



"""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 SCREAMING_SNAKE_CASE__					(		lowercase_      ):
						_lowerCAmelCase							=					'''data2vec-vision'''



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _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							)

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

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







class SCREAMING_SNAKE_CASE__					(		lowercase_      ):
						_lowerCAmelCase							=					version.parse("1.11"      )



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 1e-4






"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

