from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
     import PIL

if is_torch_available():
     import torch


lowerCAmelCase__	   =						logging.get_logger(__name__)
class 					snake_case	(     A__   ):

   """simple docstring"""







   __lowerCAmelCase   						=      ["""pixel_values"""]





   def __init__(    self					,	lowerCAmelCase_ = True					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = PILImageResampling.BILINEAR					,	lowerCAmelCase_ = True					,	lowerCAmelCase_ = 1 / 255					,	lowerCAmelCase_ = True					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = True					,	**lowerCAmelCase_					,	):
     super().__init__(**__snake_case	)
     __lowercase					=		size if size is not None else {"shortest_edge": 224}
     __lowercase					=		get_size_dict(__snake_case					,	default_to_square=__snake_case	)
     __lowercase					=		crop_size if crop_size is not None else {"height": 256, "width": 256}
     __lowercase					=		get_size_dict(__snake_case					,	param_name="crop_size"	)

     __lowercase					=		do_resize
     __lowercase					=		size
     __lowercase					=		resample
     __lowercase					=		do_rescale
     __lowercase					=		rescale_factor
     __lowercase					=		do_center_crop
     __lowercase					=		crop_size
     __lowercase					=		do_flip_channel_order





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_ = PIL.Image.BILINEAR					,	lowerCAmelCase_ = None					,	**lowerCAmelCase_					,	):
     __lowercase					=		get_size_dict(__snake_case					,	default_to_square=__snake_case	)
     if "shortest_edge" not in size:
       raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''	)
     __lowercase					=		get_resize_output_image_size(__snake_case					,	size=size["shortest_edge"]					,	default_to_square=__snake_case	)
     return resize(__snake_case					,	size=__snake_case					,	resample=__snake_case					,	data_format=__snake_case					,	**__snake_case	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_ = None					,	**lowerCAmelCase_					,	):
     __lowercase					=		get_size_dict(__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()}'''	)
     return center_crop(__snake_case					,	size=(size["height"], size["width"])					,	data_format=__snake_case					,	**__snake_case	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_ = None					,	**lowerCAmelCase_					,	):
     return rescale(__snake_case					,	scale=__snake_case					,	data_format=__snake_case					,	**__snake_case	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_ = None	):
     return flip_channel_order(__snake_case					,	data_format=__snake_case	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = None					,	lowerCAmelCase_ = ChannelDimension.FIRST					,	**lowerCAmelCase_					,	):
     __lowercase					=		do_resize if do_resize is not None else self.do_resize
     __lowercase					=		resample if resample is not None else self.resample
     __lowercase					=		do_rescale if do_rescale is not None else self.do_rescale
     __lowercase					=		rescale_factor if rescale_factor is not None else self.rescale_factor
     __lowercase					=		do_center_crop if do_center_crop is not None else self.do_center_crop
     __lowercase					=		(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     __lowercase					=		size if size is not None else self.size
     __lowercase					=		get_size_dict(__snake_case					,	default_to_square=__snake_case	)
     __lowercase					=		crop_size if crop_size is not None else self.crop_size
     __lowercase					=		get_size_dict(__snake_case					,	param_name="crop_size"	)

     __lowercase					=		make_list_of_images(__snake_case	)

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

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

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

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

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

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       __lowercase					=		[self.flip_channel_order(image=__snake_case	) for image in images]

     __lowercase					=		[to_channel_dimension_format(__snake_case					,	__snake_case	) for image in images]

     __lowercase					=		{"pixel_values": images}
     return BatchFeature(data=__snake_case					,	tensor_type=__snake_case	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_ = None	):
     __lowercase					=		outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(__snake_case	) != len(__snake_case	):
         raise ValueError(
             "Make sure that you pass in as many target sizes as the batch dimension of the logits"	)

       if is_torch_tensor(__snake_case	):
         __lowercase					=		target_sizes.numpy()

       __lowercase					=		[]

       for idx in range(len(__snake_case	)	):
         __lowercase					=		torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0	)					,	size=target_sizes[idx]					,	mode="bilinear"					,	align_corners=__snake_case	)
         __lowercase					=		resized_logits[0].argmax(dim=0	)
         semantic_segmentation.append(__snake_case	)
     else:
       __lowercase					=		logits.argmax(dim=1	)
       __lowercase					=		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation








'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

