




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


__lowerCAmelCase		:				Dict       			=		{
    '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
}

__lowerCAmelCase		:				Optional[int]       			=		logging.get_logger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					"""maskformer"""
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					{"""hidden_size""": """mask_feature_size"""}
				SCREAMING_SNAKE_CASE_       :							int							 =					["""resnet""", """swin"""]
				SCREAMING_SNAKE_CASE_       :							Dict							 =					["""detr"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     int = 2_56					,      __lowerCamelCase				:     int = 2_56					,      __lowerCamelCase				:     float = 0.1					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     Optional[Dict] = None					,      __lowerCamelCase				:     Optional[Dict] = None					,      __lowerCamelCase				:     float = 0.02					,      __lowerCamelCase				:     float = 1.0					,      __lowerCamelCase				:     float = 1.0					,      __lowerCamelCase				:     float = 1.0					,      __lowerCamelCase				:     float = 20.0					,      __lowerCamelCase				:     Optional[bool] = None					,      **__lowerCamelCase				:     int					,      )					->     Optional[int]:
							if backbone_config is None:
										# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
										a    =			SwinConfig(
										    image_size=3_84					,      in_channels=3					,      patch_size=4					,      embed_dim=1_28					,      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(__lowerCamelCase					,      __lowerCamelCase							):
										a    =			backbone_config.pop("model_type"							)
										a    =			CONFIG_MAPPING[backbone_model_type]
										a    =			config_class.from_dict(__lowerCamelCase							)

							# 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
										a    =			DetrConfig()
							else:
										# verify that the decoder is supported
										a    =			(
										    decoder_config.pop("model_type"							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) 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(__lowerCamelCase					,      __lowerCamelCase							):
													a    =			CONFIG_MAPPING[decoder_type]
													a    =			config_class.from_dict(__lowerCamelCase							)

							a    =			backbone_config
							a    =			decoder_config
							# main feature dimension for the model
							a    =			fpn_feature_size
							a    =			mask_feature_size
							# initializer
							a    =			init_std
							a    =			init_xavier_std
							# Hungarian matcher && loss
							a    =			cross_entropy_weight
							a    =			dice_weight
							a    =			mask_weight
							a    =			use_auxiliary_loss
							a    =			no_object_weight
							a    =			output_auxiliary_logits

							a    =			self.decoder_config.encoder_attention_heads
							a    =			self.decoder_config.num_hidden_layers
							super().__init__(**__lowerCamelCase							)





				@classmethod
				def 							__UpperCAmelCase							(      cls				:     Optional[Any]					,      __lowerCamelCase				:     PretrainedConfig					,      __lowerCamelCase				:     PretrainedConfig					,      **__lowerCamelCase				:     Tuple							)					->     List[str]:
							return cls(
							    backbone_config=__lowerCamelCase					,      decoder_config=__lowerCamelCase					,      **__lowerCamelCase					,      )





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict[str, any]:
							a    =			copy.deepcopy(self.__dict__							)
							a    =			self.backbone_config.to_dict()
							a    =			self.decoder_config.to_dict()
							a    =			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

__lowerCAmelCase		:				List[str]       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Dict,  A		:					int,  A		:					Optional[int]   ):






			'''simple docstring'''







			return [
			    int(1000 * (box[0] / width)   ),
			    int(1000 * (box[1] / height)   ),
			    int(1000 * (box[2] / width)   ),
			    int(1000 * (box[3] / height)   ),
			]




def 			__magic_name__				( A		:					np.ndarray,  A		:					Optional[str],  A		:					Optional[str] = None   ):






			'''simple docstring'''







			a    =			tesseract_config if tesseract_config is not None else ""

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

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

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

			# finally, normalize the bounding boxes
			a    =			[]
			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__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					["""pixel_values"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = ""					,      **__lowerCamelCase				:     Tuple					,      )					->     None:
							super().__init__(**__lowerCamelCase							)
							a    =			size if size is not None else {"height": 2_24, "width": 2_24}
							a    =			get_size_dict(__lowerCamelCase							)

							a    =			do_resize
							a    =			size
							a    =			resample
							a    =			apply_ocr
							a    =			ocr_lang
							a    =			tesseract_config





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     np.ndarray					,      __lowerCamelCase				:     Dict[str, int]					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     np.ndarray:
							a    =			get_size_dict(__lowerCamelCase							)
							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    =			(size["height"], size["width"])
							return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     ImageInput					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = None					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase				:     ChannelDimension = ChannelDimension.FIRST					,      **__lowerCamelCase				:     Optional[Any]					,      )					->     PIL.Image.Image:
							a    =			do_resize if do_resize is not None else self.do_resize
							a    =			size if size is not None else self.size
							a    =			get_size_dict(__lowerCamelCase							)
							a    =			resample if resample is not None else self.resample
							a    =			apply_ocr if apply_ocr is not None else self.apply_ocr
							a    =			ocr_lang if ocr_lang is not None else self.ocr_lang
							a    =			tesseract_config if tesseract_config is not None else self.tesseract_config

							a    =			make_list_of_images(__lowerCamelCase							)

							if not valid_images(__lowerCamelCase							):
										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.
							a    =			[to_numpy_array(__lowerCamelCase							) for image in images]

							if apply_ocr:
										requires_backends(self					,      "pytesseract"							)
										a    =			[]
										a    =			[]
										for image in images:
													a    ,		a    =			apply_tesseract(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
													words_batch.append(__lowerCamelCase							)
													boxes_batch.append(__lowerCamelCase							)

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

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

							a    =			BatchFeature(data={"pixel_values": images}					,      tensor_type=__lowerCamelCase							)

							if apply_ocr:
										a    =			words_batch
										a    =			boxes_batch
							return data






