING_SNAKE_CASE					):
        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.
      lowerCamelCase_         =						[to_numpy_array(__SCREAMING_SNAKE_CASE					) for image in images]

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

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

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

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

      if apply_ocr:
        lowerCamelCase_         =						words_batch
        lowerCamelCase_         =						boxes_batch
      return data












"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Dict						= """Salesforce/blip-image-captioning-base"""
    SCREAMING_SNAKE_CASE						:     int						= (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    SCREAMING_SNAKE_CASE						:     Optional[int]						= """image_captioner"""
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= AutoModelForVisionaSeq

    SCREAMING_SNAKE_CASE						:     int						= ["""image"""]
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ["""text"""]






    def __init__(       self     :							Optional[Any]       ,						*__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      requires_backends(self       ,						['vision']					)
      super().__init__(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							"Image"					)     ->		Dict:
      return self.pre_processor(images=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		str:
      return self.model.generate(**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Optional[Any]:
      return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE       ,						skip_special_tokens=__SCREAMING_SNAKE_CASE					)[0].strip()




