




"""simple docstring"""



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


SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=			{
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}




class       UpperCAmelCase_    (		A_				):
    lowercase__		   = '''tvlt'''




    def __init__(       self		:			Any      ,					snake_case_		:			Union[str, Any]=224      ,					snake_case_		:			Union[str, Any]=2_048      ,					snake_case_		:			List[Any]=128      ,					snake_case_		:			List[str]=[16, 16]      ,					snake_case_		:			Optional[Any]=[16, 16]      ,					snake_case_		:			List[Any]=3      ,					snake_case_		:			Any=1      ,					snake_case_		:			str=8      ,					snake_case_		:			List[Any]=768      ,					snake_case_		:			Optional[int]=12      ,					snake_case_		:			Any=12      ,					snake_case_		:			int=3_072      ,					snake_case_		:			Optional[Any]="gelu"      ,					snake_case_		:			Tuple=0.0      ,					snake_case_		:			List[str]=0.0      ,					snake_case_		:			int=0.02      ,					snake_case_		:			Dict=1e-6      ,					snake_case_		:			Optional[Any]=True      ,					snake_case_		:			Tuple=False      ,					snake_case_		:			List[Any]=16      ,					snake_case_		:			List[Any]=512      ,					snake_case_		:			Optional[Any]=8      ,					snake_case_		:			Optional[Any]=2_048      ,					snake_case_		:			List[str]=0.75      ,					snake_case_		:			List[Any]=0.15      ,					snake_case_		:			int="frame-level"      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			List[Any]=True      ,					snake_case_		:			List[str]="classification"      ,					**snake_case_		:			Tuple      ,					) ->				Dict:




     '''simple docstring'''

     super().__init__(**snake_case_ )

     if audio_mask_type not in ("frame-level", "patch_level"):
      raise ValueError(
          "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
          F"""got {audio_mask_type}""" )

     A__			=							image_size
     A__			=							spectrogram_length
     A__			=							frequency_length
     A__			=							image_patch_size
     A__			=							audio_patch_size
     A__			=							num_image_channels
     A__			=							num_audio_channels
     A__			=							num_frames

     A__			=							hidden_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							intermediate_size
     A__			=							hidden_act
     A__			=							hidden_dropout_prob
     A__			=							attention_probs_dropout_prob
     A__			=							initializer_range
     A__			=							layer_norm_eps
     A__			=							qkv_bias
     A__			=							use_mean_pooling

     A__			=							decoder_num_attention_heads
     A__			=							decoder_hidden_size
     A__			=							decoder_num_hidden_layers
     A__			=							decoder_intermediate_size
     A__			=							pixel_mask_ratio
     A__			=							audio_mask_ratio
     A__			=							audio_mask_type

     A__			=							task_matching
     A__			=							task_mae
     A__			=							loss_type











"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							=			{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPRContextEncoderTokenizerFast",
          "DPRQuestionEncoderTokenizerFast",
          "DPRReaderTokenizerFast",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPRContextEncoder",
          "DPRPretrainedContextEncoder",
          "DPRPreTrainedModel",
          "DPRPretrainedQuestionEncoder",
          "DPRPretrainedReader",
          "DPRQuestionEncoder",
          "DPRReader",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDPRContextEncoder",
          "TFDPRPretrainedContextEncoder",
          "TFDPRPretrainedQuestionEncoder",
          "TFDPRPretrainedReader",
          "TFDPRQuestionEncoder",
          "TFDPRReader",
      ]


if TYPE_CHECKING:
      from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
      from .tokenization_dpr import (
          DPRContextEncoderTokenizer,
          DPRQuestionEncoderTokenizer,
          DPRReaderOutput,
          DPRReaderTokenizer,
      )

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_dpr_fast import (
                DPRContextEncoderTokenizerFast,
                DPRQuestionEncoderTokenizerFast,
                DPRReaderTokenizerFast,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpr import (
                DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPRContextEncoder,
                DPRPretrainedContextEncoder,
                DPRPreTrainedModel,
                DPRPretrainedQuestionEncoder,
                DPRPretrainedReader,
                DPRQuestionEncoder,
                DPRReader,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_dpr import (
                TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDPRContextEncoder,
                TFDPRPretrainedContextEncoder,
                TFDPRPretrainedQuestionEncoder,
                TFDPRPretrainedReader,
                TFDPRQuestionEncoder,
                TFDPRReader,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





