





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_SCREAMING_SNAKE_CASE      	=		{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			List[Any]       =       "maskformer"
						__magic_name__:			Optional[Any]       =       {"hidden_size": "mask_feature_size"}
						__magic_name__:			Optional[int]       =       ["resnet", "swin"]
						__magic_name__:			List[Any]       =       ["detr"]






						def __init__(      self      :      Any		,   _A      :      int = 256		,   _A      :      int = 256		,   _A      :      float = 0.1		,   _A      :      bool = False		,   _A      :      Optional[Dict] = None		,   _A      :      Optional[Dict] = None		,   _A      :      float = 0.0_2		,   _A      :      float = 1.0		,   _A      :      float = 1.0		,   _A      :      float = 1.0		,   _A      :      float = 2_0.0		,   _A      :      Optional[bool] = None		,   **_A      :      Optional[Any]		,   )							->    Any:







							"""simple docstring"""


							if backbone_config is None:
								# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
								snake_case_		:			Union[str, Any]											=	SwinConfig(
								    image_size=384		,   in_channels=3		,   patch_size=4		,   embed_dim=128		,   depths=[2, 2, 18, 2]		,   num_heads=[4, 8, 16, 32]		,   window_size=12		,   drop_path_rate=0.3		,   out_features=['stage1', 'stage2', 'stage3', 'stage4']		,   )

							if isinstance(_A		,   _A	):
								snake_case_		:			Optional[int]											=	backbone_config.pop('model_type'	)
								snake_case_		:			Tuple											=	CONFIG_MAPPING[backbone_model_type]
								snake_case_		:			Any											=	config_class.from_dict(_A	)

							# verify that the backbone is supported
							if backbone_config.model_type not in self.backbones_supported:
								logger.warning_once(
								    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
								    F"""Supported model types: {','.join(self.backbones_supported	)}"""	)

							if decoder_config is None:
								# fall back to https://huggingface.co/facebook/detr-resnet-50
								snake_case_		:			Optional[Any]											=	DetrConfig()
							else:
								# verify that the decoder is supported
								snake_case_		:			List[str]											=	(
								    decoder_config.pop('model_type'	) if isinstance(_A		,   _A	) else decoder_config.model_type
								)
								if decoder_type not in self.decoders_supported:
									raise ValueError(
									    F"""Transformer Decoder {decoder_type} not supported, please use one of"""
									    F""" {','.join(self.decoders_supported	)}"""	)
								if isinstance(_A		,   _A	):
									snake_case_		:			Optional[int]											=	CONFIG_MAPPING[decoder_type]
									snake_case_		:			Optional[int]											=	config_class.from_dict(_A	)

							snake_case_		:			List[Any]											=	backbone_config
							snake_case_		:			Optional[Any]											=	decoder_config
							# main feature dimension for the model
							snake_case_		:			List[Any]											=	fpn_feature_size
							snake_case_		:			Optional[Any]											=	mask_feature_size
							# initializer
							snake_case_		:			Union[str, Any]											=	init_std
							snake_case_		:			int											=	init_xavier_std
							# Hungarian matcher && loss
							snake_case_		:			List[str]											=	cross_entropy_weight
							snake_case_		:			str											=	dice_weight
							snake_case_		:			int											=	mask_weight
							snake_case_		:			List[Any]											=	use_auxiliary_loss
							snake_case_		:			Tuple											=	no_object_weight
							snake_case_		:			List[str]											=	output_auxiliary_logits

							snake_case_		:			Dict											=	self.decoder_config.encoder_attention_heads
							snake_case_		:			int											=	self.decoder_config.num_hidden_layers
							super().__init__(**_A	)






						@classmethod
						def UpperCAmelCase_				(      cls      :      Optional[int]		,   _A      :      PretrainedConfig		,   _A      :      PretrainedConfig		,   **_A      :      List[str]	)							->    Union[str, Any]:







							"""simple docstring"""


							return cls(
							    backbone_config=_A		,   decoder_config=_A		,   **_A		,   )






