




'''simple docstring'''





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE							:       Dict						=			["LxmertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE							:       Optional[int]						=			[
      "LxmertEncoder",
      "LxmertForPreTraining",
      "LxmertForQuestionAnswering",
      "LxmertModel",
      "LxmertPreTrainedModel",
      "LxmertVisualFeatureEncoder",
      "LxmertXLayer",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE							:       Optional[Any]						=			[
      "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFLxmertForPreTraining",
      "TFLxmertMainLayer",
      "TFLxmertModel",
      "TFLxmertPreTrainedModel",
      "TFLxmertVisualFeatureEncoder",
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  _SCREAMING_SNAKE_CASE							:       Tuple						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




