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


_lowercase:	Any       			=		{
    '''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
}

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



class        lowerCamelCase__				(   A__				):
			UpperCamelCase__    							="""maskformer"""
			UpperCamelCase__    							={"""hidden_size""": """mask_feature_size"""}
			UpperCamelCase__    							=["""resnet""", """swin"""]
			UpperCamelCase__    							=["""detr"""]





			def __init__(       self	:	List[str]  ,  lowercase__	:	Optional[int] = 2_56  ,  lowercase__	:	List[str] = 2_56  ,  lowercase__	:	Any = 0.1  ,  lowercase__	:	Dict = False  ,  lowercase__	:	str = None  ,  lowercase__	:	Any = None  ,  lowercase__	:	Union[str, Any] = 0.0_2  ,  lowercase__	:	Tuple = 1.0  ,  lowercase__	:	List[str] = 1.0  ,  lowercase__	:	Tuple = 1.0  ,  lowercase__	:	Tuple = 2_0.0  ,  lowercase__	:	Dict = None  ,  **lowercase__	:	Optional[int]  ,  ):
										if backbone_config is None:
																	# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																	_lowerCAmelCase							=							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(_UpperCamelCase  ,  _UpperCamelCase						):
																	_lowerCAmelCase							=							backbone_config.pop('model_type'						)
																	_lowerCAmelCase							=							CONFIG_MAPPING[backbone_model_type]
																	_lowerCAmelCase							=							config_class.from_dict(_UpperCamelCase						)

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

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

										_lowerCAmelCase							=							self.decoder_config.encoder_attention_heads
										_lowerCAmelCase							=							self.decoder_config.num_hidden_layers
										super().__init__(**_UpperCamelCase						)





			@classmethod
			def   SCREAMING_SNAKE_CASE__     (       cls	:	Tuple  ,  lowercase__	:	List[str]  ,  lowercase__	:	Union[str, Any]  ,  **lowercase__	:	List[Any]						):
										return cls(
										    backbone_config=_UpperCamelCase  ,  decoder_config=_UpperCamelCase  ,  **_UpperCamelCase  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							copy.deepcopy(self.__dict__						)
										_lowerCAmelCase							=							self.backbone_config.to_dict()
										_lowerCAmelCase							=							self.decoder_config.to_dict()
										_lowerCAmelCase							=							self.__class__.model_type
										return output













from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
        return center_crop(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        if not valid_images(_UpperCamelCase					):
           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_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

        if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

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

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

        if do_center_crop:
           _UpperCAmelCase           =   [self.center_crop(image=_UpperCamelCase				,							size=_UpperCamelCase					) for image in images]

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

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

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)