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Dict[str, any]:







							"""simple docstring"""


							snake_case_		:			Any											=	copy.deepcopy(self.__dict__	)
							snake_case_		:			Union[str, Any]											=	self.backbone_config.to_dict()
							snake_case_		:			Union[str, Any]											=	self.decoder_config.to_dict()
							snake_case_		:			Dict											=	self.__class__.model_type
							return output








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      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	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__						(    __a   , __a   , __a = None   ):
	snake_case_		:			int											=	tesseract_config if tesseract_config is not None else ''

	# apply OCR
	snake_case_		:			str											=	to_pil_image(__a   )
	snake_case_						,snake_case_		:			str											=	pil_image.size
	snake_case_		:			str											=	pytesseract.image_to_data(__a   , lang=__a   , output_type='dict'   , config=__a   )
	snake_case_						,snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Dict											=	data['text'], data['left'], data['top'], data['width'], data['height']

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

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

	# finally, normalize the bounding boxes
	snake_case_		:			Dict											=	[]
	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 SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       ["pixel_values"]






						def __init__(      self      :      List[str]		,   _A      :      bool = True		,   _A      :      Dict[str, int] = None		,   _A      :      PILImageResampling = PILImageResampling.BILINEAR		,   _A      :      bool = True		,   _A      :      Optional[str] = None		,   _A      :      Optional[str] = ""		,   **_A      :      int		,   )							->    None:







							"""simple docstring"""


							super().__init__(**_A	)
							snake_case_		:			Optional[int]											=	size if size is not None else {'height': 224, 'width': 224}
							snake_case_		:			List[str]											=	get_size_dict(_A	)

							snake_case_		:			int											=	do_resize
							snake_case_		:			int											=	size
							snake_case_		:			Any											=	resample
							snake_case_		:			List[str]											=	apply_ocr
							snake_case_		:			str											=	ocr_lang
							snake_case_		:			Dict											=	tesseract_config






						def UpperCAmelCase_				(      self      :      int		,   _A      :      np.ndarray		,   _A      :      Dict[str, int]		,   _A      :      PILImageResampling = PILImageResampling.BILINEAR		,   _A      :      Optional[Union[str, ChannelDimension]] = None		,   **_A      :      str		,   )							->    np.ndarray:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	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()}"""	)
							snake_case_		:			Optional[Any]											=	(size['height'], size['width'])
							return resize(_A		,   size=_A		,   resample=_A		,   data_format=_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      ImageInput		,   _A      :      bool = None		,   _A      :      Dict[str, int] = None		,   _A      :      PILImageResampling = None		,   _A      :      bool = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[Union[str, TensorType]] = None		,   _A      :      ChannelDimension = ChannelDimension.FIRST		,   **_A      :      List[str]		,   )							->    PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:			Dict											=	do_resize if do_resize is not None else self.do_resize
							snake_case_		:			Optional[Any]											=	size if size is not None else self.size
							snake_case_		:			Union[str, Any]											=	get_size_dict(_A	)
							snake_case_		:			Dict											=	resample if resample is not None else self.resample
							snake_case_		:			Tuple											=	apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case_		:			int											=	ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case_		:			int											=	tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case_		:			str											=	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.
							snake_case_		:			List[str]											=	[to_numpy_array(_A	) for image in images]

							if apply_ocr:
								requires_backends(self		,   'pytesseract'	)
								snake_case_		:			str											=	[]
								snake_case_		:			List[Any]											=	[]
								for image in images:
									snake_case_						,snake_case_		:			Tuple											=	apply_tesseract(_A		,   _A		,   _A	)
									words_batch.append(_A	)
									boxes_batch.append(_A	)

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

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

							snake_case_		:			List[str]											=	BatchFeature(data={'pixel_values': images}		,   tensor_type=_A	)

							if apply_ocr:
								snake_case_		:			Dict											=	words_batch
								snake_case_		:			List[str]											=	boxes_batch
							return data

