
'''simple docstring'''







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


lowerCAmelCase_      =   logging.get_logger(__name__)

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







class 		snake_case_       (						_lowerCAmelCase						):



   """simple docstring"""





   __lowerCAmelCase	:							str								="tvlt"







   def __init__( self			,   UpperCamelCase=2_24			,   UpperCamelCase=20_48			,   UpperCamelCase=1_28			,   UpperCamelCase=[16, 16]			,   UpperCamelCase=[16, 16]			,   UpperCamelCase=3			,   UpperCamelCase=1			,   UpperCamelCase=8			,   UpperCamelCase=7_68			,   UpperCamelCase=12			,   UpperCamelCase=12			,   UpperCamelCase=30_72			,   UpperCamelCase="gelu"			,   UpperCamelCase=0.0			,   UpperCamelCase=0.0			,   UpperCamelCase=0.0_2			,   UpperCamelCase=1E-6			,   UpperCamelCase=True			,   UpperCamelCase=False			,   UpperCamelCase=16			,   UpperCamelCase=5_12			,   UpperCamelCase=8			,   UpperCamelCase=20_48			,   UpperCamelCase=0.7_5			,   UpperCamelCase=0.1_5			,   UpperCamelCase="frame-level"			,   UpperCamelCase=True			,   UpperCamelCase=True			,   UpperCamelCase="classification"			,   **UpperCamelCase			,   ):
          super().__init__(**A__)

          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}""")

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

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

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

          lowerCamelCase__				   =      task_matching
          lowerCamelCase__				   =      task_mae
          lowerCamelCase__				   =      loss_type







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)