
'''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


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  '''data2vec-vision'''




						def __init__(   self       ,  _lowercase=768       ,  _lowercase=12       ,  _lowercase=12       ,  _lowercase=3_072       ,  _lowercase="gelu"       ,  _lowercase=0.0       ,  _lowercase=0.0       ,  _lowercase=0.02       ,  _lowercase=1e-12       ,  _lowercase=224       ,  _lowercase=16       ,  _lowercase=3       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=True       ,  _lowercase=[3, 5, 7, 11]       ,  _lowercase=[1, 2, 3, 6]       ,  _lowercase=True       ,  _lowercase=0.4       ,  _lowercase=256       ,  _lowercase=1       ,  _lowercase=False       ,  _lowercase=255       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(**_lowercase )

												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					layer_norm_eps

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






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Union[str, Any]										=  version.parse('''1.11'''					)




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 1e-4






'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A_			      =		logging.get_logger(__name__)






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					float     ,							**__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   int	      =    feature_size
           snake_case__       :   List[str]	      =    sampling_rate
           snake_case__       :   Any	      =    padding_value

           snake_case__       :   Union[str, Any]	      =    kwargs.pop('padding_side'     ,							'right'			)
           snake_case__       :   List[Any]	      =    kwargs.pop('return_attention_mask'     ,							__lowerCamelCase			)

           super().__init__(**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ]     ,							__lowerCamelCase     :					Union[bool, str, PaddingStrategy] = True     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					bool = False     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[bool] = None     ,							__lowerCamelCase     :					Optional[Union[str, TensorType]] = None     ,							):
           # If we have a list of dicts, let's convert it in a dict of lists
           # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
           if isinstance(__lowerCamelCase     ,							(list, tuple)			) and isinstance(processed_features[0]     ,							(dict, BatchFeature)			):
                 snake_case__       :   int	      =    {
                     key: [example[key] for example in processed_features] for key in processed_features[0].keys()
                 }

           # The model's main input name, usually `input_values`, has be passed for padding
           if self.model_input_names[0] not in processed_features:
                 raise ValueError(
                     'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                     F" to this method that includes {self.model_input_names[0]}, but you provided"
                     F" {list(processed_features.keys()			)}"			)

           snake_case__       :   Tuple	      =    processed_features[self.model_input_names[0]]
           snake_case__       :   Union[str, Any]	      =    (
               return_attention_mask if return_attention_mask is not None else self.return_attention_mask
           )

           if len(__lowerCamelCase			) == 0:
                 if return_attention_mask:
                       snake_case__       :   str	      =    []
                 return processed_features

           # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
           # and rebuild them afterwards if no return_tensors is specified
           # Note that we lose the specific device the tensor may be on for PyTorch

           snake_case__       :   Dict	      =    required_input[0]
           if isinstance(__lowerCamelCase     ,							(list, tuple)			):
                 # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
                 snake_case__       :   List[Any]	      =    0
                 while len(required_input[index]			) == 0:
                       index += 1
                 if index < len(__lowerCamelCase			):
                       snake_case__       :   List[Any]	      =    required_input[index][0]

           if return_tensors is None:
                 if is_tf_tensor(__lowerCamelCase			):
                       snake_case__       :   int	      =    'tf'
                 elif is_torch_tensor(__lowerCamelCase			):
                       snake_case__       :   str	      =    'pt'
                 elif isinstance(__lowerCamelCase     ,							(int, float, list, tuple, np.ndarray)			):
                       snake_case__       :   Any	      =    'np'
                 else:
                       raise ValueError(
                           F"type of {first_element} unknown: {type(__lowerCamelCase			)}. "
                           'Should be one of a python, numpy, pytorch or tensorflow object.'			)

           for key, value in processed_features.items():
                 if isinstance(value[0]     ,							(int, float)			):
                       snake_case__       :   List[Any]	      =    to_numpy(__lowerCamelCase			)
                 else:
                       snake_case__       :   List[str]	      =    [to_numpy(__lowerCamelCase			) for v in value]

        # Convert padding_strategy in PaddingStrategy
           snake_case__       :   str	      =    self._get_padding_strategies(padding=__lowerCamelCase     ,							max_length=__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    processed_features[self.model_input_names[0]]

           snake_case__       :   Tuple	      =    len(__lowerCamelCase			)
           if not all(len(__lowerCamelCase			) == batch_size for v in processed_features.values()			):
                 raise ValueError('Some items in the output dictionary have a different batch size than others.'			)

           snake_case__       :   str	      =    []
           for i in range(__lowerCamelCase			):
                 snake_case__       :   str	      =    {k: v[i] for k, v in processed_features.items()}
                 # truncation
                 snake_case__       :   Optional[Any]	      =    self._truncate(
                     __lowerCamelCase     ,							max_length=__lowerCamelCase     ,							pad_to_multiple_of=__lowerCamelCase     ,							truncation=__lowerCamelCase     ,							)
                 truncated_inputs.append(__lowerCamelCase			)

