




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

lowercase						:   List[Any]      						=							{
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}

class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Tuple       =   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 __snake_case						:  Dict       =   "squeezebert"



 def __init__(	self ,		A=30522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.0_2 ,		A=1E-12 ,		A=0 ,		A=768 ,		A=4 ,		A=4 ,		A=4 ,		A=1 ,		A=4 ,		A=4 ,		**A ,		):






        '''simple docstring'''




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

        lowercase	:		int    =   vocab_size
        lowercase	:		List[Any]    =   hidden_size
        lowercase	:		Optional[Any]    =   num_hidden_layers
        lowercase	:		str    =   num_attention_heads
        lowercase	:		Optional[int]    =   hidden_act
        lowercase	:		List[Any]    =   intermediate_size
        lowercase	:		Any    =   hidden_dropout_prob
        lowercase	:		Union[str, Any]    =   attention_probs_dropout_prob
        lowercase	:		List[Any]    =   max_position_embeddings
        lowercase	:		List[str]    =   type_vocab_size
        lowercase	:		Tuple    =   initializer_range
        lowercase	:		List[str]    =   layer_norm_eps
        lowercase	:		Tuple    =   embedding_size
        lowercase	:		Optional[Any]    =   q_groups
        lowercase	:		Optional[Any]    =   k_groups
        lowercase	:		Union[str, Any]    =   v_groups
        lowercase	:		int    =   post_attention_groups
        lowercase	:		Optional[Any]    =   intermediate_groups
        lowercase	:		Tuple    =   output_groups






class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if self.task == "multiple-choice":
               lowercase	:		Optional[int]    =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
               lowercase	:		int    =   {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
                ('''token_type_ids''', dynamic_axis),
            ]			)










'''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   )




