



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      """qdqbert"""





     def __init__(							self			:				str  ,	UpperCamelCase			:				Union[str, Any]=3_0_5_2_2  ,	UpperCamelCase			:				str=7_6_8  ,	UpperCamelCase			:				int=1_2  ,	UpperCamelCase			:				List[Any]=1_2  ,	UpperCamelCase			:				str=3_0_7_2  ,	UpperCamelCase			:				Optional[int]="gelu"  ,	UpperCamelCase			:				Optional[int]=0.1  ,	UpperCamelCase			:				int=0.1  ,	UpperCamelCase			:				Tuple=5_1_2  ,	UpperCamelCase			:				str=2  ,	UpperCamelCase			:				Optional[Any]=0.0_2  ,	UpperCamelCase			:				List[str]=1E-12  ,	UpperCamelCase			:				Dict=True  ,	UpperCamelCase			:				Optional[int]=1  ,	UpperCamelCase			:				Any=0  ,	UpperCamelCase			:				Union[str, Any]=2  ,	**UpperCamelCase			:				Tuple  ,	)->Tuple:
      super().__init__(pad_token_id=UpperCamelCase  ,	bos_token_id=UpperCamelCase  ,	eos_token_id=UpperCamelCase  ,	**UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						vocab_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						max_position_embeddings
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						hidden_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						num_hidden_layers
      __SCREAMING_SNAKE_CASE							:     int							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     Tuple							=						intermediate_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     int							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Tuple							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Tuple							=						type_vocab_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						layer_norm_eps
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						use_cache












'''simple docstring'''






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


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		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   )




