


"""simple docstring"""




from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__SCREAMING_SNAKE_CASE      		=    logging.get_logger(__name__)





@add_end_docstrings(__lowercase   )
class __snake_case		(       __lowercase   ):







   """simple docstring"""




   def __init__(		self				:Optional[Any]					,	**UpperCamelCase__				:List[Any]							):
          super().__init__(**SCREAMING_SNAKE_CASE_							)
          requires_backends(self					,	"vision"							)
          requires_backends(self					,	"torch"							)

          if self.framework != "pt":
                 raise ValueError(f'The {self.__class__} is only available in PyTorch.'							)

          self.check_model_type(SCREAMING_SNAKE_CASE_							)




   def SCREAMING_SNAKE_CASE_   (		self				:Dict					,	**UpperCamelCase__				:Optional[Any]							):
          _a							 =					{}
          _a							 =					{}
          _a							 =					{}
          # preprocess args
          if "points_per_batch" in kwargs:
                 _a							 =					kwargs["points_per_batch"]
          if "points_per_crop" in kwargs:
                 _a							 =					kwargs["points_per_crop"]
          if "crops_n_layers" in kwargs:
                 _a							 =					kwargs["crops_n_layers"]
          if "crop_overlap_ratio" in kwargs:
                 _a							 =					kwargs["crop_overlap_ratio"]
          if "crop_n_points_downscale_factor" in kwargs:
                 _a							 =					kwargs["crop_n_points_downscale_factor"]
          # postprocess args
          if "pred_iou_thresh" in kwargs:
                 _a							 =					kwargs["pred_iou_thresh"]
          if "stability_score_offset" in kwargs:
                 _a							 =					kwargs["stability_score_offset"]
          if "mask_threshold" in kwargs:
                 _a							 =					kwargs["mask_threshold"]
          if "stability_score_thresh" in kwargs:
                 _a							 =					kwargs["stability_score_thresh"]
          if "crops_nms_thresh" in kwargs:
                 _a							 =					kwargs["crops_nms_thresh"]
          if "output_rle_mask" in kwargs:
                 _a							 =					kwargs["output_rle_mask"]
          if "output_bboxes_mask" in kwargs:
                 _a							 =					kwargs["output_bboxes_mask"]
          return preprocess_kwargs, forward_params, postprocess_kwargs




   def __call__(		self				:Dict					,	UpperCamelCase__				:int					,	*UpperCamelCase__				:Optional[Any]					,	UpperCamelCase__				:str=None					,	UpperCamelCase__				:Optional[int]=None					,	**UpperCamelCase__				:Tuple							):
          return super().__call__(SCREAMING_SNAKE_CASE_					,	*SCREAMING_SNAKE_CASE_					,	num_workers=SCREAMING_SNAKE_CASE_					,	batch_size=SCREAMING_SNAKE_CASE_					,	**SCREAMING_SNAKE_CASE_							)




   def SCREAMING_SNAKE_CASE_   (		self				:str					,	UpperCamelCase__				:Optional[Any]					,	UpperCamelCase__				:Optional[int]=64					,	UpperCamelCase__				:Dict = 0					,	UpperCamelCase__				:Union[str, Any] = 512 / 1_500					,	UpperCamelCase__				:Union[str, Any] = 32					,	UpperCamelCase__				:Dict = 1					,	):
          _a							 =					load_image(SCREAMING_SNAKE_CASE_							)
          _a							 =					self.image_processor.size["longest_edge"]
          _a ,			_a ,			_a ,			_a							 =					self.image_processor.generate_crop_boxes(
              SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_							)
          _a							 =					self.image_processor(images=SCREAMING_SNAKE_CASE_					,	return_tensors="pt"							)

          with self.device_placement():
                 if self.framework == "pt":
                        _a							 =					self.get_inference_context()
                        with inference_context():
                               _a							 =					self._ensure_tensor_on_device(SCREAMING_SNAKE_CASE_					,	device=self.device							)
                               _a							 =					self.model.get_image_embeddings(model_inputs.pop("pixel_values"							)							)
                               _a							 =					image_embeddings

