

'''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      lowerCAmelCase   (					_A      ):
       lowerCAmelCase_				=       "beit"







       def __init__(							self	:       Dict  ,					__lowercase	:       Union[str, Any]=8192  ,					__lowercase	:       str=768  ,					__lowercase	:       Optional[int]=12  ,					__lowercase	:       Union[str, Any]=12  ,					__lowercase	:       Union[str, Any]=3072  ,					__lowercase	:       str="gelu"  ,					__lowercase	:       Dict=0.0  ,					__lowercase	:       List[Any]=0.0  ,					__lowercase	:       Optional[int]=0.0_2  ,					__lowercase	:       Union[str, Any]=1E-12  ,					__lowercase	:       Any=224  ,					__lowercase	:       int=16  ,					__lowercase	:       Dict=3  ,					__lowercase	:       Tuple=False  ,					__lowercase	:       Optional[Any]=False  ,					__lowercase	:       List[str]=False  ,					__lowercase	:       List[Any]=False  ,					__lowercase	:       List[Any]=0.1  ,					__lowercase	:       List[Any]=0.1  ,					__lowercase	:       str=True  ,					__lowercase	:       Tuple=[3, 5, 7, 11]  ,					__lowercase	:       Dict=[1, 2, 3, 6]  ,					__lowercase	:       str=True  ,					__lowercase	:       Tuple=0.4  ,					__lowercase	:       List[Any]=256  ,					__lowercase	:       str=1  ,					__lowercase	:       Union[str, Any]=False  ,					__lowercase	:       List[str]=255  ,					**__lowercase	:       Optional[Any]  ,					):







            """simple docstring"""







            super().__init__(**__lowercase					)

            __lowercase							=vocab_size
            __lowercase							=hidden_size
            __lowercase							=num_hidden_layers
            __lowercase							=num_attention_heads
            __lowercase							=intermediate_size
            __lowercase							=hidden_act
            __lowercase							=hidden_dropout_prob
            __lowercase							=attention_probs_dropout_prob
            __lowercase							=initializer_range
            __lowercase							=layer_norm_eps

            __lowercase							=image_size
            __lowercase							=patch_size
            __lowercase							=num_channels
            __lowercase							=use_mask_token
            __lowercase							=use_absolute_position_embeddings
            __lowercase							=use_relative_position_bias
            __lowercase							=use_shared_relative_position_bias
            __lowercase							=layer_scale_init_value
            __lowercase							=drop_path_rate
            __lowercase							=use_mean_pooling
            # decode head attributes (semantic segmentation)
            __lowercase							=out_indices
            __lowercase							=pool_scales
            # auxiliary head attributes (semantic segmentation)
            __lowercase							=use_auxiliary_head
            __lowercase							=auxiliary_loss_weight
            __lowercase							=auxiliary_channels
            __lowercase							=auxiliary_num_convs
            __lowercase							=auxiliary_concat_input
            __lowercase							=semantic_loss_ignore_index






class      lowerCAmelCase   (					_A      ):
       lowerCAmelCase_				=       version.parse("1.11"      )







       @property
       def 	snake_case      (							self	:       int					):







            """simple docstring"""







            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]					)







       @property
       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            return 1E-4










"""simple docstring"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import torch





class 					_UpperCAmelCase       ( TensorFormatter[Mapping, "torch.Tensor", Mapping]							):




					def __init__(				self     :						Any    ,						A     :						int=None    ,						**A     :						str   )  ->  Union[str, Any]:
							super().__init__(features=A   )
							lowercase_ : Union[str, Any]							       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization




					def   A  (				self     :						Dict    ,						A     :						int   )  ->  List[Any]:
							import torch

							if isinstance(A    ,						A   ) and column:
									if all(
									    isinstance(A    ,						torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column   ):
											return torch.stack(A   )
							return column




					def   A  (				self     :						int    ,						A     :						Any   )  ->  Optional[Any]:
							import torch

							if isinstance(A    ,						(str, bytes, type(A   ))   ):
									return value
							elif isinstance(A    ,						(np.character, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.character   ):
									return value.tolist()

							lowercase_ : Any							       =   {}

							if isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.integer   ):
									lowercase_ : Any							       =   {'''dtype''': torch.intaa}
							elif isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.floating   ):
									lowercase_ : Dict							       =   {'''dtype''': torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(A    ,						PIL.Image.Image   ):
											lowercase_ : Dict							       =   np.asarray(A   )
							return torch.tensor(A    ,						**{**default_dtype, **self.torch_tensor_kwargs}   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]   )  ->  str:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(A    ,						'''__array__'''   ) and not isinstance(A    ,						torch.Tensor   ):
									lowercase_ : Optional[int]							       =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(A    ,						np.ndarray   ):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
											return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							elif isinstance(A    ,						(list, tuple)   ):
									return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							return self._tensorize(A   )




					def   A  (				self     :						Dict    ,						A     :						dict   )  ->  Tuple:
							return map_nested(self._recursive_tensorize    ,						A    ,						map_list=A   )




					def   A  (				self     :						str    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[Any]							       =   self.numpy_arrow_extractor().extract_row(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_row(A   )
							return self.recursive_tensorize(A   )




					def   A  (				self     :						List[Any]    ,						A     :						pa.Table   )  ->  "torch.Tensor":
							lowercase_ : List[str]							       =   self.numpy_arrow_extractor().extract_column(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_column(A    ,						pa_table.column_names[0]   )
							lowercase_ : Optional[int]							       =   self.recursive_tensorize(A   )
							lowercase_ : Any							       =   self._consolidate(A   )
							return column






					def   A  (				self     :						List[str]    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[int]							       =   self.numpy_arrow_extractor().extract_batch(A   )
							lowercase_ : int							       =   self.python_features_decoder.decode_batch(A   )
							lowercase_ : Dict							       =   self.recursive_tensorize(A   )
							for column_name in batch:
									lowercase_ : Optional[Any]							       =   self._consolidate(batch[column_name]   )
							return batch



