
"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__          = {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase__          = ['CLIPTokenizerFast']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase__          = ['CLIPFeatureExtractor']
 UpperCAmelCase__          = ['CLIPImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase__          = [
     'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CLIPModel',
     'CLIPPreTrainedModel',
     'CLIPTextModel',
     'CLIPTextModelWithProjection',
     'CLIPVisionModel',
     'CLIPVisionModelWithProjection',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase__          = [
     'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCLIPModel',
     'TFCLIPPreTrainedModel',
     'TFCLIPTextModel',
     'TFCLIPVisionModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase__          = [
     'FlaxCLIPModel',
     'FlaxCLIPPreTrainedModel',
     'FlaxCLIPTextModel',
     'FlaxCLIPTextPreTrainedModel',
     'FlaxCLIPVisionModel',
     'FlaxCLIPVisionPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_clip import (
     CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     CLIPConfig,
     CLIPOnnxConfig,
     CLIPTextConfig,
     CLIPVisionConfig,
 )
 from .processing_clip import CLIPProcessor
 from .tokenization_clip import CLIPTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_clip_fast import CLIPTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clip import CLIPFeatureExtractor
  from .image_processing_clip import CLIPImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_clip import (
      CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      CLIPModel,
      CLIPPreTrainedModel,
      CLIPTextModel,
      CLIPTextModelWithProjection,
      CLIPVisionModel,
      CLIPVisionModelWithProjection,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_clip import (
      TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCLIPModel,
      TFCLIPPreTrainedModel,
      TFCLIPTextModel,
      TFCLIPVisionModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_clip import (
      FlaxCLIPModel,
      FlaxCLIPPreTrainedModel,
      FlaxCLIPTextModel,
      FlaxCLIPTextPreTrainedModel,
      FlaxCLIPVisionModel,
      FlaxCLIPVisionPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase__          = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				(						A_	):
     __a						=					"""masked_bert"""







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any=30522    ,      _lowerCamelCase			:			Union[str, Any]=768    ,      _lowerCamelCase			:			Tuple=12    ,      _lowerCamelCase			:			Any=12    ,      _lowerCamelCase			:			str=3072    ,      _lowerCamelCase			:			str="gelu"    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Dict=512    ,      _lowerCamelCase			:			List[Any]=2    ,      _lowerCamelCase			:			int=0.0_2    ,      _lowerCamelCase			:			Union[str, Any]=1e-12    ,      _lowerCamelCase			:			Union[str, Any]=0    ,      _lowerCamelCase			:			List[str]="topK"    ,      _lowerCamelCase			:			Optional[Any]="constant"    ,      _lowerCamelCase			:			Optional[Any]=0.0    ,      **_lowerCamelCase			:			str    ,      ):
       super().__init__(pad_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)

       _snake_case					     =       vocab_size
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       hidden_act
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       type_vocab_size
       _snake_case					     =       initializer_range
       _snake_case					     =       layer_norm_eps
       _snake_case					     =       pruning_method
       _snake_case					     =       mask_init
       _snake_case					     =       mask_scale



