




'''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


a		:	Union[str, Any]       =      get_logger(__name__)





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



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""splinter"""



      def __init__(		self       ,     lowercase=3_05_22       ,     lowercase=7_68       ,     lowercase=12       ,     lowercase=12       ,     lowercase=30_72       ,     lowercase="gelu"       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=2       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=True       ,     lowercase=0       ,     lowercase=1_04       ,     **lowercase       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase       ,     **lowercase)

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


