
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


__magic_name__           =					get_logger(__name__)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    if version.parse(__lowerCAmelCase							) < version.parse(__lowerCAmelCase							):
        if "dev" in min_version:
            snake_case__  =			(
                "This example requires a source install from HuggingFace diffusers (see "
                "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
            )
        else:
            snake_case__  =			F"""This example requires a minimum version of {min_version},"""
        error_message += F""" but the version found is {__version__}.\n"""
        raise ImportError(__lowerCAmelCase							)





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


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Any      		=       'swiftformer'
      def __init__(     self						,		lowerCamelCase=3						,		lowerCamelCase=[3, 3, 6, 4]						,		lowerCamelCase=[48, 56, 1_12, 2_20]						,		lowerCamelCase=4						,		lowerCamelCase=[True, True, True, True]						,		lowerCamelCase="gelu"						,		lowerCamelCase=3						,		lowerCamelCase=2						,		lowerCamelCase=1						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=1e-5						,		lowerCamelCase=1e-5						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			num_channels
          snake_case__  =			depths
          snake_case__  =			embed_dims
          snake_case__  =			mlp_ratio
          snake_case__  =			downsamples
          snake_case__  =			hidden_act
          snake_case__  =			down_patch_size
          snake_case__  =			down_stride
          snake_case__  =			down_pad
          snake_case__  =			drop_path_rate
          snake_case__  =			use_layer_scale
          snake_case__  =			layer_scale_init_value
          snake_case__  =			batch_norm_eps





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						int      		=       version.parse('1.11'	)
      @property
      def A_     (     self	):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]	)



      @property
      def A_     (     self	):
          return 1e-4



