


import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A__:   Any    =	get_logger(__name__)


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     List[Any]  )  ->      Union[str, Any]:
       if version.parse(_lowerCamelCase  ) < version.parse(_lowerCamelCase  ):
              if "dev" in min_version:
                     _a   :			List[Any]					 =(
                         """This example requires a source install from HuggingFace diffusers (see """
                         """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
                     )
              else:
                     _a   :			List[str]					 =F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(_lowerCamelCase  )






'''simple docstring'''







import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


A__:   List[Any]    =	logging.get_logger(__name__)

A__:   List[Any]    =	{
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Dict     	=							"vit-hybrid"






      def __init__(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Any=7_6_8					,						SCREAMING_SNAKE_CASE      :Optional[Any]=1_2					,						SCREAMING_SNAKE_CASE      :int=1_2					,						SCREAMING_SNAKE_CASE      :Any=3_0_7_2					,						SCREAMING_SNAKE_CASE      :List[str]="gelu"					,						SCREAMING_SNAKE_CASE      :str=0.0					,						SCREAMING_SNAKE_CASE      :Dict=0.0					,						SCREAMING_SNAKE_CASE      :Dict=0.02					,						SCREAMING_SNAKE_CASE      :Tuple=1e-12					,						SCREAMING_SNAKE_CASE      :List[Any]=2_2_4					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=1					,						SCREAMING_SNAKE_CASE      :List[Any]=3					,						SCREAMING_SNAKE_CASE      :Optional[Any]=[1, 1_0_2_4, 2_4, 2_4]					,						SCREAMING_SNAKE_CASE      :Optional[Any]=True					,						**SCREAMING_SNAKE_CASE      :Dict					,						)	-> List[Any]:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE     )

             if backbone_config is None:
                    logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""     )
                    _a   :			Optional[int]					 ={
                        """global_padding""": """same""",
                        """layer_type""": """bottleneck""",
                        """depths""": [3, 4, 9],
                        """out_features""": ["""stage3"""],
                        """embedding_dynamic_padding""": True,
                    }

             if isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
                    if "model_type" in backbone_config:
                           _a   :			Dict					 =CONFIG_MAPPING[backbone_config["""model_type"""]]
                    else:
                           logger.info(
                               """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""     )
                           _a   :			List[str]					 =BitConfig
                    _a   :			List[Any]					 =backbone_config_class(**SCREAMING_SNAKE_CASE     )

             _a   :			str					 =backbone_featmap_shape
             _a   :			Dict					 =backbone_config
             _a   :			Tuple					 =hidden_size
             _a   :			Union[str, Any]					 =num_hidden_layers
             _a   :			Tuple					 =num_attention_heads
             _a   :			int					 =intermediate_size
             _a   :			Any					 =hidden_act
             _a   :			Union[str, Any]					 =hidden_dropout_prob
             _a   :			Any					 =attention_probs_dropout_prob
             _a   :			Tuple					 =initializer_range
             _a   :			int					 =layer_norm_eps
             _a   :			Union[str, Any]					 =image_size
             _a   :			str					 =patch_size
             _a   :			Optional[int]					 =num_channels
             _a   :			Union[str, Any]					 =qkv_bias






      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Dict[str, any]:




             '''simple docstring'''
             _a   :			Dict					 =copy.deepcopy(self.__dict__     )
             _a   :			Tuple					 =self.backbone_config.to_dict()
             _a   :			str					 =self.__class__.model_type
             return output


