

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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Union[str, Any]												=     'convnext'



   def __init__(       self,					lowercase_=3,					lowercase_=4,					lowercase_=4,					lowercase_=None,					lowercase_=None,					lowercase_="gelu",					lowercase_=0.02,					lowercase_=1E-12,					lowercase_=1E-6,					lowercase_=0.0,					lowercase_=224,					lowercase_=None,					lowercase_=None,					**lowercase_,					)							->					List[str]:






     """simple docstring"""






     super().__init__(**lowercase_ )

     a__						=num_channels
     a__						=patch_size
     a__						=num_stages
     a__						=[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     a__						=[3, 3, 9, 3] if depths is None else depths
     a__						=hidden_act
     a__						=initializer_range
     a__						=layer_norm_eps
     a__						=layer_scale_init_value
     a__						=drop_path_rate
     a__						=image_size
     a__						=['''stem'''] + [F"""stage{idx}""" for idx in range(1,					len(self.depths ) + 1 )]
     a__,   a__						=get_aligned_output_features_output_indices(
         out_features=lowercase_,					out_indices=lowercase_,					stage_names=self.stage_names )







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Tuple												=     version.parse('1.11'							)



   @property
   def 				_UpperCAmelCase				(       self )							->					Mapping[str, Mapping[int, str]]:






     """simple docstring"""






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



   @property
   def 				_UpperCAmelCase				(       self )							->					float:






     """simple docstring"""






     return 1E-5



from dataclasses import dataclass
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
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
   lowerCamelCase__				:			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
       from .modeling_roberta_series import RobertaSeriesModelWithTransformation
       from .pipeline_alt_diffusion import AltDiffusionPipeline
       from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
