

'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'beit'







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Any=8192		,					UpperCAmelCase__     :		Optional[int]=768		,					UpperCAmelCase__     :		Union[str, Any]=12		,					UpperCAmelCase__     :		Optional[Any]=12		,					UpperCAmelCase__     :		str=3072		,					UpperCAmelCase__     :		Dict="gelu"		,					UpperCAmelCase__     :		Dict=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		int=0.02		,					UpperCAmelCase__     :		str=1E-12		,					UpperCAmelCase__     :		str=224		,					UpperCAmelCase__     :		Optional[Any]=16		,					UpperCAmelCase__     :		int=3		,					UpperCAmelCase__     :		Dict=False		,					UpperCAmelCase__     :		List[str]=False		,					UpperCAmelCase__     :		int=False		,					UpperCAmelCase__     :		int=False		,					UpperCAmelCase__     :		Dict=0.1		,					UpperCAmelCase__     :		Optional[Any]=0.1		,					UpperCAmelCase__     :		Any=True		,					UpperCAmelCase__     :		List[Any]=[3, 5, 7, 11]		,					UpperCAmelCase__     :		List[str]=[1, 2, 3, 6]		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		Union[str, Any]=0.4		,					UpperCAmelCase__     :		List[Any]=256		,					UpperCAmelCase__     :		List[Any]=1		,					UpperCAmelCase__     :		Union[str, Any]=False		,					UpperCAmelCase__     :		Optional[int]=255		,					**UpperCAmelCase__     :		Tuple		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							List[str]    		=vocab_size
										lowercase :							str    		=hidden_size
										lowercase :							Any    		=num_hidden_layers
										lowercase :							List[str]    		=num_attention_heads
										lowercase :							List[Any]    		=intermediate_size
										lowercase :							Optional[int]    		=hidden_act
										lowercase :							Optional[int]    		=hidden_dropout_prob
										lowercase :							Any    		=attention_probs_dropout_prob
										lowercase :							List[str]    		=initializer_range
										lowercase :							int    		=layer_norm_eps

										lowercase :							str    		=image_size
										lowercase :							Union[str, Any]    		=patch_size
										lowercase :							Tuple    		=num_channels
										lowercase :							List[Any]    		=use_mask_token
										lowercase :							Optional[int]    		=use_absolute_position_embeddings
										lowercase :							Any    		=use_relative_position_bias
										lowercase :							int    		=use_shared_relative_position_bias
										lowercase :							str    		=layer_scale_init_value
										lowercase :							int    		=drop_path_rate
										lowercase :							Optional[int]    		=use_mean_pooling
										# decode head attributes (semantic segmentation)
										lowercase :							Dict    		=out_indices
										lowercase :							int    		=pool_scales
										# auxiliary head attributes (semantic segmentation)
										lowercase :							Optional[Any]    		=use_auxiliary_head
										lowercase :							Optional[int]    		=auxiliary_loss_weight
										lowercase :							Optional[int]    		=auxiliary_channels
										lowercase :							Any    		=auxiliary_num_convs
										lowercase :							Tuple    		=auxiliary_concat_input
										lowercase :							List[Any]    		=semantic_loss_ignore_index





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    version.parse('1.11'      )







				@property
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]   )







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return 1E-4







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 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_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

   return pad(SCREAMING_SNAKE_CASE_       ,	((0, pad_height), (0, pad_width))       ,	mode="""symmetric"""       ,	data_format=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_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

   lowercase__	: str     		=   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_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

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

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






