



from typing import List

import numpy as np






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					{key: len(_lowerCamelCase						) for key, value in gen_kwargs.items() if isinstance(_lowerCamelCase       ,				_lowerCamelCase						)}
							if len(set(lists_lengths.values()						)						) > 1:
														raise RuntimeError(
														    (
														        "Sharding is ambiguous for this dataset: "
														        + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
														        + "\n".join(F"\t- key {key} has length {length}" for key, length in lists_lengths.items()						)
														        + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
														        + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
														    )						)
							_lowerCAmelCase  :							Dict				     =					max(lists_lengths.values()       ,				default=0						)
							return max(1       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					[]
							for group_idx in range(_lowerCamelCase						):
														_lowerCAmelCase  :							Optional[Any]				     =					num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
														if num_shards_to_add == 0:
																					break
														_lowerCAmelCase  :							Optional[int]				     =					shards_indices_per_group[-1].stop if shards_indices_per_group else 0
														_lowerCAmelCase  :							Optional[int]				     =					range(_lowerCamelCase       ,				start + num_shards_to_add						)
														shards_indices_per_group.append(_lowerCamelCase						)
							return shards_indices_per_group






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					_number_of_shards_in_gen_kwargs(_lowerCamelCase						)
							if num_shards == 1:
														return [dict(_lowerCamelCase						)]
							else:
														_lowerCAmelCase  :							Tuple				     =					_distribute_shards(num_shards=_lowerCamelCase       ,				max_num_jobs=_lowerCamelCase						)
														return [
														    {
														        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
														        if isinstance(_lowerCamelCase       ,				_lowerCamelCase						)
														        else value
														        for key, value in gen_kwargs.items()
														    }
														    for group_idx in range(len(_lowerCamelCase						)						)
														]






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return {
							    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
							    if isinstance(gen_kwargs_list[0][key]       ,				_lowerCamelCase						)
							    else gen_kwargs_list[0][key]
							    for key in gen_kwargs_list[0]
							}






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					{len(_lowerCamelCase						) for value in gen_kwargs.values() if isinstance(_lowerCamelCase       ,				_lowerCamelCase						)}
							_lowerCAmelCase  :							List[Any]				     =					{}
							for size in list_sizes:
														_lowerCAmelCase  :							int				     =					list(range(_lowerCamelCase						)						)
														rng.shuffle(indices_per_size[size]						)
							# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
							_lowerCAmelCase  :							List[str]				     =					dict(_lowerCamelCase						)
							for key, value in shuffled_kwargs.items():
														if isinstance(_lowerCamelCase       ,				_lowerCamelCase						):
																					_lowerCAmelCase  :							Dict				     =					[value[i] for i in indices_per_size[len(_lowerCamelCase						)]]
							return shuffled_kwargs




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


_snake_case	=						logging.get_logger(__name__)

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



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'data2vec-vision'







							def __init__(     self,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.0,    __a=0.0,    __a=0.02,    __a=1E-12,    __a=224,    __a=16,    __a=3,    __a=False,    __a=False,    __a=False,    __a=False,    __a=0.1,    __a=0.1,    __a=True,    __a=[3, 5, 7, 11],    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

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

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



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

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







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4