           if padding_strategy == PaddingStrategy.LONGEST:
                 # make sure that `max_length` cannot be longer than the longest truncated length
                 snake_case__       :   Any	      =    max(len(input_slice[self.model_input_names[0]]			) for input_slice in truncated_inputs			)
                 snake_case__       :   Union[str, Any]	      =    PaddingStrategy.MAX_LENGTH

           snake_case__       :   List[Any]	      =    {}
           for i in range(__lowerCamelCase			):
                 # padding
                 snake_case__       :   Tuple	      =    self._pad(
                     truncated_inputs[i]     ,							max_length=__lowerCamelCase     ,							padding_strategy=__lowerCamelCase     ,							pad_to_multiple_of=__lowerCamelCase     ,							return_attention_mask=__lowerCamelCase     ,							)

                 for key, value in outputs.items():
                       if key not in batch_outputs:
                             snake_case__       :   Union[str, Any]	      =    []
                       if value.dtype is np.dtype(np.floataa			):
                             snake_case__       :   int	      =    value.astype(np.floataa			)
                       batch_outputs[key].append(__lowerCamelCase			)

           return BatchFeature(__lowerCamelCase     ,							tensor_type=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]     ,							__lowerCamelCase     :					Union[Dict[str, np.ndarray], BatchFeature]     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[bool] = None     ,							):
           snake_case__       :   Optional[int]	      =    processed_features[self.model_input_names[0]]

           if padding_strategy == PaddingStrategy.LONGEST:
                 snake_case__       :   Tuple	      =    len(__lowerCamelCase			)

           if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                 snake_case__       :   Dict	      =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           snake_case__       :   List[str]	      =    padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowerCamelCase			) < max_length

           if return_attention_mask and "attention_mask" not in processed_features:
                 snake_case__       :   Optional[int]	      =    np.ones(len(__lowerCamelCase			)     ,							dtype=np.intaa			)

           if needs_to_be_padded:
                 snake_case__       :   List[Any]	      =    max_length - len(__lowerCamelCase			)
                 if self.padding_side == "right":
                       if return_attention_mask:
                             snake_case__       :   List[str]	      =    np.pad(
                                 processed_features['attention_mask']     ,							(0, difference)			)
                       snake_case__       :   List[Any]	      =    ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                       snake_case__       :   int	      =    np.pad(
                           __lowerCamelCase     ,							__lowerCamelCase     ,							'constant'     ,							constant_values=self.padding_value			)
                 elif self.padding_side == "left":
                       if return_attention_mask:
                             snake_case__       :   str	      =    np.pad(
                                 processed_features['attention_mask']     ,							(difference, 0)			)
                       snake_case__       :   int	      =    ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                       snake_case__       :   int	      =    np.pad(
                           __lowerCamelCase     ,							__lowerCamelCase     ,							'constant'     ,							constant_values=self.padding_value			)
                 else:
                       raise ValueError('Invalid padding strategy:' + str(self.padding_side			)			)

           return processed_features





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Union[Dict[str, np.ndarray], BatchFeature]     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[int] = None     ,							__lowerCamelCase     :					Optional[bool] = None     ,							):
           if not truncation:
                 return processed_features
           elif truncation and max_length is None:
                 raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'			)

           snake_case__       :   Optional[int]	      =    processed_features[self.model_input_names[0]]

           # find `max_length` that fits `pad_to_multiple_of`
           if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                 snake_case__       :   List[str]	      =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           snake_case__       :   str	      =    len(__lowerCamelCase			) > max_length

           if needs_to_be_truncated:
                 snake_case__       :   Optional[int]	      =    processed_features[self.model_input_names[0]][:max_length]
                 if "attention_mask" in processed_features:
                       snake_case__       :   List[Any]	      =    processed_features['attention_mask'][:max_length]

           return processed_features





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Optional[int]=False     ,							__lowerCamelCase     :					Optional[Any]=None			):

           # Get padding strategy
           if padding is not False:
                 if padding is True:
                       snake_case__       :   int	      =    PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                 elif not isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                       snake_case__       :   str	      =    PaddingStrategy(__lowerCamelCase			)
                 elif isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                       snake_case__       :   str	      =    padding
           else:
                 snake_case__       :   Union[str, Any]	      =    PaddingStrategy.DO_NOT_PAD

           # Set max length if needed
           if max_length is None:
                 if padding_strategy == PaddingStrategy.MAX_LENGTH:
                       raise ValueError(
                           F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"			)

        # Test if we have a padding value
           if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
                 raise ValueError(
                     'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                     ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'			)

           return padding_strategy