          _a							 =					grid_points.shape[1]
          _a							 =					points_per_batch if points_per_batch is not None else n_points

          if points_per_batch <= 0:
                 raise ValueError(
                     "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
                     "To return all points at once, set points_per_batch to None"							)

          for i in range(0					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_							):
                 _a							 =					grid_points[:, i : i + points_per_batch, :, :]
                 _a							 =					input_labels[:, i : i + points_per_batch]
                 _a							 =					i == n_points - points_per_batch
                 yield {
                     "input_points": batched_points,
                     "input_labels": labels,
                     "input_boxes": crop_boxes,
                     "is_last": is_last,
                     **model_inputs,
                 }




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:Any=0.88					,	UpperCamelCase__				:List[str]=0.95					,	UpperCamelCase__				:Any=0					,	UpperCamelCase__				:Any=1					,	):
          _a							 =					model_inputs.pop("input_boxes"							)
          _a							 =					model_inputs.pop("is_last"							)
          _a							 =					model_inputs.pop("original_sizes"							).tolist()
          _a							 =					model_inputs.pop("reshaped_input_sizes"							).tolist()

          _a							 =					self.model(**SCREAMING_SNAKE_CASE_							)

          # post processing happens here in order to avoid CPU GPU copies of ALL the masks
          _a							 =					model_outputs["pred_masks"]
          _a							 =					self.image_processor.post_process_masks(
              SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	binarize=SCREAMING_SNAKE_CASE_							)
          _a							 =					model_outputs["iou_scores"]
          _a ,			_a ,			_a							 =					self.image_processor.filter_masks(
              masks[0]					,	iou_scores[0]					,	original_sizes[0]					,	input_boxes[0]					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	)
          return {
              "masks": masks,
              "is_last": is_last,
              "boxes": boxes,
              "iou_scores": iou_scores,
          }




   def SCREAMING_SNAKE_CASE_   (		self				:List[str]					,	UpperCamelCase__				:List[Any]					,	UpperCamelCase__				:List[Any]=False					,	UpperCamelCase__				:List[str]=False					,	UpperCamelCase__				:Optional[Any]=0.7					,	):
          _a							 =					[]
          _a							 =					[]
          _a							 =					[]
          for model_output in model_outputs:
                 all_scores.append(model_output.pop("iou_scores"							)							)
                 all_masks.extend(model_output.pop("masks"							)							)
                 all_boxes.append(model_output.pop("boxes"							)							)

          _a							 =					torch.cat(SCREAMING_SNAKE_CASE_							)
          _a							 =					torch.cat(SCREAMING_SNAKE_CASE_							)
          _a ,			_a ,			_a ,			_a							 =					self.image_processor.post_process_for_mask_generation(
              SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_							)

          _a							 =					defaultdict(SCREAMING_SNAKE_CASE_							)
          for output in model_outputs:
                 for k, v in output.items():
                        extra[k].append(SCREAMING_SNAKE_CASE_							)

          _a							 =					{}
          if output_rle_mask:
                 _a							 =					rle_mask

          if output_bboxes_mask:
                 _a							 =					bounding_boxes

          return {"masks": output_masks, "scores": iou_scores, **optional, **extra}


"""simple docstring"""




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__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		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 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			to_pil_image(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			pil_image.size
		SCREAMING_SNAKE_CASE_    			=			pytesseract.image_to_data(__a,				lang=__a,				output_type='''dict''',				config=__a     )
		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(__a     ) if not word.strip()]
		SCREAMING_SNAKE_CASE_    			=			[word for idx, word in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) 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(__a,				__a,				__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			[x, y, x + w, y + h]
				actual_boxes.append(__a     )

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

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

		return words, normalized_boxes





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''pixel_values''']

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = 1 / 2_55							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ""							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )

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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			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()}'     )
									SCREAMING_SNAKE_CASE_    			=			(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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else self.image_std
									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(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.'''     )

									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.
									SCREAMING_SNAKE_CASE_    			=			[to_numpy_array(SCREAMING_SNAKE_CASE_     ) for image in images]

									# Tesseract OCR to get words + normalized bounding boxes
									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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
													words_batch.append(SCREAMING_SNAKE_CASE_     )
													boxes_batch.append(SCREAMING_SNAKE_CASE_     )

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

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

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

									SCREAMING_SNAKE_CASE_    			=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) for image in images]

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

									if apply_ocr:
											SCREAMING_SNAKE_CASE_    			=			words_batch
											SCREAMING_SNAKE_CASE_    			=			boxes_batch
									return data