
from collections import OrderedDict
from typing import Mapping

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


A_		:     Tuple        =     logging.get_logger(__name__)

A_		:     Optional[int]        =     {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}



class 		_lowercase		(       lowercase__	):
   _UpperCAmelCase     	=			'''data2vec-text'''

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Dict=3_0522							,   __lowerCAmelCase			:       Optional[int]=768							,   __lowerCAmelCase			:       Union[str, Any]=12							,   __lowerCAmelCase			:       Dict=12							,   __lowerCAmelCase			:       str=3072							,   __lowerCAmelCase			:       Optional[int]="gelu"							,   __lowerCAmelCase			:       Any=0.1							,   __lowerCAmelCase			:       List[Any]=0.1							,   __lowerCAmelCase			:       List[Any]=512							,   __lowerCAmelCase			:       Any=2							,   __lowerCAmelCase			:       Optional[Any]=0.0_2							,   __lowerCAmelCase			:       Union[str, Any]=1E-12							,   __lowerCAmelCase			:       Tuple=1							,   __lowerCAmelCase			:       int=0							,   __lowerCAmelCase			:       Any=2							,   __lowerCAmelCase			:       List[str]="absolute"							,   __lowerCAmelCase			:       List[Any]=True							,   __lowerCAmelCase			:       Optional[int]=None							,   **__lowerCAmelCase			:       Optional[Any]							,   )       ->				List[str]:


    """simple docstring"""






    super().__init__(pad_token_id=__lowerCamelCase							,   bos_token_id=__lowerCamelCase							,   eos_token_id=__lowerCamelCase							,   **__lowerCamelCase							)

    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		type_vocab_size
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		use_cache
    a						=		classifier_dropout



class 		_lowercase		(       lowercase__	):

   @property
   def 		A							(      self			:       Union[str, Any]							)       ->				Optional[Any]:


    """simple docstring"""






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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel

